ERASMUS SCHOOL OF ECONOMICS
Department of Business Economics
Accounting, Auditing and Control
ERASMUS UNIVERSITY ROTTERDAM
The impact of poor
matching on
accounting earnings
Course
Student
Student number
Supervisor:
Date
:
:
:
:
:
FEM 11032-10 Master’s thesis Accounting, Auditing & Control
Fabian Stevense
308407
Dr. C.D. Knoops
August 2, 2011
Preface
It is a great pleasure to hereby present you my Master’s thesis on matching and the changing
properties of earnings. This thesis has been written as part of the PwC Honours Accounting, Auditing
and Control. I would like to express my deep and sincere gratitude to my supervisor, dr. C.D. Knoops
for his detailed and constructive comments, and for his important support throughout this work. I
would also like to thank my family for their encouragement and support throughout my entire study.
Leiden, August 2, 2011
2
Executive Summary
This study focuses on the idea that an increase in poor matching amongst European firms over the
last twenty year had a decreasing impact on the contemporaneous correlation between revenues
and expenses. From the theory of perfect matching a model was created by Dichev and Tang (2008)
to depict the effects of poor matching. The model indicates that poor matching acts as noise in the
economic relation of advancing expenses to earn revenues. As a result of this the mismatched
expenses have an increasing effect on the volatility of earnings and a decreasing effect on the
persistence of earnings. Linked to the decrease of earnings persistence is an increase in negative
autocorrelation in earnings changes. Finally the model also gives rise to the idea that for longerhorizon definitions of earnings the effects of poor matching will be less pronounced. These ideas
suggest a decline in earnings quality which could have serious implications for several actors in the
financial markets.
The empirical tests using samples comprising the top 1000 firms from 15 European countries
document the temporal change in the quality of matching. The results of these tests show that there
is enough supporting evidence to conclude that there is in fact a declining trend in the
contemporaneous correlation between revenues and expenses and that a substantial part of the
expenses is scattered to a future period. A resulting temporal decrease in earnings volatility is also
borne out of the data with the confidence that this decline is not caused by a change in the volatility
of the underlying business fundamentals. The decrease in the persistence of earnings and the
increase in negative autocorrelation require supporting evidence, but the obtained results depict a
clear trend which validates a conclusion that earnings persistence has declined over time. The results
can not verify that the effects of poor matching are eased over longer-time horizons. Combining the
evidence suggests that amongst European firms matching has become worse over the last twenty
years.
3
Table of content
Preface..................................................................................................................................................... 2
Executive Summary ................................................................................................................................. 3
Table of Content ........................................................................................ Error! Bookmark not defined.
1. Introduction ......................................................................................................................................... 6
2. Background .......................................................................................................................................... 8
2.1 Balance sheet approach and Income statement approach .......................................................... 8
2.2 Conceptual Framework ................................................................................................................. 9
2.3 Revising the conceptual framework .............................................................................................. 9
2.4 Arguments against the balance sheet approach ......................................................................... 10
2.5 Combining the two approaches .................................................................................................. 11
2.6 Summary...................................................................................................................................... 13
3. Empirical studies................................................................................................................................ 14
3.1Dichev and Tang 2008 .................................................................................................................. 14
3.2Donelson, Jennings and McInnis 2010 ......................................................................................... 17
3.3Dichev and Tang 2009 .................................................................................................................. 18
3.4. Summary..................................................................................................................................... 20
4. Research design ................................................................................................................................. 21
4.1 Model .......................................................................................................................................... 21
4.1.1 Perfect Matching .................................................................................................................. 21
4.1.2 Poor Matching ...................................................................................................................... 23
4.2 Hypotheses .................................................................................................................................. 24
4.3 Sample ......................................................................................................................................... 26
4.3.1 Firms ..................................................................................................................................... 26
4.3.2 Period ................................................................................................................................... 26
4.3.3 Accounting data.................................................................................................................... 27
4.3.4 Samples 1 and 2.................................................................................................................... 27
4.3.5 Additional samples 3 and 4 .................................................................................................. 27
4.4. Summary..................................................................................................................................... 29
5. Descriptive statistics .......................................................................................................................... 30
5.1. Firm year observations ............................................................................................................... 30
5.2 Testing for parametric data ......................................................................................................... 30
5.2.1Normality of data .................................................................................................................. 30
4
5.2.2 Homogeneity of variance ..................................................................................................... 31
5.2.3 Independence ....................................................................................................................... 31
5.3. Testing for regression ................................................................................................................. 32
5.4. Summary..................................................................................................................................... 33
6. Test results ........................................................................................................................................ 34
6.1. Hypothesis 1: Revenues-expense relation ................................................................................. 34
6.2. Hypotheses 2: Volatility of earnings ........................................................................................... 38
6.3. Hypothesis 3: Persistence of earnings........................................................................................ 46
6.4. Hypothesis 4: Effects of longer time horizon ............................................................................. 51
6.5. Additional tests........................................................................................................................... 52
Changing industry composition ..................................................................................................... 52
Changing country composition...................................................................................................... 53
6.6. Summary..................................................................................................................................... 55
7. Analysis .............................................................................................................................................. 56
7.1. Hypothesis 1: Revenues-expense relation ................................................................................. 56
7.2. Hypothesis 2: Volatility of earnings ............................................................................................ 57
7.3. Hypothesis 3: Persistence of earnings........................................................................................ 58
7.4. Hypothesis 4: Effects of longer time horizon ............................................................................. 58
7.5. Additional tests........................................................................................................................... 59
7.6 Summary...................................................................................................................................... 60
8. Conclusion ......................................................................................................................................... 61
Literature ............................................................................................................................................... 63
Appendix 1.Empirical studies overview................................................................................................. 66
Appendix 2. Proof of calculations.......................................................................................................... 67
Appendix 3. Accounting variables ......................................................................................................... 69
Appendix 4 Selection criteria ................................................................................................................ 71
Appendix 5 Firm years in sample .......................................................................................................... 73
Appendix 6 Descriptive statistics........................................................................................................... 74
Appendix 7 Tests for normality ............................................................................................................. 76
Appendix 8 Multicollinearity ................................................................................................................. 80
Appendix 9 Revenue-expense regression ............................................................................................. 81
Appendix 10 Persistence in earnings..................................................................................................... 83
Appendix 11 Sample composition ......................................................................................................... 87
5
1. Introduction
In 1976 the Financial Accounting Standards Board (FASB) concluded that in the new conceptual
framework it would shift away from the income statement approach and would start supporting the
balance sheet approach as the leading approach to report financial information. This decision had a
tremendous impact for the use of the income statement approach as well as for its underlying
concept of matching. No longer were earnings determined by matching the advanced expenses with
the revenues for that period, but by changes in the net assets of a company. Recent evidence by
Dichev and Tang (2008) on firms in the US has indicated that poor matching, which can be regarded
as a situation in which mismatched expenses act as noise in the economic relation of advancing
expenses to earn revenues, has a pronounced effect on the properties of the resulting earnings.
Through the years the International Accounting Standards Board (IASB) increasingly modeled its
framework to that of the FASB. This might indicate that firms in Europe experience a similar effect
and that the accounting earnings that presented by these companies might suffer from noise that
could impact their usefulness.
It is therefore relevant to conduct research on the concept of matching and its implications for
earnings, because this provides us with better understanding of the consequences of the switch
made by the FASB in 1976 towards the balance sheet approach (Dichev 2008). Besides a better
understanding of the consequences, relevance of research on this topic is found in the determination
of earnings. Poor matching influences the ability to determine earnings. Since users of financial
information have indicated that they regard earnings as the single most important output of
accounting systems (Graham et al. 2005), it is important to look at the developments in matching
expenses and revenues.
The objective of this study is to investigate whether a possible decrease in correlation between
expenses and revenues as a result of an increase in poor matching also appears in Europe. In order to
examine this, the study will make use of the models constructed by Dichev and Tang (2008) and will
construct a sample with information from European companies to the extent that data is available.
Research on this topic might first of all be useful for standard setters. It can provide further insight in
the informativeness of earnings that might support their future considerations when developing new
standards. Second of all this study might be helpful for users of financial statements, since
knowledge about the correlation between expenses and revenues and its implications for earnings
persistence and volatility can possibly be used to improve earnings forecasts.
The study is divided into several chapters, with this introduction being the first. The second chapter
will provide a short introduction on the fundamental discussion of accounting recognition and will
examine its developments during the last century. It starts with discussing the two fundamental
perspectives on accounting recognition, which are the balance sheet approach and the income
statement approach. The chapter advances with the discussion of the conceptual framework and the
joined efforts of the FASB and IASB to revise this conceptual framework. Finally some arguments are
examined on why the FASB and IASB’s current direction, which is moving away from matching and
more towards fair value accounting, is not considered optimal. Chapter three discusses three
empirical studies on matching which were quite recently conducted. Since the available literature is
limited on this subject, these papers will be discussed in depth. In the fourth chapter the hypotheses
6
that will be tested in this research are formulated and the characteristics of the model and samples
defined. The fifth chapter elaborates requirements that need to be met in order to be able to
perform statistical test. In the sixth chapter the result of the main tests and some additional tests are
presented including. The seventh chapter analysis these findings and compares them with previous
research. Finally a conclusion is made in chapter 8.
7
2. Background
2.1 Balance sheet approach and Income statement approach
There has been a long debate about which of the available approaches in the reporting of financial
information should be adopted. There are two fundamental approaches often discussed, which are
the balance sheet approach and the income statement approach. Over time alternative methods
have been developed, which often hold some elements of both approaches. However, to clearly
illustrate the implications of the different approaches only the balance sheet and the income
statement approach will be discussed.
The Balance sheet approach looks at the assets and liabilities of a company and regards the proper
valuation of these assets and liabilities as the most important goal of financial reporting. Accounting
variables other then assets and liabilities are considered to be a result of the differences in valuation
and are therefore regarded as secondary (Dichev 2008). Due to this the amounts that are in the
income statement of a company and especially earnings are dependent on fluctuations in the value
of the assets and liabilities. The result of the balance sheet approach is that the correct
determination of assets and liabilities largely determines the earnings of a company. There is no
need to measure the revenues and expenses (Fox et al. 2003). If earnings need to be calculated for a
certain period, this can be done by just looking at the changes in the net assets over that period. The
most important fundamentals for looking at the income from a balance sheet perspective were
developed by Hicks (1946).
The Income statement approach can be regarded as the opposite of the balance sheet approach. The
most important goal of financial reporting in this approach is the determination of the revenues and
expenses and the earnings of a company (Dichev 2008). Fundamental for the income statement
approach is the measurement and timing of the amount of expenses and revenues. One of the most
important principle to guide in this process is the matching of expenses to revenues.
Matching of expenses to revenues is the process of selecting a particular time period, collecting all
the revenues that are earned during that period and match those revenues with the expenses
incurred in order to produce those revenues. With time set as a boundary, matching is used to bring
together effort, in the form of expenses, and resulting accomplishments, in the form of revenues
(Evans 2003).
The net income of a company can be determined by the difference of matching expenses and
revenues. The amounts that are on the balance sheet can be considered residuals of the matching
and revenue recognition process. In the income statement approach assets and liabilities are
compromised of the cumulative effect of the periodic accruals (Dichev 2008).
A challenging aspect in matching lies in the breaking up of the income stream, which for most
businesses is a continues flow (Evans 2003).This is needed in order to be able to measure the
expenses and revenues in a selected time period. However, once accomplished matching would
provide an objective view according to Paton and Littleton (1940), because it is based on recorded
costs.
8
2.2 Conceptual Framework
The work of Paton and Littleton(1940) is often referred to as the accounting book of the century and
clearly indicates the importance of matching and the primacy of the Income Statement during that
period. Although there were some authors that supported the balance sheet approach and even in
some instances fair-value, it can be said that historically the income statement approach was the
dominant view in financial reporting (Dichev 2008). This changed when in 1973 the Financial
Accounting Standards Board was installed as the new US standard-setter.
The FASB indicated soon after it started its work that there was a need for a conceptual framework,
that would provide direction and structure to financial reporting (FASB 2004a). At that point in time
standards suffered from internal inconsistencies and even in some cases contradictions. The FASB
indicated that this was due to the absence of shared conceptual foundations (Dichev 2008). To be
able to move forward would first require the FASB to define the underlying accounting concepts and
principles. In order to avoid any internal inconsistency and to be able to ensure conceptual clarity the
FASB recognized that certain concepts needed to have primacy. These concepts are the concepts
that will be used to define the other concepts (FASB 2004b). The FASB therefore had to choose
whether the balance sheet approach or the income statement approach would receive this
conceptual primacy.
After extensive discussions the FASB concluded in 1976 that the balance sheet approach was
conceptually superior and therefore the approach to use. The Board noted that fundamental
concepts such as revenues, expenses, income, appropriate matching and distortion of periodic net
income need to be clearly defined, because otherwise the earnings in the income statement
approach will have a very subjective character (FASB Discussion Memorandum 1976). Since the
Board and those participated in the discussion were unable to define expenses, revenues and income
without first defining assets and liabilities, conceptual primacy for the balance sheet approach was
considered to be the logical result (Bullen and Crook 2005). This shift from the revenue-expense view
to the asset-liability view was also adopted by the International Accounting Standards Committee
(IASC) in their Framework for the Preparation and Presentation of Financial Statements of 1989 (IASC
1989).
In the period that followed, the balance sheet approach increased significantly in importance. First of
all older rules and standards were adjusted and aligned with the new conceptual framework. Second
of all elements of the balance sheet approach, such as fair-value accounting, were steadily adopted
by the FASB (Dichev 2008). The balance sheet approach also expanded rapidly on a geographical
level. The FASB has often been modeled by foreign standard setters and the conceptual framework
regularly formed the basis for their own frameworks. Especially the IASC, which was later replaced by
the International Accounting Standards Board (IASB), based its own conceptual framework heavily on
the conceptual framework issued by the FASB (Dichev 2008).
2.3 Revising the conceptual framework
Although the conceptual framework has placed its mark on financial reporting for the last thirty
years, the FASB has acknowledged that “certain aspects of the framework are incomplete, internally
inconsistent, and lack clarity” (FASB 2003) In order to effectively combat these problems, the FASB
and the IASB have expressed in 2004 their commitment to jointly revise their conceptual frameworks
9
and converge the US and international accounting standards (FASB 2004a). The goal is to refine,
update and complete the frameworks currently in place so that they can be converged into a
common framework that both Boards can use to develop new and revised accounting standards
(Bullen and Crook 2005). Because there are a lot of similarities between both frameworks, the Boards
will focus on those conceptual issues which most likely result in standard-setting benefits in the near
future (FASB 2004a). The joint project between the two bodies have identified those issues that
reappear frequently and expressed their intent to resolve them in the coming years (Bullen and
Crook 2005).
The conceptual framework project consists of six parts which will be developed over several phases.
In each phase a preliminary views document will be issued. This document represents the
preliminary conclusions of the Boards and is open for discussion with constituents. The preliminary
views document is followed by an exposure draft (Gore and Zimmerman 2007). On July 2006 the
FASB and the IASB published their first preliminary views document, which examines the objective of
financial reporting and the qualitative characteristics of decision-useful financial reporting
information (FASB 2006).
This preliminary views states that decision-usefulness to the primary users of financial information is
a guiding objective in the framework of both Boards, This objective is also found in the frameworks
of all other leading standard-setters (Kvifte 2008).Naturally, the choice whether to use the balance
sheet approach or the income statement approach should this objective into consideration. If the
decision-usefulness of financial reporting is increased by adopting the balance sheet approach, then
it is valid to provide the asset-liability definitions with conceptual primacy. However, if this is not the
case and these definition do not lead to more decision-useful information, they should be rejected
(Kvifte 2008). Although the balance sheets approach is strongly advocated in the preliminary views,
the FASB lacks somewhat on the side of providing documentation that supports the decisionusefulness of the asset-liability definitions, which might be interpreted as a weakness to their
conclusion of conceptual primacy (Bullen and Crook 2005).
2.4 Arguments against the balance sheet approach
The proponents of the income statement approach are reluctant towards the Boards’ strong
endorsement for the balance sheet model in the conceptual framework and fear that it might lead to
a decline in the availability of decision-useful information for several reasons (Dichev 2008).
First of all the balance sheet approach would not be in line with the way most businesses operate
and create value (Dichev 2008). Business can be seen as entities that continuously advance expenses
in the hope to earn revenues and earnings from this effort. From this perspective, the assets that a
business holds just have a supplementary function and can be seen as supporting elements that the
company uses to maintain the continuous stream of operations. If viewed independently, most
assets have relatively little value. The assets however are bought or produced to be put into the
operations of a business to serve the main goal of the company, which is creating revenues and
earnings (Dichev 2008). The balance sheet approach therefore gives a wrong impression, because it
looks as if there is a permanent store of assets, but these assets are only there because they are part
of the continuous process in which they get sacrificed and renewed (Dichev 2008). According to the
income statement proponents, financial reporting should reflect this process where the main
10
objective is advancing expenses to earn revenues and assets just have a supporting role in
accomplishing this objective.
Second of all it is not really clear why the balance sheet approach would have conceptual superiority
over the income statement approach (Dichev 2008). As was described above the FASB has expressed
its believes that the conceptual primacy for the balance sheet approach was considered the logical
result, since the Boards were unable to define the concepts of expenses, revenues and income
without first defining assets and liabilities (Bullen and Crook 2005). Assets therefore are considered
the most fundamental concept for accounting, with liabilities being the converse of assets. However
in the definition of assets the FASB states that “asset are probable future economic benefits obtained
or controlled by a particular entity as a result of past transactions or events” (Statement of Financial
Accounting Concepts No. 6). However it is likely that with benefits the FASB means net benefits,
which in essence are earnings. The FASB therefore creates a circularity in its own definition of assets
by using a form of expected earnings to define assets (Dichev 2008).
Third of all there are several substantial problems with using the balance sheet approach in practice
(Dichev 2008).The character of the income statement naturally results in the use of historical costs
for valuation. For the balance sheet approach and more so for mark-to-market accounting and fairvalue accounting the real economy and the financial markets depend on each other for information
that is needed for valuation. The balance sheet approach is therefore implicitly coupled to the
correct valuation of assets and liabilities. Due to the fact that the real economy and financial markets
depend on each other for information, fair-value accounting is forced to put a lot of faith in the
correctness of market prices (Dichev 2008). This can lead to undesirable situations such as market
bubbles if the prices deviate too much from their fundamental values (Hirshleifer 2001). Accounting
should have independent checks on valuation and provide information on the real economic
activities.
Finally the balance sheet approach is likely to have a negative influence on the forward-looking
usefulness of earnings over a longer period in time, which is referred to as the temporal decline in
the informativeness of earnings (Dichev 2008). From Graham et al. (2005) it can be concluded that
investors view earnings as the most important indicator to evaluate existing and future investments.
Current earnings are considered to be the best predictor of the future earnings that a company is
likely to generate (Graham et al. 2005). The fact that the balance sheet approach views earnings as a
change in net assets could have a negative influence on the predictability of earnings over time, if
fair-value accounting is used and the value changes are recorded in the profit and loss statement,
because it leads to higher volatility and lower persistence in earnings.
2.5 Combining the two approaches
Dichev and other proponents of the income statement approach received critique from the
normative side on their interpretation of the conceptual framework and the intents of the FASB.
Normative theorists search for accounting practices that should be used. These normative critics
argue that proponents of the income statement approach are too focused on finding the single best
theory. The result of this is the rise of a winner-takes-all competition, in which the theories become
opponents and everybody is looking for the approach that is the most proper (Miller and Bahnson
2010). Some authors however suggest that a multiple path in which a combination of the two
11
approaches is proposed would allow financial accounting to better serve the diverse needs of its
users. They question whether financial accounting must be conducted in a single specific way, or that
it might be possible to produce different sets of information to serve more users (Miller and Bahnson
2010).
The proposition to use different measurement approaches in order to increase the usefulness for
financial statement users is not new and has also been the subject of debate in The Netherlands. In
the early 1980 Bindenga already argues that it is impossible to realize the objective of the balance
sheet as well as the objective of the income statement at the same time and therefore proposes to
separate the two (Bindenga 1986). According to Bindenga it is impossible to simultaneously provide a
fair view of the results of a company as well as a fair view on the value of that company.
The solution proposed by Bindenga is known as a system of “tweeledig monisme” or dual monism.
Since the key objective of an organization is continuity, the profit and loss account is the most
important in the financial statements, because this account provides evidence whether the
organization is financially healthy and viable (Bindenga 1986). Primacy in this account is attached to
the matching principle whereas the prudence concept, which requires the expenses and liabilities to
be recorded as soon as possible, but the revenues only when they are realized or assured, is of no
real importance. A connection between the valuation of assets and liabilities in balance sheet and
the income statement is not necessary. This enables the possibility of creating two different sets of
balance sheets. The first is a balance sheet where the assets and liabilities are residual of the
matching and revenue recognition process and therefore have a direct relation with the profit and
loss account. The second is a balance sheet which gives a fair view on the value of a company
through the process of valuation the assets and liabilities that have no direct relation with the profit
and loss account. The prudence concept would play a vital role in this balance sheet (Bindenga 1986).
Although Bindenga does not suggest the concept of fair value for the second balance sheet , which
would mean that the balance sheet might not display the true value of the company, he believes that
fair-value accounting increases subjectivity and more importantly volatility in the financial reports
(Hoogendoorn 2003). This is also an argument stated by the proponents of the income statement
approach.
In 2004 the FASB and the IASB jointly started a performance reporting project to investigate a new
format for reporting performance (IASB 2004). The performance report project has proposed to
replace the present income statement with the statement of comprehensive income. Comprehensive
income is defined as the change in the net assets from all sources except for transactions with
owners during a particular period (Robinson 1991). With comprehensive income all income and
expense items are reconciled, regardless of whether the items were booked directly on the equity
account or passed through the net income statement (Van Cauwenberge and De Beelde 2007). One
of the main goals of the reporting performance project is to create a comprehensive income
statement that will integrate all sources of income and will categorize and display income
components in a way that is useful to investors (IASB 2005).
One of the ways to categorize income that has been suggested is based on the division between
historical cost income and fair value income, which uses remeasurements (Joint International Group
Working Group on Performance Reporting 2005). In this form of categorization, the comprehensive
income statement would contain a subtotal based on historical cost income in which fair value
12
remeasurements are excluded, while the total comprehensive income would include fair value
remeasurements (Van Cauwenberge and De Beelde 2007). It is clear that this approach would
explicitly use both measurement concepts, which shows that the IASB does support the notion that a
combination of concepts can exist side by side and possibly enhance decision usefulness.
2.6 Summary
There has been a long debate about which of the available approaches in the reporting of financial
information should be adopted. Until 1976 there was a primacy for the income statement approach,
but this changed when the FASB issued their conceptual framework, which switched the conceptual
primacy to the balance sheet approach. This view was also adopted by the IASC in their Framework
for the Preparation and Presentation of Financial Statements of 1989. In 2004 both boards have
expressed their commitment to jointly revise their conceptual frameworks and effectively combat
those issues that reappear frequently in a variety of standard-setting projects. The revised
conceptual framework also endorses the balance sheet approach which is met with opposition
claiming that it does not provide more decision-useful information. As a result of this discussion
some authors have suggested that a multiple path in which a combination of the two approaches is
proposed would allow financial accounting to better serve the diverse needs of its users. In 2004 the
FASB and the IASB jointly started a performance reporting project, which after investigation
proposed to replace the present income statement with the statement of comprehensive income,
which includes both historical cost income and fair value income.
13
3. Empirical studies
With the introduction of the Conceptual Framework and the Framework for the Preparation and
Presentation of Financial Statements both the FASB and the IASC made a deliberate choice to provide
the balance sheet approach with conceptual primacy and to put less emphasis on matching as the
fundamental concept in the determination of earnings. It can be expected that through the years this
decision has had consequences for the properties of earnings. Since earnings is considered to be the
single most important output of the accounting system (Graham, Harvey, and Rajgopal 2005), it is
important to examine and document the effects of the Boards’ decision because changes in the
properties of earnings might have implications for the informativeness of earnings. Opponents of the
balance sheet approach fear that the turn away from the matching concept as the fundamental
concept in the determination of earnings leads to a decline in the informativeness and usefulness of
earnings. They argue that a decrease in the quality of matching results in a decrease in the
informativeness of earnings. Examining the quality of matching might provide useful insight into the
properties of earnings and can be helpful in topics such as accounting-based valuation and using
earnings as a predictor in earning forecasting and equity valuation.
The temporal changes in the informativeness of earnings from a matching perspective has only
recently been the subject of a series of studies, initially sparked by the study of Dichev and Tang in
2008. An overview of these studies can be found in appendix 1.
3.1Dichev and Tang 2008
Compared to prior studies, the study of Dichev and Tang (2008) proposes an alternative explanation
for the possible decline in the informativeness of earnings and is the first study that suggests that
there is a connection with the quality of matching. They start by stating that they look at earnings
from an income statement perspective, and therefore measure earnings as the excess of revenues
over the expenses that were necessarily incurred to earn those revenues. According to the
perspective of Dichev and Tang (2008) the purpose of accounting is to properly match the expenses
against the resulting revenues. If expenses are not properly matched against the resulting revenues,
it is defined as poor matching and is modeled in the study of Dichev and Tang as noise in the
economic relation of advancing expenses to obtain revenues. It is noted that poor matching can arise
from different sources and also in different degrees, but since the consequences for the theoretical
model are the same Dichev and Tang treat them in a similar way.
The development of the model that Dichev and Tang use, starts with creating a case of perfect
matching. In the test phase this case will function as a benchmark for the case of poor matching. The
matching is considered to be perfect in the case where all costs can be traced directly and specifically
to specific revenues. Besides that the authors treat the schedule of revenues as given and therefore
concentrate on expense recognition and the properties of matching. The equation that is used for
representing a case of perfect matching, which is examined in detail in chapter 4.1.1, is constructed
in a time-series specification, because matching expenses against revenues is essentially a timeseries phenomenon, in which the mismatches of expenses are resolved in the long run. The equation
constructed by the authors for perfect matching provides a series of implications. The first
implication is that in a competitive equilibrium the earnings tend to gravitate towards the cost of
equity capital. Secondly, deviations in earnings from the long-run mean will gradually diminish over
time. Thirdly, there is an economic shock in every period, which is the noise in the matching relation
and has a mean of zero. The variance of this economic shock represents the economic volatility of
14
the business environment. Fourthly, in a perfect matching situation, the volatility is driven entirely by
economic factors.
After the perfect matching case the authors turn to the case of poor matching and start by modeling
the equation for expenses. In this equation a random variable is introduced that represents
mismatched expense. This variable for mismatched expense is unrelated to the well-matched
expense and revenue. Because of this the mismatched expense acts as noise. The second
characteristic of this variable is that it has a strong negative first-order autocorrelation, which reflects
the fact that the mismatches of expenses are eventually resolved in the long run. From the equation
that Dichev and Tang construct for the expenses under poor matching it becomes clear that matching
becomes worse if the noise in the current period is higher. They define the quality of matching as the
inverse of the noise variable.
If the effects of poor matching are closely examined the following can be observed.
First of all poor matching decreases in the synchronal correlation between revenues and expenses.
With poor matching some of the perfectly matched expenses get scattered across different periods,
resulting in a lower synchronal correlation than the underlying economic correlation of advancing
expenses to produce revenues. Second of all poor matching increases the volatility of earnings. The
volatility in earnings that are poorly matched is higher, because the mismatched expenses act as
noise that is not related to the economics process of creating earnings. Third of all persistence of
earnings decreases with poor matches. Persistence of earnings is represented by the slope
coefficient from a regression of current earnings on lagged earnings and from the equation
constructed by Dichev and Tang it can be concluded that poor matching decreases this slope
coefficient. A low persistence in earnings implies a high negative autocorrelation in earnings changes.
Finally, the effects of poor matching are resolved over longer-time horizons. This is the result of the
fact that accounting is self-correcting and therefore errors due to mismatching will naturally get
resolved in the long run.
The model constructed by Dichev and Tang is used to test the hypothesis that matching has become
worse over time. Reasons to believe this hypothesis is true come from changes in the real economy,
such as rising research and development activities, and changes in standard setting, especially the
transition from the income statement approach to the balance sheet approach. The sample that is
used to test the hypothesis consists of the top 1000 U.S. firms in terms of assets for every year that
the study covers, which is from 1967 to 2003. The complete data for assets, revenues, earnings
before extraordinary items and preceding 9 years of revenues and earnings need to be available for
every firm-year. The need for data preceding 9 years of the study period comes from the fact that for
every variable both a one-year as well as a two-year definition will be used and it takes up to 10 years
to calculate the volatility in two-years earnings. From the available one-year and two-year sample the
top and bottom one percent of all variables are eliminated in order to avoid any influences by
extreme observations. The final two-year sample consists of 17.788 firm-year observation and the
final one year sample consists of 34.785 firm-year observations.
Dichev and Tang start their analysis by examining the trends in the relation between revenues and
expenses. Since the authors expect that matching has become worse in the last 40 years, the model
predicts a declining pattern in the correlation between revenues and expenses over the period of the
study. Since poor matching scatters expenses to different period then their associated revenues it
15
can be expected that the correlation between revenues and non-contemporaneous expenses has
increased. The results confirm both relations with a highly statistical significance level.
The authors then examine the effect of poor matching on earnings volatility. From the model in
combination with the assumption that matching has become worse, an increase in volatility is
expected. The results from the one-year sample provide clear evidence that earnings volatility has
substantially increased over the study period. The two-year sample shows a comparable result
however, the increase in volatility is slightly smaller than in the one-year sample.
Finally, the persistence of earnings and autocorrelation in earnings changes are tested, for which the
authors expect to see that earnings persistence has declined over the last 40 years and that the
autocorrelation in earnings changes has become more negative. The evidence provided by Dichev
and Tang confirms the expectations for both the one-year and two-year horizon.
The authors present a series of additional tests to enhance the robustness of their findings.
First the effect of one-time items and losses are examined, since they might provide an alternative
explanation for the obtained results. Several one-time items have increased in both frequency and
magnitude in the last 30 years which might have an influence on some of the relations that were
examined. However controlling for this by excluding one-time items leads to qualitatively similar
results.
Next the authors investigate the effect of changing industry composition on the obtained results.
Firms that are active in certain industries tend to have less persistent earnings and more volatility. If
these industries are becoming more prominent over time, this might account for some of the results
obtained. Dichev and Tang control for this effect by re-testing the main tests with 2 subsamples, one
of which contains industries that have increasing firm count over time and the other contains
industries that have decreasing firm count over time. The firm count is done by examining the
presence of industries in the sample of 1967 and comparing these numbers with the presence in the
sample of 2003. The results from the two subsamples demonstrate patterns as the main tests.
Finally evidence is provided on the relative role of accounting and real economy factors on the
relations that were observed. For the real economy, this is done by investigating the temporal
properties of cash-based measures of revenues. The idea behind this is that if the real economy is
indeed the primary determinant of the results, then similar patterns would likely occur in cash-based
measures, since they are unaffected by the accrual process. The evidence that Dichev and Tang
however find is that changes in the real economy play only a secondary role in explaining the
changing properties of earnings.
For explaining the possible role of accounting factors the authors look at accrual quality. Their
motivation is that if indeed accounting factors play a role, it is more likely that the observed relations
are found in firms where the quality of accruals is low, because these firms are likely to be more
affected by the general trend of deteriorating matching quality than firms have naturally good
accrual quality. The results obtained from the test on accrual quality suggest that accounting factors
are a substantial determinant of the observed temporal patterns. The authors even express their
believes that this might even be the primary determinant.
16
3.2Donelson, Jennings and McInnis 2010
The study of Donelson, Jennings and McInnis (2010), advances on the work of Dichev and Tang
(2008) by first looking at the revenue-expense relation that they described and trying to identify the
line items that are influencing this relation. Next they try to examine if the line items that they
distinguished are more likely to have been affected by changes in certain economic activity or by
changes in accounting standards.
The authors start with composing their sample in almost exactly the same way as Dichev and Tang in
order to be able to replicate their results and advance on them. They identify the largest 1000 firms
on the basis of total assets for the years 1967 to 2005. Again data needs to be available that enables
the calculation of one- and two-year earnings and the volatility of two years earnings over the
preceding 10 years. In order to be able to distinguish between line items, Donelson et al. (2010) also
require the availability of data on cost of goods sold, selling general and administrative expenses,
income tax expenses, and operating income after depreciation. Eliminating the extremes results in a
sample of 32.645 firm year-observations.
Donelson et al. (2010) start their analysis with dividing the total expenses that are used in the study
of Dichev into six components, which are the costs of goods sold, selling general and administrative
expenses, depreciation expenses, tax expenses, other expenses and special items. Special items
consist mostly of gains and losses from asset sales, restructuring charges and asset impairments.
With the decomposition framework that was constructed by Kee (2009), the importance of a
particular component on the relation between revenues and current expenses can be detected. The
authors find that the decline in the relation is caused by the special items component. They perform
a series of control test to provide greater assurance that special items is indeed responsible for this
effect. These control tests show that the patterns that the authors discovered are greatly reduced if
they exclude firm-years with large special items, exclude special items completely or add back special
items to net income. This result can be explained by both the fact that importance of this line item
has increased over time, thereby increasing its weight in the equation and because current period
special items’ association with current period revenue is lower than other expenses.
In trying to find an explanation for the increase in total special items, Donelson et al. (2010) look at
the role of changes in economic events and changes in specific accounting standards.
In order to test the influence of accounting standards , the authors examine specific accounting
standards that were implemented during the last 40 years that are likely to have influenced the
frequency of special items. Five components of income are identified that account for the majority of
special events and transactions that need to be disclosed and can be regarded as special items, which
are asset write-downs, sales of assets, restructuring, impairment of intangible assets and debt
extinguishments. Donelson et al. examine all the standards that apply to these components and
implement their effects into an equation. The frequency of special items is tested in the years before
and after implementation of these standards in order to discover if a particular standard triggered
any effects. It becomes clear from the results of the test that the authors were not able to find any
accounting indicator variable that showed a strong positive effect, which leads to the conclusion that
none of the accounting standards that were examined significantly impacted special items.
The influence of economic events is tested by constructing an index of specific economic events that
are often associated with organizations that report special items, but which can not arise from
bookkeeping practices alone. The events identified are a negative employee growth, merger and
acquisition activities, discontinuing operations, declining sales and operating loss. For every event the
17
authors assign a point if the indicator variable is present, which results in an E-score, which is the
sum, between zero and five. The larger the E-Score, the higher the level of economic activities that
are related to the special items. The index that was constructed provides strong evidence that the
economic events are increasing over time and that they are associated with the reporting of special
items.
Literature by other researchers has shown that the level of competition in the US economy has
increased during the period for which the study of Donelson et al. (2010) was conducted. Since there
is evidence that the economic events have a significant influence on special items, the authors try to
distinguish whether the increase that they found in their index of economic events is possibly related
to the increasing level of competition. To calculate this they use the Herfindahl Index, which is the
standard measure for competition (Gaspar and Massa 2006) and combine this with the Z-score which
is developed by Altman (1968) and is a measure for bankruptcy risk or financial distress. These two
measures are computed separately and also combined. In order to combine them a P-score needs to
be calculated, which is done the same way as the E-score for economics events by assigning points,
ranging from zero to two. The P-score is furthermore divided into three groups, low, middle and high,
depending on the number of points received, with low receiving a score of 0 and high receiving a
score of 2. A positive correlation between the P-Score and the E-score would indicate that the
economic events are associated with increasing competitive pressure. From analyzing the
computations, it becomes clear that for the middle and high group there is a significant correlation,
indicating that the economics events associated with special items have increased among
organizations that have a medium and high level of competitive pressure.
3.3Dichev and Tang 2009
Dichev and Tang (2009) examine the relation between earnings volatility and earnings predictability.
From Dichev and Tang (2008), it became apparent that poor matching increases the volatility of
earnings. In this study they investigate what implications this volatility has on short- and long term
earnings predictability. The authors again consider that economic events and problems in the
accounting determination of income are factors that cause volatility, however their focus is not
plausible causes, but on investigating the existence of a relation.
A framework is constructed first that will be used as a basis for the empirical analysis. For explaining
the relation between earnings volatility and earnings predictability the authors start off with the
autoregressive regression equation of current earnings on one-year lagged earnings, from which they
take the variance of both sides. The resulting equation gives rise to two important implications.
Firstly, if earnings persistence is held constant, earnings volatility is inversely related to earnings
predictability. Secondly, the persistence coefficient is likely to have a strengthening effect of this
negative relation. The underlying reason for this is that noise in earnings caused by economic events
or accounting standards is likely to increase the volatility of earnings as well as decrease the
persistence of earnings.
As a next step the authors take the total derivative of the variance of the error term with respect to
earnings volatility, in order to be able to formally examine the relation between earnings volatility
and earnings predictability. This expression again gives two important insights. Firstly, the strength of
the direct relation between earnings volatility and earnings predictability seems to be determined by
earnings persistence, where a higher level of earnings persistence indicates more predictable
earnings. Secondly, there is a link between earnings volatility and earnings predictability through the
effect of earnings volatility on earnings persistence. This results from the fact that the negative link
between earnings volatility and earnings predictability is strengthened by the negative effect that
earnings volatility has on earnings persistence.
18
For the sample Dichev and Tang selected firms between the period of 1988 and 2004 that could
provide data for assets, earnings, cash flow from operations and preceding 4 years of earnings and
cash flow from operations. The top and bottom one percent of earnings, accruals (difference
between earnings and cash flow from operations) and cash flow from operations. Companies need to
have a minimum of $100 million in assets and fiscal year needs to end at the 31st of December, which
results in a sample of 22.113 firm-years. This sample is divided into two groups where the first group
is used for the analysis of the predictive power of earnings volatility for earnings predictability and
the second group is used to perform out-of-sample tests of forecasting accuracy.
The authors start with the result for the earnings persistence regression for 1 year predictive
horizons. The results present the persistence coefficients and coefficient of determination (R2) of
regressions of one-year ahead earnings on current earnings and are divided into different panels. The
first shows the regression results for the full sample, the other panels show the regression results for
quintiles of earnings volatility, quintiles of absolute amount of accruals, quintiles of earnings level
and quintiles of cash flow volatility. Quintiles are divided into 5 groups, where quintile 1 yields the
highest earnings persistence and quintile 5 yields the lowest earnings persistence. The results show
that earnings volatility dominates all other indicators with respect to earnings predictability.
The test is repeated for a 5 year predictive horizon. From the results It can be concluded that there is
a substantial difference in the predictive power of the samples used. Firms with high-volatility show a
rapid deterioration of the persistence coefficient and the coefficient of determination. This was to be
expected from the model. The firms with low levels of volatility however, show a very robust
predictive power over the entire 5-year horizon. Dichev and Tang conclude as a result of these tests,
that earnings volatility has differentiating powers when it comes to the long-run prediction of
earnings.
In a series of control tests and robustness checks the authors strengthen their findings. First the
effect of transitory items on the results is tested, since these items both increase the volatility of
earnings and decrease earnings predictability. This is done by repeating the main test but excluding
those firm year-observations where the sum of special items and non-operating income and
expenses excluding interest income exceeds 5% of total assets. The effect is shown to be minimal for
low-volatility quintiles and more pronounced for high-volatility quintiles. The magnitude of the
overall results however remains mostly the same. To control for the temporal rise in the importance
of special items, the test was also repeated with year additional dummies to control, but did not
trigger a different result. A possible survivorship bias was also rejected by retesting a sample of 4032
observations that have a minimum of 5 years of earnings into the future. Both short- and long-term
results remained constant. Controlling evidence is also provided on the effect of cross-sectional
dependence in earnings on the test of significance , which shows that the documented relation are
indeed significant. Finally an out-of-sample forecasting test was performed in order to provide
additional evidence on the utility of the earnings specification versus other models considered in the
study. The results of this test show first of all that as with the test sample, the earnings volatility
model produces lower forecasting errors than other examined variables, second of all the superiority
of earnings volatility model is concentrated in firms with low to medium volatility of earnings.
Having provided empirical proof on the relation between earnings volatility and earnings
predictability Dichev and Tang investigate whether financial statement users are in fact aware of the
19
existence and the magnitude of this relation. In their tests they use analysts as a proxy for financial
statement users, because they are considered to be sophisticated in utilizing the information
provided.
The first test examines the level of current earnings, where two portfolios are matched on all
percentiles of the empirical distribution at time t, so that any deviation in future profitability can be
fully ascribed to differences in their earnings volatility information. It becomes clear from the results
of the first test that the forecasts of analysts only partially incorporate the earnings volatility
information that is available to them. The evidence indicates that analysts incorporate less than half
of the full implications of earnings volatility for earnings predictability. Additional test show that this
is almost the same for 2-year-ahead earnings forecasts.
The second test performed by Dichev and Tang investigates the magnitude of the current forecast
error. The authors indicate that if analysts fail to recognize that earnings are less persistent for highvolatility firms, high volatility firms with positive earnings surprises in a particular time period are
expected to have negative earnings surprise the next period and vice versa. A regression equation is
constructed on the forecast error at t+1. The evidence that Dichev and Tang find again leads to the
conclusion analysts do not understand the implications of existing earnings volatility for future
earnings. Because of this it is possible to identify reliable and important patterns in analyst forecast
errors.
3.4. Summary
With the issuing of the Conceptual Framework the FASB made a deliberate choice to provide the
balance sheet approach with conceptual primacy and to put less emphasis on matching as the
fundamental concept in the determination of earnings. Opponents of the balance sheet approach
fear that the turn away from the matching concept leads to a decline in the informativeness and
usefulness of earnings. This possible change in the informativeness of earnings from a matching
perspective has only recently been examined in a series of studies. Dichev and Tang (2008) were the
first to investigate the effects of poor matching on accounting earnings. They found a declining
contemporaneous correlation between revenues and expenses. They also found an increase in the
volatility of earnings and a decline in the persistence of earnings. The authors present evidence that
accounting standards are responsible for the declining revenue-expense relation. Donelson, Jennings
and McInnis (2010) advanced on the study of Dichev and Tang by identifying factors responsible for
the changes in the revenue-expense relation. Their findings indicated that the changes are primarily
caused by special items. Also economic events associated with special items turn out to be more
important than individual accounting standards. Finally Dichev and Tang (2009) investigate the
effects of earnings volatility on earnings predictability. They find that a better awareness of the
earnings volatility trend can lead to significant improvements in the prediction of both short- and
long-term earnings.
20
4. Research design
The object of this research is to investigate whether the effects of poor matching on the properties of
earnings that were recently discovered for US firms can also be found with European firms. It
becomes clear from the research discussed in the literate review that poor matching acts as noise in
the relation of advancing expenses to earn revenues and that it has significant implications on the
volatility and earnings persistence. In order to be able to conclude whether these effects have also
occurred in Europe first a model of both perfect and poor matching will be constructed. The
conclusions that can be drawn from these theoretical models will form the basis for the hypotheses
that will be used in this research.
4.1 Model
The model that is used to examine the effects of poor matching on the properties of earnings is a
combination of the models developed by Dichev and Tang (2008) and Donelson, Jennings and
McInnis (2010). The model of Dichev and Tang forms the basis for testing the hypotheses that will be
constructed later on to see whether there is a decrease in the relation of advancing expenses to earn
revenues.
The model uses an income statement approach as its base, which implicates that a firm continually
advances expenses in order to be able to generate revenues and earnings. As already stated the
earnings of a firm are then defined as the excess of revenues over the expenses advanced to earn
them and poor matching is the extent to which expenses do not get matched against the resulting
revenues.
Poor matching can result from a number of sources. A company can be faced with unavoidable
business factors, such as a large amount of fixed costs or costs that are poorly traceable. Also
managerial discretion, such as the decision to take a big bath, and the influence of accounting rules
can have an impact. In practice there is also a distinction made between the level of matching
success. There is direct matching when costs can be allocated directly to associated revenues, such as
costs of goods sold. A situation of indirect matching arises when costs are matched indirectly by
allocating them to a specific period, such as depreciation costs and finally no matching occurs when
the considerations needed for matching are completely absent. Advertising costs and costs for
research and development are part of this last group, because it is not possible to relate these costs
to revenues in the same period. In order to keep the model simple, there will be no provisions made
for these different sources and levels of matching. All aspects of poor matching are regarded to have
similar consequences on earnings and are therefore treated the same (Dichev and Tang 2008).
4.1.1 Perfect Matching
The model of poor matching that will be constructed is used to test the effect of poor matching on
the properties of earnings. However, before constructing this model it is useful to first build and
examine a model of perfect matching. The model of perfect matching can be used as a benchmark, to
contrast the effects of poor matching (Dichev and Tang 2008).
Perfect matching is a situation in which all the relevant revenues are matched against the expenses
that were incurred in order to produce those revenues. In the situation of perfect matching all
expenses of a company can be traced directly to specific revenues. In the model of perfect matching
21
the revenue recognition is considered to be fixed (Dichev and Tang 2008). As a result of this property,
the model can focus on the properties of matching and expense recognition. The implication of fixed
revenue recognition is that the schedule of revenues is treated as given. Important to note is that in
the equations that are constructed for the perfect matching model, the accounting variables that are
derived under perfect matching are presented with an asterisk (*).
The following two expressions derived from Dichev and Tang (2008) indicate the earnings relations
for organizations that use perfected matching:
(1) Et* = Revt* - Expt*
and
(2) Et* = Ecc + β1*(Et-1* - Ecc) + εt
In these equations, the variable Et indicates the earnings at time t, the variable Rev indicates the
revenues, the variable Exp indicates the expenses and finally the Ecc indicates the earnings which is
expressed by the cost of equity capital of the firm. The Ecc is treated as a constant and is basically the
rate of return that the organization requires and the long-run mean of its earnings. The Ecc can be
kept constant, because a varying Ecc would not influence any of the main conclusions that can be
drawn from the model (Dichev and Tang 2008). β1* is between 0 and 1.
The two equations both have their own implications. The first equation can be used to illustrate the
economic performance or economic earnings of a company, which is denoted by Et*, because it
clearly depicts the result that is obtained from spending Expt* in order to get Revt*. The second
equation can be used to indicate that the process of creating economic earnings has time-series
elements. This is due to the fact that matching is a time-series process. Every situation in which a
mismatch between expenses and revenues occurs will get resolved over time (Freeman, Ohlson and
Penman 1982). The β1* coefficient is therefore positive, but must be less than 1 (Dichev and Tang
2008). The second equation also depicts that there is an economic shock, which is denoted by εt.
With perfect matching this error term has a mean of 0. The variance of ε, which is denoted as var(ε)
can be used as a representation of the economic volatility in the business environment in which the
firm operates (Dichev and Tang 2008). Finally the second equation shows that in the long-run
earnings tend to gravitate towards the cost of equity capital.
The second equation can be rewritten in a form that easier clarifies the economic properties of
earnings1:
(3) Et* = β0* + β1*Et-1* + εt
Examining the third expression teaches us that this is a similar expression as a regression of present
earnings on past earnings. This expression can be used to determine the link between the quality of
matching and earnings persistence (Dichev and Tang 2008). The third expression can also be used to
establish the link between economic volatility and volatility of earnings. This is done by taking the
variance of Et*:
(4) Var(Et*) = β1*2Var(Et-1*) + Var(ε)
1
Formal proof can be found in Appendix 2.
22
The fourth expression can be rewritten in form that provides more intuitive information. This can be
achieved by observing that the variance of earnings is a function of lagged-once earnings. The laggedonce earnings is in turn a function of lagged-two earnings, etc.(Dichev and Tang 2008). Realizing this,
the fourth expression can be rewritten by using recursive substitution:
(5) Var(Et*) = Var(ε)(1 + β1*2 + β1*4 + … )
From this fifth expression it can be concluded that in a perfect matching situation, earnings volatility
is determined entirely by the economic factors.
4.1.2 Poor Matching
Using the model of perfect matching, an alteration can be made to depict a situation of poor
matching. In the poor matching model the expense recognition deviates from the perfect situation,
which has an effect on the expenses (Dichev and Tang 2008). In a situation of poor matching the
expenses can be formulated as followed:
(6) Expt = Expt* + νt
In expression (6) νt is an equation of itself, which is νt = τt – τt-1
In the equation of νt, the variable τ is a random variable and there is no correlation between τ and
Exp* and there is also no correlation between τ and Rev*.
From the sixth expression two important properties of the variable ν can be distillated. The first
property is that the mismatches expenses are acting as noise in the revenue-expense relation. The
second property is that νt has a strong negative first-order autocorrelation, which becomes clear
when realized that νt = τt – τt-1 and νt-1 = τt-1 – τt-2. This negative autocorrelation in the noise term
illustrates that in due time all mismatching of expenses will be resolved and that accounting acts selfcorrecting in the long run (Dichev and Tang 2008).
Because νt is formulated as τt – τt-1, it is assumed that all mismatching reversals occur within one
period from the moment that they originated. Expression (6) gives the essence of what happens in a
situation of poor matching. In a situation of poor matching, the recorded expense (Expt) differs from
the perfectly matched expense, because of two influences. First of all because of the currently
mismatched expenses (τ1) and second of all because of the current correction of previously
mismatched expenses (τt-1). From this equation it becomes clear that when τ is large, there is an
increase in the level of noise in the current period and matching becomes worse. Measuring the
inverse of the variance of τ can be regarded as measuring the quality of matching (Dichev and Tang
2008).
From the sixth equation it follows that the model assumes that all mismatching of expenses is
resolved within one period. This simplification is done for convenience, however the conclusions that
can be drawn from this model remain virtually the same if the model would assume the reversals of
mismatched expenses would occur over a longer period of time (Dichev and Tang 2008).
23
4.2 Hypotheses
The model of poor matching gives rise to five observations that will be the object of testing in order
to form a conclusive answer to the four hypotheses. Since the mathematical proof for these
observations are quite substantive they are included in Appendix 2 to prevent them from clouding
the essence. Naturally, if needed the mathematical statements will be used in testing the
hypotheses.
The first observation is that poor matching has a decreasing effect on the contemporaneous
correlation between revenues and expenses. This effect is expected because in a situation of poor
matching a part of the perfectly matched expenses get scattered across period (Dichev and Tang
2008). As a result of this it can be expected that the contemporaneous correlation between expenses
and revenues that will be observed is lower than the underlying economic correlation of advancing
expenses that are needed to generate revenues. The empirical tests conducted by Dichev and Tang
(2008) and Donelson e.a. (2010) showed that for US firms poor matching has a negative effect on this
correlation. Dichev and Tang (2008) also provided evidence that accounting standards are
responsible for the declining revenue-expense relation. From the results of this study and the
knowledge that European accounting standards have increasingly started to resemble US standards ,
it can be expected that European firms will show a similar effect.
The first research hypothesis states:
“Poor matching decreases the contemporaneous correlation between revenues and expenses.”
In order to visualize the expect trend in the revenue-expense relation, a regression of revenues on
one-year back, present and one-year forward expenses is used:
(7)
Revt = α + β1*Expt-1 + β2*Expt + β3*Expt+1
The second observation is that poor matching increases the volatility of earnings. This can be
explained by the fact that mismatched expenses acts as noise which is not related to the process of
generating earnings. Mathematically this is made visible by first looking at the earnings relation in
the poor matching model and substituting the function of vt into equation (6):
(8)
Et =Revt* - Expt* -τt + τt-1
Which is equal to:
(9)
Et = Et* - τt + τt-1
If after that the variance of earnings is taken, the following equation results:
(10) Var(Et) = Var(Et*) +2Var(τ)
Which can be rewritten as:
(11) Var(Et) = Var(ε)(1 + β1*2 + β1*4 + … ) + 2Var(τ)
Comparing expression (11) with expression (5) shows that due to poor matching the volatility of
earnings has increased. Therefore it can be stated that in the theoretical model the mismatched
24
expenses are responsible for additional volatility to the already existing and unavoidable economicsdriven volatility (Dichev and Tang 2008). The results on US firms clearly indicated a trend of
increasing earnings volatility as a result of poor matching, which guides the expectations for the
second hypothesis.
The second research hypothesis:
“Poor matching increases the volatility in earnings.”
In order to examine this hypothesis the trend in the volatility of earnings is calculated and compared
to the volatility of revenues and the volatility of expenses. Adjusting the latter two for the correlation
between expenses and revenue will enable to distillate the volatility effects caused by poor
matching.
The third observation is that poor matching decreases the persistence of earnings. The persistence of
earnings is defined as the slope coefficient from a current earnings on lagged earnings regression. By
using a ordinary least squares estimation on β1* the model depicts that in a situation of poor
matching this slope coefficient is decreasing, having a negative impact on the persistence of earnings
(Dichev and Tang 2008). Linked to the decrease in the persistence of earnings is the observation that
poor matching causes a negative autocorrelation in earnings changes. This effect arises because in a
situation of poor matching noise is introduced to the revenues-expense relation, which is the
negative autocorrelation(Dichev and Tang 2008). Both the change in earnings persistence as well as
the change in negative autocorrelation will be examined under the same hypothesis, because these
are just different sides of the same effect, with low persistence in earnings implying a high negative
autocorrelation in earnings changes (Dichev and Tang 2008). The study of Dichev and Tang as well as
that of Donelson, Jennings and McInnis (2010) found a relation between poor matching and a
decrease in the persistence of earnings. For this study the results are expected to be similar.
The third research hypothesis:
“Poor matching decreases the persistence of earnings.”
The hypothesis will be tested by calculating and depicting both one-year and two-year specifications
of earnings persistence and autocorrelation in earnings changes.
Observation four is that in the long run the effects of poor matching are resolved. This is due to the
self-correcting nature of accounting, which implies that over a longer period of time all mismatching
errors will get resolved (Dichev and Tang 2008). By using the assumption that all mismatched
expenses will get resolved within one year, it can be shown that a five year period has relatively less
mismatching because the three years in the middle are already resolved. Using the findings of
Dichev and Tang (2008) as support It is expected that stretching the time horizons eases the effect of
poor mismatching.
The fourth hypothesis:
“The effects of poor matching are eased over longer-time horizons.”
25
The effects of poor matching in longer-horizons will be examined by comparing the two-year results
of the earnings volatility, the autocorrelation in earnings changes and the earnings persistence from
the second and third hypothesis with the results from the one-year variables.
The results obtained from the previous four hypothesis will form the basis for an attempt to
conclude whether there is a link between the quality of matching and the informativeness of
earnings.
4.3 Sample
Since the objective of the research is to examine whether the effects on the revenue-expense
relation for European firms resemble those found for US firms, comparability is a key factor. In order
to a optimize this comparability between the European and the American situation, the mains ample
in this study will have similar characteristics as the sample used in the studies of Dichev and Tang
(2008).However because data availability for European companies starts at 1980 two additional
samples will be constructed that are subjected to less stringent requirements in order to provide a
form of robustness to the findings.
4.3.1 Firms
The object of this study are listed firms of European countries that were a member of the European
Union in 1989 when the IASC developed its Framework for the Preparation and Presentation of
Financial Statements(IASC 1989). For every year that the period of the study covers, the top 1000
non-financial firms in terms of total assets will be selected for the sample. Banks and other financial
firms are excluded because their financial structure is unique and different from non-financial firms.
They are also subjected to regulatory requirements and accounting standards which are specifically
drafted for their industry. Selecting the top 1000 firms counters a bias which is present in all primary
databases. According to Klein and Marquardt (2006), databases like Compustat and Thomson One
Banker have a much more complete coverage of firms in the more recent years, which potentially
can introduce systematic biases in reported results. Isolating the top 1000 firms for every individual
year has the advantage that the coverage of firms throughout the sample period remains the same.
4.3.2 Period
The tests performed in this research will be time-series tests that look at longer horizons effects. The
problem with performing time-series tests over a longer period of time is that that the composition
of firms in the market are subject to changes (Dichev and Tang 2008). New firms originate, while
other firms exit the market either on a voluntary basis or due to failure. Also the reciprocal
importance of companies shifts. This makes it difficult to preserve comparability, however this is
countered by the conditions on which firms are selected. Since the IASC developed its Framework for
the Preparation and Presentation of Financial Statements in the late 1980’s this study will span its
tests to a period before this event. Data availability in Thomson One Banker, which is the database
that holds the most information on accounting variables for European firms and therefore the
primary database in this research, is a key factor in determining the starting point of the testing
period. Research on available data in Thomson One Banker indicates that 1980 can be used as a
starting point for European companies. This implicates that the research period from which data will
be obtained will span from 1980 till 2010.
26
4.3.3 Accounting data
In order to be added to the samples, firms must have data available on several accounting numbers.
There is a strict condition that firms cannot have missing values for these numbers. In order to be
able to successfully research the hypotheses data needs to be available on assets, revenues, earnings
before extraordinary items and preceding 9 years and subsequent 1 year of revenues and earnings.
The data from preceding years is used to give the variables both a one-year and a two-year
definition. The two-year variables are used to depict the longer time horizon effects and are
calculated by taking the average of the current and the previous period. The requirement of data
availability on the 9 preceding years is vital because it takes up to 10 years of data to calculate the
volatility in two-year earnings. The subsequent year of revenues and earnings is used in the
regression to research the contemporaneous correlation between revenues and expenses. The other
variables needed for the different tests can be calculated from the obtained numbers. In order to
eliminate the impact of differences in firm size, all variables are scaled by the total assets of the firm.
The complete calculations of the variables can be found in appendix two.
4.3.4 Samples 1 and 2
From the companies that fulfill the conditions on accounting data availability, both one-year and
two-year variables are calculated. The one-year variables will form sample 1 and the two-year
variables will form sample 2. Similar to the studies of Dichev and Tang (2008) and Donelson, Jennings
and McInnis (2010) the selection criteria for the construction of both samples will be based on the
two-year availability of the accounting variables, because the two-year definitions are more
restrictive.
Due to this sample 2 which holds the two-year variables is firstly constructed and will be used as the
basis for sample 1. The second sample will hold those firms which have data available on the twoyear specification of assets, revenues and earnings and are amongst the top 1000 firms for each odd
year from 1989 until 2009. From the firms that fulfill these criteria the top and bottom one percent
of all the two-year variables is eliminated in order to prevent possible outliers from influencing the
results of the different tests. The final firm-year observations in sample 2 will be used in testing the
second and third hypothesis on the temporal behavior of volatility and earnings persistence. The
testing of the first hypothesis solely requires one-year specification of the variables and is therefore
limited to sample 1.
Having defined the firms that will be included in sample 2 enables the construction of sample 1.
Sample 1 will consist of those firms which satisfy the one-year variables criteria and are also present
in the two-year sample. This means that the first sample holds only firms for both the odd and the
even years which are also present in the accommodating odd year of sample 2. From the firms that
satisfy this condition, the top and bottom one percent of all the one-year variables is eliminated in
order to remove extreme observations. The resulting firm-year observations in sample 1 will be used
for all the hypotheses that are being tested in this study.
4.3.5 Additional samples 3 and 4
The strict criteria used to construct the two samples, which are comparable to the ones used in the
study of Dichev and Tang (2008) and Donelson, Jennings and McInnis (2010),clearly limit the overall
research period. Since data availability for European companies starts in 1980 and a minimum of 9
preceding years on revenues and earnings is needed in order to be able to calculate two-year
variables, the starting point for the two-year sample is 1989. From 1980 onwards data can be
27
collected until 2010. Because a subsequent year of revenues and earnings is needed to examine the
correlation between revenues and expenses, the research period is limited to 2009. A research
period from 1988 until 2009 means that a total of 22 years of one-year variables for European
companies will be examined. Since the object of this study is to examine the impact of poor
matching on the properties of accounting earnings over a longer horizon, maximizing the research
period will positively contribute to the conclusions that can be drawn from the results obtained
during the testing of the hypotheses. Also the indication from previous research that the introduction
of the Conceptual Framework in the United states in 1976, which initiated a switch in accounting
approaches towards the balance sheet approach, had a significant impact on the quality of matching,
requests for close examination of the possible research period. Since the counterpart of the
Conceptual Framework, the Framework for the Preparation and Presentation of Financial
Statements, was developed in 1989, this study would clearly benefit from a research period that
would include years before 1988.
In order to facilitate this an additional sample, sample 3, will be constructed of European companies
that is subject to less stringent conditions than the ones used for the main sample. For the
construction of this sample only one-year selection criteria will be used, which means that only 4
years of preceding data on revenues and earnings needs to be available. Since this is a stand-alone
sample there is no need to require that the sample holds the same companies as the ones that are
present in sample 2 and therefore the amount of firm-years that this sample holds increases
substantially. The other conditions remain similar to the original sample. Constructing a sample on
this less stringent basis allows the research period to be extended to 1984. Although this sample will
be of no use in depicting or explaining any resolving effects over longer time-horizons, it can be
useful in solidifying a possible trend in the contemporaneous correlation between revenues and
expenses. As a result this sample will be subjected as a robustness test to the same hypotheses as
sample 1. This means that all hypotheses will be tested using sample 3 in order to provide additional
strength to the obtained results from sample 1.
The limited research period due to the stringent selection criteria used in constructing the main
samples has an even greater impact on sample 2. Calculating the two-year accounting variables for
every odd year of the research period results in a mere 11 year observations. Such a low amount of
year observations will most likely result in limitations on the ability to draw significant conclusions for
the obtained test values. This will make it more difficult to conclude whether the effects of poor
matching are resolving over longer time-horizons. In order to counter this problem there will also be
an additional sample constructed to provide robustness to the results of sample 2. For this sample,
which will be sample 4, the two-year specifications for every even year during the research period
will be calculated and will be added to the firm-years already available in sample 2. This results in a
doubling of the year observations to 22 and enables a more grounded observation of the temporal
trend in two-year variables. Since the purpose of sample 4 is to provide robustness and strength to
the results obtained from sample 2, this sample will subjected to the same hypotheses which use
sample 2 in their testing.
A complete overview of the conditions to which the four samples are subjected are found in
appendix four.
28
4.4. Summary
The object of this research is to investigate whether the effects of poor matching on the properties of
earnings that were recently discovered for US firms can also be found with European firms. In order
to examine this a model of both perfect and poor matching are constructed that give rise to a series
of observations which will form the basis of the hypothesis used in this study. These observations are
that poor matching decreases the contemporaneous correlation between revenues and expenses,
increases the volatility of earnings and decreases the persistence of earnings. The final observation is
that in the long run the effects of poor matching are resolved. These observations are tested by
constructing four samples comprising the top 1000 firms from 15 European countries. The
accounting data needed from these firms is extracted from Thomson One Banker and stretches a
research period from 1988 to 2009.
29
5. Descriptive statistics
5.1. Firm year observations
Constructing the first two main samples results in a total of 13.386 firm year observations for the
one-year sample and 6.779 firm year observations for the two-year sample. The final one-year
sample only contains firms which are also present in the two-year sample. The third sample which
only used one-year variables as its selection criteria consists of 21.388 observations from 1984 to
2009. Sample 4 which combines two-year firm observations for each odd-year with two-year firm
observations for each even-year has a total of 13.717 observations (appendix 5).
From the total firm years that were obtained from Thomson One Banker a considerable amount of
observations did not get included into the samples because of two problems that were discovered
upon researching the output that the database produced. First of all there were companies included
in the data that reported a zero on several variables. Since Thomson One Banker considers this to be
a valid entry they were added to the total output. The objective of this study does not require the
thorough investigation as to why these companies reported zero on these variables and as a result of
this a choice was made to eliminate them.
The second obstacle that the output of Thomson One Banker posed was the industry classification of
the companies. There are two variables that can be used as a selection criteria for industry, which are
the general industry classification code and the standard industrial classification code. Although in
theory these industry codes should produce roughly the same output, in practice this turns out not to
be the case. This resulted in a selection based on the general industry classification which still
contained companies which were regarded financial institutions according to the standard industrial
classification code. In order to solve this problem the financial firms of both classification codes were
eliminated.
The descriptive statistics of the first two samples, which can be found in appendix 6, are broadly in
line with those obtained in the study of Dichev and Tang (2008) and Donelson, Jennings and McInnis
(2010). There is only a slight deviation observable when comparing the different variables. The
samples of both the one-year variables and the two-year variables show a clear pattern. The mean of
earnings in both samples is somewhat lower, whereas the means of revenues and expenses as well
as those of the volatility and correlation in both sample 1 and sample 2 are slightly higher. The
standard deviations of volatility and correlation variables samples fluctuate a bit more in both
samples but again can be considered generally in line with previous research.
5.2 Testing for parametric data
In order to be able to test the hypotheses for this research it is necessary to test whether the
samples that were created meet the requirements to do statistical tests on them. Since the statistical
test that will be performed are parametric tests based on a normal distribution there are three
assumptions that must be met, which are the assumption of normality, the assumption of
homogeneity of variance and the assumption of independence (Field, 2009).
5.2.1Normality of data
In order to be able to use statistical tests to make predictions on the expense-revenue relation it is
vital that data in the sample that we constructed is normally distributed. When this data is in fact
normally distributed it can be used to draw conclusions on the sample distribution. This sampling
30
distribution is the frequency distribution of sample means of the entire population. Since it is
impossible to gather several hundreds of samples from the entire population in order to make a
perfect sampling distribution an approximation is used. The theory behind this approximation, which
is called the central limit theorem, states that as samples get large, the sampling distribution has a
normal distribution, in which the mean is equal to the mean of the population with a standard
deviation of s/√𝑁. In big samples the sampling distribution will tend to be normally distributed and
can be used to draw conclusions on the population distribution. If the sample used is greater than 30
it can be assumed that sample data is approximately normally distributed and the assumption of
normality of data is fulfilled.
The distribution of the samples can be checked visually by plotting the variables as a histogram or as
a Probability-Probability plot (P-P plot). The P-P plot is used to plot the cumulative probability of a
variable against the cumulative probability of a particular distribution. Since the object is to test for
normality, this distribution would be a normal distribution. The results of these plots for the first
sample can be found in appendix 7. Although both show signs of both skewness and kurtosis in the
distribution of the sample, the possible threat of these phenomena are negligible due to the large
sample size.
Besides visually, the distribution of the samples can also be quantified with numbers. The test that is
most often used to check whether data satisfies the assumption of normality is the KolmogorovSmirnov test (K-S test). This test compares the distribution of the sample with that of a normal
distribution to see whether the distribution of the sample deviates from the normal distribution. The
scores of the sample are compared to a normally distributed set of scores with the same mean and
standard deviation. If the test results in a significant result, which is clearly the case in the samples
used for this study, then the sample distribution is significantly different from a normal distribution.
However, the K-S test is somewhat limited when it comes to testing large samples, since small
deviations from normality will already provide a significant result.
5.2.2 Homogeneity of variance
The second assumption that must be met is that the variance of the variables in the data that is used
for testing is homogeneous. Homogeneity of variance is present when the variance of other
variables does not change with different levels of a particular variable. In order to check this
assumption the Levene’s test is often used. In the Levene’s test the null hypothesis that the variances
in different groups are zero is tested. A significant result means that the null hypothesis is rejected
and that variances are significantly different. This would mean that the variances are heterogeneous
instead of homogeneous. The Levene’s test has the same limitation when it comes to large sample
sizes as the K-S test. Small differences will easily produce a significant result, due to the improved
power of the test.
5.2.3 Independence
The characteristics of the independence assumption largely depend on the specific statistical test
that is being performed, but in general it can be stated that the data from the different participants
in the sample needs to be independent. As a result, the data of one participant must not influence
the data of another. This independence is usually lost in situations where the behavior of one
participant influences the behavior of another. The samples used in this study consist of Europeans
companies for which various accounting variables are obtained. These companies clearly satisfy the
31
independence assumption from a general perspective. When using a regression model the
assumption of independence relates to the errors of the model and will require a more specific
verification.
5.3. Testing for regression
Besides the general assumptions for statistical tests on parametric data, every model also has its own
assumptions. These assumptions must be met in order to be able to generalize the results obtained
when using this model. A regression will be used multiple time during this study, which make it
necessary to test whether the data used in the samples suffice the nine assumptions of the
regression model (Berry, 1993).
The first assumption is that all predictor variables need to be quantitative or categorical and that the
outcome variable needs to be quantitative continuous and unbounded. The second assumption
requires that the predictor variables have some variance. Both of these assumptions are obviously
met by all samples.
The third assumption requires that there is no perfect multicollinearity between two or more
predictor variables. This means that there should not be a strong correlation amongst the predictor
variables. Although low levels of multicollinearity are virtually unavoidable, high levels of
multicollinearity posses three possible threats to the regression model. The first is that the standard
error of the betas is increased, decreasing the overall trustworthiness of the betas. The second is that
the size of R, which is a measure of the multiple correlation between the predictors and the outcome
variable, is limited. The final threat is that multicollinearity makes it difficult to assess the individual
importance of a predictor variable.
The level of multicollinearity between predictor variables can be examined by looking at the variance
inflation factor (VIF). The VIF can depict whether a predictor variable has a strong linear relationship
with the other predictor variables. A value higher than 10 is usually indicating that there might be
multicollinearity present in the regression model. The VIF results for the first sample are included in
appendix 8. These are the VIF values resulting from the regression model used for testing the first
hypothesis. This model contains the following three predictor variables: Expensest-1, Expensest and
Expensest+1. It is clear from the results that the second predictor variable shows clear signs of
multicollinearity with almost all values being well above 10. The year 2002 peaks with a value of
27,457. This means that there is a strong correlation of the second predictor variable with the first
and the third predictor variable. The first and third predictors both have values closer to the critical
level of 10. This is means that as time passes the correlation between expenses variables decreases
which is a logical result. Although the levels on multicollinearity are high, they clearly show that
expenses correlate the highest with the expenses of the period directly next to it. Since this relation
is part of the objective of this study the high levels of multicollinearity do not threat the usability of
the regression model.
The fourth assumption is that the predictor variables used in the regression model are uncorrelated
with external variables. From the results obtained when examining the assumption of no perfect
multicollinearity it can concluded that there will be external variables which correlate with one or
more predictor variables used in the model. Obviously Expensest-1 will show a significant level of
correlation with Expensest-2 and Expensest+1 will show a similar correlation with Expensest+2. However
this does not interfere with the predictive power of the variables used in the regression model.
32
The fifth assumption is that there is homoscedasticity amongst the variance of the residual terms.
This assumption of homoscedasticity is similar to the assumption that is required for parametric data.
The sixth assumption is that for any two observations the residual terms should be independent. This
is the specific interpretation for the regression model of the independence assumption already
mentioned when examining the assumptions of parametric data. A Durbin-Watson test can be used
to depict whether the residual terms are correlated. The test results in a value between 0 and 4,
where a value below 2 indicates a negative correlation between residuals and a value above 2
indicates a positive correlation. The value of 2 implicates that the residual values are uncorrelated.
The results of the Durbin-Watson test for the first sample can be found in appendix 8 and clearly
show that all the results approach the value 2 with a total spread between 1,808 and 2,131. From
these results it can be concluded that there is a very low level of correlation between the residual
values of any two observations.
The seventh assumption requires the residuals in the model to be random, normally distributed
variables with a mean of 0. The contrary only occurs occasionally, and is especially rare in large
samples. The eight assumption assumes requires that every outcome variable comes from a separate
entity. In the samples used for this study, this is clearly the case since the accounting variables are
obtained from different companies. The ninth assumption and final assumption is the assumption of
linearity, which assumes that the mean value of the outcome variable can be depicted by a straight
line. The regression model is used to model a linear relationship.
5.4. Summary
The descriptive statistics of the main samples are broadly in line with those obtained in the study of
Dichev and Tang (2008) and Donelson, Jennings and McInnis (2010).This implicates that the samples
can be used to test the hypothesis and provide comparison with previous research. However, in
order to be able to test the hypotheses for this research it is necessary that the samples meet the
requirements of parametric data. These requirements are that the data in the samples is normally
distributed, that the variances of the variables in the data is homogeneous and that the data
originates from independent participants. Besides the requirements of parametric data, the samples
also need to fulfill the seven assumptions of regression, in order to be able to generalize the results
obtained when using this model. Using a variety of tests evidence was provided that the samples in
this study fulfill all requirements and meet all assumptions.
33
6. Test results
6.1. Hypothesis 1: Revenues-expense relation
The model that was constructed to examine the effects of poor matching on the properties of
earnings gave rise to a series of observations. The first observation is that poor matching has a
decreasing effect on the contemporaneous correlation between revenues and expenses. The
underlying thought of this decreasing effect is that in a situation of poor matching the perfectly
matched expenses get scattered across different periods, away from their associated revenues
(Dichev and Tang 2008). As such it can be expected that the contemporaneous correlation between
expenses and revenues that will be observed is lower than the underlying economic correlation of
advancing expenses that are needed to generate revenues. The first hypothesis will examine whether
this effect is also observable with European companies in the last twenty-two years.
The hypothesis stating that poor matching decreases the contemporaneous correlation between
revenues and expenses is tested by a regression of revenues on one-year back expenses, present
expenses and one-year forward expenses:
Revt = α + β1 Expt-1 + β2 Expt + β3 Expt+1
The temporal behavior of the coefficients of this regression,β1 for one-year back expenses, β2 for
present expenses, β3 for one-year forward expenses will be examined in order to see if a decreasing
correlation between revenues and expenses is in fact observed. Rising coefficients of one-year back
and one-year forward expenses will indicate that expenses indeed are being scattered increasingly
across periods. As a result it can be concluded that the contemporaneous correlation between
revenues and expenses has decreased.
Sample 1
The first sample to be tested is sample 1 which has a research period stretching from 1989 to 2009
and holds 12.306 firm-year observations. The selection criteria used are similar to the ones used in
the study of Dichev and Tang (2008) and Donelson, Jennings and McInnis (2010). An overview of the
results for the expenses coefficients of every sample will be displayed, additional information on the
significance levels of the coefficients and the squared correlation coefficients (R2) can be found in
appendix 9.
34
Sample 1
Year
Coefficient
on past
expenses
(β1)
Coefficient
on current
expenses
(β2)
Coefficient
on future
expenses
(β3)
Year
Coefficient
on past
expenses
(β1)
Coefficient
on current
expenses
(β2)
Coefficient
on future
expenses
(β3)
1988
-0,029
1,044
-0,008
1999
-0,021
1,004
0,019
1989
0,028
0,982
-0,001
2000
0,017
0,960
0,027
1990
0,044
0,953
0,016
2001
0,008
0,942
0,048
1991
-0,011
1,012
0,009
2002
-0,026
0,951
0,063
1992
-0,026
0,996
0,040
2003
-0,012
0,968
0,037
1993
-0,030
0,994
0,038
2004
-0,015
0,981
0,033
1994
-0,030
1,022
0,007
2005
-0,033
1,020
0,014
1995
-0,008
0,940
0,066
2006
-0,029
1,000
0,022
1996
-0,028
0,976
0,054
2007
0,038
0,942
0,031
1997
-0,031
1,030
0,007
2008
-0,027
0,967
0,063
1998
-0,009
0,966
0,048
2009
-0,079
1,014
0,074
Coefficient on past
expenses (β1)
Coefficient on current
expenses (β2)
Coefficient on future
expenses (β3)
Mean 1988 to 1998
-0,012
0,992
0,025
Mean 1999 to 2009
-0,016
0,977
0,039
Difference
-0,004
-0,015
0,014
0,674
0,231
0,163
P-value difference
It is clear that the coefficient on current expenses shows mostly consistent values for the year 1988
to 1994 averaging around 1. The exception to this is 1990 where the coefficient is substantially lower
with 0,953. From 1995 there is a lot more deviation in the coefficients and an increase in spread.
1997 and 1999 disrupt a series of coefficients that average around 0,960. In the final five years of the
sample the coefficients on current expenses show a reversing pattern with three coefficients above 1
and only two coefficients substantially below that. Examining the significance levels of the
coefficients on current expenses reveals that all coefficients are significant at the 5 percent level. The
mean for the first half of the sample stretching from 1988 to 1998 is 0,992. The second half of the
sample which covers the years 1999 to 2009 has a mean of 0,977. This is a total decrease of 0,015.
Comparing these means using a paired sample t-test returns a p-value of 0,231, which leads to the
conclusion that the difference in means cannot be considered statically significant.
Examining the coefficient on past expenses reveals a temporal decrease, which is influenced largely
by the values of 0,028 for the year 1989 and 0,044 for the year 1990. In both years the coefficient on
past expenses shows a relatively high positive value which deviates largely from the average of the
other nine years in the first half of the sample, which is around -0,020. As a result of the two positive
results, the mean for the first half of the sample amounts to -0,012. In the second half there is a clear
35
rising in the overall values of the coefficients with the exception of 2009. In this year the coefficient
on past expenses has a value of -0,079, which results in a total mean for the second half of the
sample of -0,016. The total increase between the mean stretching from 1988 to 1998 and the mean
stretching from 1999 to 2009 is 0,004. The p-value of this difference is 0,674 which is not statistically
significant.
The coefficients on future expenses show a trend where there is a wide spread in the first half of the
sample, ranging from -0,008 in 1989 to 0,066 in 1995. The mean of the first half of the sample is
0,025. Such a deviation is less profound in the second half of the sample. There is a decreasing trend
from 2003 to 2005, but this is still at the average level of the first half of the sample. After 2005, the
value of the coefficients on future expenses keep rising until the end of the sample. The total mean
for the second half of the sample is 0,039. The difference between the two means is an increase of
0,014, which validates the observation that there is a more profound level of scattered expenses.
However, a paired sample t-test of the differences in means results in a statistically insignificant pvalue of 0,163.
An examination of the significance levels of the coefficients on past earnings shows that for 9 betas
the 5 percent significance level is not met. For the coefficients on future earnings there are 4
insignificant beta values. This means that the boundaries for the coefficient to obtain a 95 percent
confidence interval is larger and that the true value of the coefficient can deviate to some extend
from the value returned by the model. Since all the coefficients of the present expenses were
presented with a statistically significant value, the insignificant betas of past expenses do not
intervene with the possible conclusion whether the contemporaneous correlation between revenues
and expenses has decreased, but it does have implications for possible conclusion as to which period
the scattering of expenses actually occurred.
When inspecting the squared correlation coefficient (R2) for sample 1, which can be used to measure
how much of the outcome variable is accounted for by the predictor variables, it becomes clear that
all years obtain a R2 values above 0,992. This indicates that the model is an almost perfect fit and that
most of the outcome variable, which is present revenues, is explained by the predictor variables past,
present and future expenses.
From the results obtained it is not possible to formulate a positive answer to the first hypothesis.
Although temporal trends found in the coefficients on present en future earnings indicate that
expenses are increasingly being scattered across periods, sample 1 does not allow to back this
intuition with statistically significant values. As a result hypothesis one is rejected.
Sample 3
Since the stringent conditions of sample 1 limit the research period to 22 years an additional sample
was constructed. This sample uses only one-year variable selection criteria and, consists of 18.834
firm-year observations and extends the research period with 4 years from 1984 to 2009. The firms in
this sample are not bounded by selection criteria of other samples. Sample 3 will be used as a
support and robustness test to see whether extending the research period has implications for the
ability to depict the temporal trend of mismatched expenses. Sample 1 indicated a trend of a
decreasing contemporaneous correlation between revenues and expenses due to decreasing
coefficients on current expenses and increasing coefficients on future expenses. Although hypothesis
36
one was rejected due to insignificant p-values on the mean differences, the non-significant results
obtained from sample 1 could be related to the limitations on the research period. Sample 3 can be
used to see whether an extended research period has implications on the conclusions that can be
drawn regarding hypothesis one. The regression results for sample 3 can be found in appendix 9.
Sample 3
Year
Coefficient
on past
expenses
(β1)
Coefficient
on current
expenses
(β2)
Coefficient
on future
expenses
(β3)
Year
Coefficient
on past
expenses
(β1)
Coefficient
on current
expenses
(β2)
Coefficient
on future
expenses
(β3)
1984
-0,033
1,043
-0,002
1997
-0,032
1,031
0,005
1985
0,013
0,982
0,007
1998
-0,002
0,957
0,049
1986
-0,014
1,010
0,009
1999
-0,018
0,996
0,022
1987
-0,013
0,994
0,024
2000
0,011
0,962
0,028
1988
-0,036
1,046
-0,005
2001
0,005
0,948
0,044
1989
0,016
0,992
0,000
2002
-0,009
0,938
0,062
1990
0,023
0,978
0,012
2003
-0,014
0,966
0,040
1991
-0,008
1,007
0,012
2004
-0,010
0,971
0,038
1992
-0,022
0,993
0,040
2005
-0,019
0,995
0,022
1993
-0,035
1,008
0,031
2006
-0,026
0,990
0,032
1994
-0,022
0,989
0,027
2007
0,036
0,954
0,021
1995
-0,017
0,948
0,065
2008
-0,022
0,977
0,050
1996
-0,030
0,987
0,044
2009
-0,079
1,017
0,074
Coefficient on past
expenses (β1)
Coefficient on current
expenses (β2)
Coefficient on future
expenses (β3)
Mean 1984 to 1996
-0,014
0,998
0,020
Mean 1997 to 2009
-0,014
0,977
0,037
0
-0,021
0,017
0,993
0,042
0,028
Difference
P-value difference
Examining the first half of coefficients on current expenses reveals higher values on most of the
betas. The values for the additional four years average a little over 1, which has an increasing impact
on the mean of the first half. With a coefficient on current expenses of 0,948 the year 1995 is
substantially lower than the rest of years from 1984 to 1996. This is similar to what was observed in
sample 1. The added years and the higher coefficient values result in a mean of 0,998 for the first half
of the sample. In the second half of the sample there is less deviation and a lower spread amongst
the coefficients on current expenses with only two years having a value above 1, which are 1997 with
1,031 and 2009 with 1,017. In sample 1 the second half of coefficients on present expenses had four
values above 1. The mean of the second half of the sample is identical to the one in sample 1 with
37
0,977. As a result the difference between means of coefficients on present earnings in sample 3 is
0,021, which results in a statistically significant p-value of 0,042.
The first half of the coefficients on past expenses resembles sample 1 quite closely. There is a little
less deviation and from the additional four years 3 values are below average which results in a
slightly lower mean of -0,014 for the years 1984 to 1996. In the second half of the sample the
coefficients remain closer to the value zero which results in a higher mean. The value of the
coefficient on past expenses for the year 2009, which is -0,079, remains substantially lower than the
rest of the years in the second half. The total of the mean for the years from 1997 to 2009 is -0,014.
Since there is no difference between the means and only slight variation amongst the years, the pvalue of the paired same t-test is 0,993. Although this value obviously implicates an insignificant
result it is noteworthy that the coefficients on past expenses went from an increase in sample 1 to a
neutral level in sample 3.
The values of the coefficients on future expenses for the starting years 1984 to 1989 are close to 0.
The remaining years of the first half show a similar pattern as sample 1 averaging around 0,025. Only
the coefficients for the years 1995 and 1996 show a large deviation and can be considered
substantially higher than the average. The total mean for the first half of sample 3 is 0,020, which is
0,005 lower compared to the mean of sample 1. The coefficients in the second half of the sample are
again comparable to values obtained in sample one, where an increasing trend is noticeable towards
the end of the sample. The total mean of the coefficients for the years 1997 to 2009 is 0,037,
resulting in a mean difference of 0,017. The p-value for this difference is 0,028 is outside the 95
percent confidence interval and is therefore statistically significant. The increase in mean difference
and the significant p-values for both the coefficient on present expenses as well as the coefficient on
future expenses are a clear indication that the additional years at the beginning of the sample do
influence the ability to draw conclusions for the temporal behavior of the expense coefficients.
Examining the significance levels of the coefficients learns that eleven of the coefficients on past
expenses are above the five percent confidence interval. For the coefficients on future expenses a
significance value which is higher than 0,025 is only found in five cases. Taking into account that
sample 3 has an additional four years of observations, the amount of insignificant beta values is in
line with sample 1. For every year that is present in the third sample, the value of R2 is above 0,992.
The significant p-values obtained for the coefficients on present and one0year forward expenses
alter the conclusion that were drawn regarding hypothesis one. The temporal behavior of these
coefficients indicate that expenses indeed are being scattered increasingly across periods. As a result
it can be concluded that the contemporaneous correlation between revenues and expenses has
decreased. Hypothesis one is therefore supported.
6.2. Hypotheses 2: Volatility of earnings
The second observation that resulted from the model is that poor matching increases the volatility of
earnings. The underlying reasoning for this is that mismatched expenses acts as noise which is not
related to the process of generating earnings. Using mathematical reasoning expression (11) clearly
showed that the volatility of earnings increases due to poor matching. As a result of this the
mismatched expenses add an additional level of volatility to the already existing and unavoidable
economics-driven volatility to which every firm is subjected (Dichev and Tang 2008).
38
The hypothesis that poor matching increases the volatility in earnings will be tested by looking at the
temporal behavior of the variables for volatility of earnings, volatility of revenues, volatility of
expenses and the correlation between revenues and expenses. A detailed explanation on the
calculation of these variables can be found in appendix 3. The variables for the volatility of revenues
and expenses are added in order to be able to depict the fact that earnings volatility arises from the
underlying fundamentals of a business in addition to poor matching. Knowing that earnings is equal
to revenues minus expenses, the volatility of earnings is equal to the volatility of revenues plus the
volatility of expenses, adjusted for the correlation between revenues and expenses (Dichev and Tang
2008.) By using this manner of testing the correlation between revenues and expenses reflect the
quality of matching while the volatility of revenues and the volatility of expenses reflect the
underlying business fundamentals. Including these variables makes it possible to assess whether the
expected increase in volatility of earnings is due to an increase in the volatility of the fundamentals
or due to poor matching.
Both sample 1 and sample 2 will be used in testing this hypothesis. Sample 1 will provide the results
for the one-year specification, sample 2 will provide these results for the 2-year specification. Since
the research period of both samples is somewhat limited and sample 2 only consists of eleven year
observations additional robustness is provided by including the results of both sample 3 and sample
4.
Sample 1
Volatility over time for the one year sample
Year
Vol
(Earn)
Vol
(Revs)
Vol (Exp)
Corr
(Rev,
Exp)
Year
1988
0,021
0,273
0,261
0,995
1999
1989
0,020
0,237
0,226
0,994
1990
0,016
0,216
0,208
1991
0,017
0,200
1992
0,020
1993
Vol
(Earn)
Vol (Rev)
Vol (Exp)
Corr
(Rev,
Exp)
0,022
0,157
0,154
0,973
2000
0,021
0,161
0,158
0,976
0,994
2001
0,025
0,165
0,164
0,965
0,197
0,991
2002
0,024
0,168
0,167
0,972
0,201
0,198
0,986
2003
0,024
0,179
0,179
0,975
0,024
0,191
0,187
0,983
2004
0,024
0,197
0,193
0,975
1994
0,022
0,160
0,154
0,977
2005
0,026
0,188
0,181
0,971
1995
0,022
0,170
0,163
0,979
2006
0,026
0,169
0,159
0,968
1996
0,023
0,181
0,173
0,982
2007
0,024
0,174
0,164
0,975
1997
0,025
0,191
0,183
0,978
2008
0,024
0,179
0,172
0,975
1998
0,022
0,163
0,158
0,977
2009
0,028
0,176
0,168
0,971
39
Vol (Earings)
Vol (Revenues)
Vol (Expenses)
Corr (Revenues,
Expenses)
Mean 1988 to 1998
0,021
0,198
0,192
0,985
Mean 1999 to 2009
0,024
0,174
0,169
0,972
Difference
0,003
-0,024
-0,023
-0,013
P-value difference
0,006
0,083
0,079
0,000
Examining the volatility of earnings in the beginning half of the sample stretching from 1988 to 1998
shows most values close to 0,02. The two years that fall substantially below this are 1990 with a
value of 0,016 and 1991 with a value of0,017. The years 1993 and 1997 report volatility values that
are clearly higher than the remaining years. This results in a total mean for the first half of the sample
of 0,021. In the second half of the sample a steady increase in the volatility is visible. Only the
volatility value for the year 2000 touches the average of the first half of the sample, the rest remains
above this level. The highest volatility of earnings is reported in the final year of the sample with
0,028. The mean for the years stretching 1999 to 2009 is 0,024 which is an increase of 0,003
compared to the first half of the sample. Subjecting the difference of these means to a paired sample
t-test results in a p-value of 0,006 which means there is a statistical significant difference between
the mean of the first half of the sample and the mean of the second half of the sample.
Since the combination of the volatility of revenues and the volatility of expenses represent the
volatility of the underlying business fundamentals, the year observations for these two variables will
be examined together. For the first half of the sample both the volatility of revenues and the
volatility of expenses show a decreasing pattern up until 1994. The volatility of revenues and
expenses respectively denote a value of 0,273 and 0,261 for the year 1988, which is gradually
reduced to 0,160 and 0,154 in 1994. As a result of this the mean for the first half of the sample is
0,198 for the volatility of revenues and 0,192 for the volatility of expenses. The second half of the
sample starts off with an increase in volatility levels, peaking in the year 2004 with a volatility of
revenues of 0,197 and a volatility of expenses of 0,194. After this the remaining years show a slight
decrease in volatility and remains approximately constant at the 0,17 level toward the end of the
sample. The mean for the years 1999 to 2009 is 0,174 for the volatility of revenues and 0,169 for the
volatility of expenses. The differences between the means of the two variable is almost identical,
with -0,024 for the volatility of revenues and -0,023 for the volatility of expenses The p-value for
these differences resulting from the t-test are both low, but insignificant. The model returns 0,083
for the volatility of revenues and 0,079 for the volatility of expenses.
The results for the correlation between revenues and expenses shows a steady decreasing trend with
a minimal amount of deviations. The first half of the sample starts with four very high levels of
correlation all above 0,99, after which the a decrease is initiated towards levels around 0,98. The
total mean for the years 1888 to 1989 is 0,985. During the second half of the sample the decrease in
correlation levels is steadily continued, with a result of 0,965 in 2001 being the lowest. The mean for
the correlation between revenues and expenses during the second half of sample 1 is 0,972 which
results in a total difference of 0,013 between the two means. A resulting p-value of 0,000 for the ttest on this difference indicates that there is a statistically significant difference between the first and
the second half of the sample.
40
The results indicate that the volatility has substantially increased during the research period. This
increase cannot be attributed to an increase in volatility of revenues and expenses. Although the
differences in means did not result in a statistically significant result it is safe to conclude from the
observed behavioral trend in the fundamentals that these variables show a stronger sign of a
possible decrease in volatility instead of an increase in volatility. Together with the fact that there is a
statistically significant decrease in the correlation of revenues, it can be concluded that the observed
increase in volatility during the research period of sample 1 is caused by poor matching. This results
in the conclusion that data on volatility obtained from the first sample supports the second
hypothesis.
Sample 2
The second sample to be tested is sample 2, which holds the two-year specification for all variables
which are calculated for every odd year that the research period covers. The sample holds 6.248
firm-year observations and uses similar selection criteria as the ones used in the study of Dichev and
Tang (2008).As a result of observation number 4 which states that over time the effects of poor
matching resolve, it can be expected that sample 2 which holds only two-year variables will show less
pronounced effects for the volatility of earnings and the correlation between revenues and expenses
compared to sample 1.
Sample 2
Year
N
Vol (two-year
Earnings)
Vol(two-year
Revenues)
Vol(two-year
Expenses)
Corr(two-year
Revenues, twoyear Expenses)
1989
503
0,020
0,272
0,260
0,996
1991
480
0,018
0,303
0,293
0,997
1993
518
0,022
0,338
0,328
0,996
1995
523
0,020
0,236
0,229
0,992
1997
612
0,022
0,239
0,231
0,990
1999
575
0,023
0,222
0,213
0,988
2001
546
0,024
0,229
0,220
0,986
2003
555
0,020
0,216
0,212
0,990
2005
610
0,023
0,238
0,231
0,990
2007
653
0,023
0,231
0,219
0,988
2009
673
0,026
0,247
0,235
0,987
Vol (two-year
Earnings)
Vol(two-year
Revenues)
Vol(two-year
Expenses)
Corr(two-year
Revenues, two-year
Expenses)
Mean 1989 to 1997
0,020
0,278
0,268
0,994
Mean 1999 to 2009
0,023
0,231
0,222
0,988
Difference
0,003
-0,047
-0,046
-0,006
P-value difference
0,072
0,054
0,045
0,009
41
Examining the first half of the sample with two-year specifications reveals that the volatility of twoyear earnings starts at a similar level, but shows less deviation in the following years. Most
observations stay closer to the 0,02 level, which is in line with the expectation that effects will be less
pronounced in two-year variables. The volatility of two-year earnings in 1991 is 0,018 compared to
the 0,017 of the volatility in the one-year sample. For the years 1993, 1995 and 1997 the one-year
sample result in values of 0,024, 0,22 and 0,025 whereas the two-year sample again shows less
deviation from the 0,02 level with 0,022, 0,020 and 0,022. The total mean for the first half of sample
2 is 0,020. The rising pattern in volatility levels that occurred in sample 1 is also visible in sample 2,
with the remark that this rising pattern is somewhat more subtle due to the characteristics of the
two-year specification. All levels of volatility of the two-year earnings for the years 1999 to 2009 are
less pronounced to their one-year counterpart in sample1. The total mean for the second half of
sample 2 is 0,023, which results in a difference of 0,003 between the two means. The p-value
returned for this difference using the t-test is 0,072, which is too large to qualify as a significant result
by just a fraction.
The volatility level in two-year revenues and two-year expenses in the first half of the sample do not
show the steady decline that is so clearly present in the first sample. With 0,272 and 0,260, 1989
returns a higher value for both variables compared to sample 1 and this value increases even further
for the years 1991 and 1993. The remainder of the years in the first half show a sharp decline in
volatility resulting in a total mean for the years 1989 to 1997 of 0,278 for the volatility of two-year
revenues and 0,268 for the volatility of two-year expenses. In the second half of the sample the
decline that was initiated in 1995 is continued in 1997, but changes into an increasing pattern with
fluctuations after that. With 0,247 and 0,235 the final year in the sample denotes the highest
volatility levels for the years 1999 to 2009. This results in a volatility mean for the second half of the
sample of 0,231 for two-year revenues and 0,222 for two-year expenses. The difference between the
two means is substantially higher than the difference observed in sample 1. For the volatility of tworevenues this difference is 0,047, with a slightly insignificant p-value of 0,054. The difference for the
volatility of two-year expenses totals 0,046. This difference results in a p-value of 0,045, which does
meet the requirements of a significant result.
Since the volatility levels of both the two-year revenues and the two year-expenses where
substantially above those found in sample one, but the volatility of two-year earnings remained
nearly unchanged, it can be expected that the levels of correlation between two-year revenues and
the two-year expenses will also have increased. Examining the result for the correlation in the second
sample reveals that this is in fact the case. The sample start with a value which is 0,002 higher than
the year observation in sample 1. The decreasing pattern is also present, but the rapidness with
which this decrease is occurring is substantially less compared to sample one. The first half of the
sample ends with a correlation level of 0,990 for the year 1997. The mean of the correlation between
revenues and expenses for the years 1989 to 1997 is 0,994. In the second half this decrease is
continued, but does not go below 0,986, resulting in a mean of 0,988. The total difference between
the first and second half of the sample is 0,006. Comparing this difference using the paired sample ttest returns a p-value of 0,009 which is statistically significant.
Although the difference between the means of the two-year variables only results in a statistical
significant value for the volatility of two-year expenses and for the correlation between two-year
revenues and two-year expenses, sample 2 clearly shows that over time the volatility effects for two42
year earnings are less pronounced compared to sample 1. The non-significant values returned by the
model are likely to be caused by the fact that every sample only contains 6 year observations, which
substantially reduces the power of the paired sample t-test. The results however do not allow to
formulate a positive answer to the second hypothesis on the basis of the results of sample 2.
Sample 3
The third sample is examined in order to try and provide robustness to the observed results in
sample 1. As mentioned before this sample tries to establish this form of robustness by adding an
additional four years to the beginning of the sample, which positively influences the ability to depict
the temporal behavior of the variability’s.
Sample 3
Year
Vol
(Earn)
Vol (Rev)
Vol (Exp)
Corr
(Rev,Exp)
Year
Vol
(Earn)
Vol
(Rev)
Vol
(Exp)
Corr
(Rev,Exp)
1984
0,020
0,227
0,225
0,988
1997
0,025
0,196
0,189
0,977
1985
0,017
0,201
0,198
0,990
1998
0,023
0,170
0,165
0,977
1986
0,018
0,223
0,225
0,991
1999
0,023
0,165
0,161
0,971
1987
0,018
0,269
0,259
0,995
2000
0,023
0,166
0,164
0,970
1988
0,021
0,285
0,272
0,995
2001
0,025
0,171
0,169
0,965
1989
0,020
0,246
0,234
0,994
2002
0,024
0,171
0,171
0,972
1990
0,017
0,223
0,215
0,994
2003
0,023
0,185
0,184
0,976
1991
0,017
0,213
0,208
0,994
2004
0,025
0,202
0,197
0,975
1992
0,021
0,214
0,210
0,986
2005
0,027
0,194
0,187
0,969
1993
0,025
0,201
0,196
0,981
2006
0,028
0,174
0,164
0,966
1994
0,023
0,167
0,161
0,975
2007
0,025
0,177
0,167
0,974
1995
0,023
0,173
0,167
0,978
2008
0,025
0,182
0,174
0,973
1996
0,024
0,179
0,171
0,980
2009
0,029
0,182
0,175
0,970
Vol(Earnings)
Vol(Revenues)
Vol(Expenses)
Corr(Revenues,
Expenses)
Mean 1984 to 1996
0,020
0,217
0,211
0,988
Mean 1997 to 2009
0,025
0,180
0,174
0,972
Difference
0,005
-0,037
-0,037
-0,016
P-value difference
0,000
0,006
0,003
0,000
The values returned for the volatility in earnings are very similar to the ones obtained from sample 1.
There are only slight difference with a maximum of 0,002. Three out of four of the additional years at
the beginning of the sample are below 0,020 which has a downgrading impact on the mean of the
first half of the sample. This results in a total mean for the years 1984 to 1996 of 0,020. The second
half of sample 3 is also in line with sample 1 which is a pattern of a slight but steady increase in the
volatility of earnings. The fact that the final six years are all 0,001 or 0,002 above their counterparts
43
in sample 1 results in a mean for the second half which is also 0,001 higher and totals 0,025. The
difference between the two means of 0,005 results in a p-value of 0,000 which is highly significant.
The additional four years average around 0,230 for the volatility of revenues and around 0,225 for
the volatility of expenses. This is substantially higher than the average of the remaining years in the
first half the of the sample. Since the rest of the sample again resembles the values obtained in
sample 1 the mean for the years 1984 to 1996 exceeds the value of sample 1. The mean for the
volatility of revenues amounts to 0,217 and the mean for the volatility totals 0,211. The remainder of
the sample is again in line with the results of sample 1, reflecting in a mean for the second half which
is comparable to the one obtained in sample 1. For the volatility of revenues this mean is 0,180 which
results in a difference of 0,037. The p-value of this difference is 0,006 which indicates that there is a
statistical difference between the two means. The mean for the volatility of expenses in de the
second half of the sample amounts to 0,174, giving rise to a similar difference of 0,037. The
performed t-test on this difference returned a p-value of 0,003, which is again statistically significant.
The correlation between revenues and expenses follows the pattern that was observed with the
other three variables. Again the additional four years add substantial correlation levels that exceed
the average of the remaining years in the first half and as a result increase the mean. For the years
1984 to 1996 the mean amounts to 0,988. The year observations in the second half of the sample
mimic the ones in sample 1 which results in an identical mean of 0,972. The difference between the
two means is 0,016 and has a p-value of 0,000 meaning that there is a highly statistical difference
between the two means.
The third sample clearly shows its use in providing robustness to the temporal trend observed in
sample 1. The extra four years do not only strengthen the observed pattern but they also enhance
the statistical power of the results. With the decrease in the volatility of the underlying business
fundamentals and a statistical significant p-value for all four variables, it can be concluded that
increase in volatility is due to an increase in poor matching. As a result hypothesis 2 is supported.
Sample 4
The fourth sample can be used to provide robustness to the observed results in sample 2. Sample 2
holds the two-year specification for all variables which are calculated for every odd year that the
research period covers. In sample 4 a similar sample is calculated with two-year variable
specifications for every even year and added to the results of the sample 2. This results in a doubling
of the year observations which has a positive effect on the power of the test. The sample holds
12.645 firm-year observations, uses similar selection criteria as sample 2 and covers a research
period from 1989 to 2010.
44
Sample 4
Year
Vol (2y
Earn)
Vol (2y
Rev)
Vol (2y
Exp)
Corr (2y
Rev, 2y
Exp)
Year
Vol
(2y
Earn)
Vol
(2y
Rev)
Vol
(2y
Exp)
Corr
(2y
Rev,
2y
Exp)
1989
0,020
0,272
0,260
0,996
2000
0,023
0,214
0,205
0,986
1990
0,018
0,278
0,266
0,997
2001
0,024
0,231
0,222
0,986
1991
0,019
0,302
0,291
0,997
2002
0,023
0,221
0,214
0,984
1992
0,020
0,322
0,312
0,997
2003
0,021
0,217
0,213
0,990
1993
0,022
0,335
0,326
0,996
2004
0,021
0,229
0,224
0,990
1994
0,021
0,273
0,265
0,993
2005
0,024
0,239
0,232
0,990
1995
0,020
0,236
0,229
0,992
2006
0,023
0,229
0,221
0,989
1996
0,020
0,230
0,224
0,991
2007
0,023
0,231
0,219
0,988
1997
0,022
0,238
0,230
0,990
2008
0,023
0,245
0,233
0,989
1998
0,022
0,220
0,212
0,988
2009
0,026
0,247
0,235
0,988
1999
0,023
0,221
0,212
0,988
2010
0,027
0,238
0,226
0,985
Vol (two-year
Earnings)
Vol(two-year
Revenues)
Vol(two-year
Expenses)
Corr(two-year
Revenues, two-year
Expenses)
Mean 1989 to 1999
0,021
0,266
0,257
0,993
Mean 2000 to 2010
0,023
0,231
0,222
0,988
Difference
0,002
-0,035
-0,035
-0,005
P-value difference
0,001
0,036
0,026
0,002
From examining the obtained results for the volatility of two-year earnings it is clear that the even
years closely follow the pattern of the two-year observations of odd years. Since most of the even
year results have a value of 0,020 or slightly above, the mean of the first half of the sample is
increased with 0,001 and amounts to 0,021 for the years 1989 to 1999. During the second half of the
sample the level of volatility increases which was already noticed in sample 2. The even years
validate this observation with similar values. The second half of the sample is concluded with a
volatility of two-year earnings of 0,026 and 0,027 for the years 2009 and 2010, which clearly
illustrates the rising pattern. The total mean for the years 2000 to 2010 is equal to the mean of
sample 2 with 0,023. The difference between the two means is 0,002 which results in a significant pvalue of 0,001.
For the volatility of two-year revenues and two-year expenses the added two-year observations of
even years have decreasing effect on the total mean of the first half of the sample. This is due to the
fact that four out of five even years show a volatility level which is lower than the average of the odd
years. This results in a mean for the volatility of two-year revenues of 0,266 and a mean for the
volatility of two-year expenses of 0,257. In the second half of the sample the even years do not alter
the average of the odds years that was observed in sample 2. Since there is no deviation from the
45
average the means of the volatility in two-year revenues and two-year expenses for the second half
of the sample are identical to the ones in sample 2, which denote a value of 0,231 and 0,222. The
difference between the two means for the volatility of two-year revenues is 0,035 which results in a
significant p-value of 0,036. For the volatility of two-expenses the difference in means also totals
0,035. When performing a paired sample t-test on this difference a p-value of 0,026 is returned,
which again meets the requirements of a statistically significant result.
The decreasing pattern which is so clearly present in the correlation between two-year revenues and
two-year expenses for odd year observations is almost perfectly matched by the even year
observations. The mean for the first half of the sample is brought down by 0,001 to 0,993, whereas
the mean of the second half remains identical at 0,988. The difference of 0,005 has a p-value of
0,002, which leads to the conclusion that there is a significant difference between the means of the
first and the second half.
The additional years of two-year observations provide robustness to the idea that two-year variables
will show less pronounced effects for the volatility of earnings because the effects of poor matching
are resolved over time. For all four variables the difference between means decreased and with the
volatility in two-year earnings returning a statistically significant result, the conclusion is
strengthened.
6.3. Hypothesis 3: Persistence of earnings
The third observation that was extracted from the constructed model is that poor matching has a
decreasing effect on the persistence of earnings. The persistence of earnings is defined as the slope
coefficient from a current earnings on lagged earnings regression.
Expt = α + β1 Expt-1
The theory of the model shows that in a situation of poor matching this slope coefficient is
decreasing, having a negative impact on the persistence of earnings (Dichev and Tang 2008). Linked
to the decrease in the persistence of earnings is the observation that poor matching causes a
negative autocorrelation in earnings changes. This effect arises because in a situation of poor
matching noise is introduced to the revenues-expense relation, which is the negative
autocorrelation(Dichev and Tang 2008). As such it can be expected that a lower level of persistence
in earnings is accompanied by an increase of negative autocorrelation in earnings changes (Dichev
and Tang 2008). Since the change in earnings persistence as well as the change in negative
autocorrelation are just different sides of the same effect, both will be examined under the same
hypothesis.
The hypothesis stating that poor matching decreases the persistence of earnings is tested by using a
regressing of current earnings on the previous period earnings. The temporal behavior of the slope
coefficient of this regression will be examined in order to so if a decrease in persistence is in fact
observed. The autocorrelation in earnings change is depicted by the correlation between current
earnings change and past earnings change. For both the earnings persistence as well as the
autocorrelation in earnings changes one-year and two-year specifications will be calculated.
Robustness to these observations will be provided by re-running the test using sample 3 and 4. The
additional information for results on all four samples can be found in appendix 10.
46
Sample 1
Year
Persistence in
earnings
Autocorrelation
Year
Persistence
in earnings
Autocorrelation
1988
0,663
-0,169
1999
0,535
-0,233
1989
0,810
-0,095
2000
0,485
-0,258
1990
0,867
-0,067
2001
0,395
-0,303
1991
0,759
-0,121
2002
0,696
-0,152
1992
0,683
-0,159
2003
0,534
-0,233
1993
0,681
-0,160
2004
0,404
-0,298
1994
0,613
-0,194
2005
0,627
-0,187
1995
0,643
-0,179
2006
0,777
-0,112
1996
0,636
-0,182
2007
0,754
-0,123
1997
0,495
-0,253
2008
0,570
-0,215
1998
0,548
-0,226
2009
0,434
-0,283
Persistence in earnings
Autocorrelation
Mean 1988 to 1998
0,673
-0,164
Mean 1999 to 2009
0,565
-0,218
-0,108
-0,054
0,093
0,093
Difference
P-value difference
In the first half of the sample the persistence of earnings shows a clear downward trend, with the
values obtained for the year 1989 to 1991 being substantially higher than the years following. The
autocorrelation shows the reverse effect with a peak value in 1997 of -0,253. The mean of the
earnings persistence for the years 1988 to 1998 amounts to 0,673. The negative autocorrelation in
earnings changes for that same period results in a mean of -0,164. The coefficients of earnings
persistence for the second half of the sample start out by continuing the decreasing trend and sets a
bottom value of 0,395 for the year 2001. From that moment on large fluctuations appear for the
remaining years with values ranging from 0,404 to 0,777. The observations in the second half of the
sample result in a mean of 0,565 for the persistence in earnings and -0,218 for the correlation in
earnings changes. Performing a paired sample t-test on the difference between earnings persistence
means, which is -0,108, results in a p-value of 0,093, which cannot be considered statistically
significant. A comparable insignificant value is obtained when a t-test is conducted in the difference
between means of the autocorrelation in earnings changes.
Examining the statistical significance level of the predictor variable for the regression, which is one
period lagged earnings, shows a value of 0,000 for all years that the sample covers. Since there is
only one predictor variable and the smallest sample size is well above 400, this is a logical result. The
squared correlation coefficient (R2) returns values averaging 0,48 for the first eleven years and 0,26
for the remaining years. This implicates that the amount for which the outcome variable is accounted
for by the predictor variable has deteriorated substantially during the course of the sample.
47
From the earnings persistence coefficients obtained for sample 1 it is clear that there is decreasing
temporal trend visible in the period from 1989 to 2001. However, the large increase in persistence in
earnings for the years 2005 to 2007 makes it impossible to conclude on a statistical basis that in fact
poor matching has decreased the persistence of earnings. As a result the third hypothesis needs to
be rejected.
Sample 2
The second sample contains the two-year specification for the persistence in earnings and the
autocorrelation in earnings changes for every odd year between 1988 and 2009. The detailed
explanation on the calculation of the persistence in two-year earnings and the autocorrelation in
two-year earnings change can be found in appendix 3.
Sample 2
Year
Persistence in earnings
Autocorrelation
1989
0,742
-0,129
1991
0,796
-0,102
1993
0,743
-0,129
1995
0,588
-0,206
1997
0,652
-0,174
1999
0,561
-0,220
2001
0,513
-0,244
2003
0,636
-0,182
2005
0,408
-0,296
2007
0,702
-0,149
2009
0,459
-0,271
Persistence in earnings
Autocorrelation
Mean 1989 to 1997
0,704
-0,148
Mean 1999 to 2009
0,547
-0,227
-0,157
-0,079
0,022
0,023
Difference
P-value difference
The decreasing temporal trend observed in sample 1 is also present in the first half of the sample 2,
with the first three year observations averaging substantially above the rest. These values have a
lifting effect on the two-year earnings persistence mean for the first half of the sample, which results
in a value of 0,704. The mean for the autocorrelation in two-year earnings changes amounts to0,148. The decrease in year observations cause the second half of the sample to only display two
substantial fluctuations that deviate from the decreasing trend. Because of this the impact of the
years with relatively low levels of persistence in two-year earnings increases which results in a
downgrading of the mean for the second half of the sample. The two-year earnings persistence mean
for the years 1999 to 2009 is 0,547 which indicates a decrease in means of 0,157. The t-test on this
difference returns a significant p-value of 0,022. For the autocorrelation in two-year earnings
48
changes the mean of the second half of the sample amounts to -0,227. The difference of 0,079
between the first and the second mean results in a significant p-value of 0,023.
The second sample provides the results that are needed in order to able to conclude with significant
confidence that in fact the persistence in two-year earnings decreased due to poor matching. The
increase in negative autocorrelation in two-year earnings changes showed a similar significance level
which supports this conclusion. As a result a positive answer can be formulated with respect to the
third hypothesis. What is noteworthy however is that sample 2 with two-year specifications does not
validate the expectation that two-year variables will show less pronounced effects due to the fact
that the effects of poor matching resolve over time. To the contrary, the increase in mean
differences and the statistical significant levels accompany this result indicate that the effects
actually were more pronounced in sample 2.
Sample 3
As already indicated the third sample is used to provide support for the temporal effect by adding an
additional four years to the beginning of the research period and vacate from the stringent selection
criteria maintained in sample 1 and 2.
Sample 3
Year
Persistence in
earnings
Autocorrelation
Year
Persistence
in earnings
Autocorrelation
1984
0,801
-0,100
1997
0,526
-0,237
1985
0,832
-0,084
1998
0,531
-0,235
1986
0,763
-0,119
1999
0,515
-0,243
1987
0,873
-0,064
2000
0,431
-0,285
1988
0,724
-0,138
2001
0,471
-0,265
1989
0,837
-0,082
2002
0,690
-0,155
1990
0,906
-0,047
2003
0,587
-0,207
1991
0,775
-0,113
2004
0,478
-0,261
1992
0,664
-0,168
2005
0,607
-0,197
1993
0,663
-0,169
2006
0,757
-0,122
1994
0,605
-0,198
2007
0,711
-0,145
1995
0,627
-0,187
2008
0,562
-0,219
1996
0,588
-0,206
2009
0,400
-0,300
Persistence in earnings
Autocorrelation
Mean 1984 to 1996
0,743
-0,129
Mean 1997 to 2009
0,559
-0,221
-0,184
-0,092
0,002
0,002
Difference
P-value difference
49
The coefficients of earnings persistence for the first four years of sample 3 depict a high level of
persistence for the years 1984 to 1987 averaging above 0,800. These high values translate into an
increase in the mean for the first half of the sample. Examining the years 1984 to 1996 results in a
mean for the persistence in earnings of 0,743 and a mean for the autocorrelation in earnings changes
of -0,129. The second half of the sample is in line with the results observed in sample 1, resulting in
mean values for the years 1997 to 2009 which deviate just slightly from the values obtained in
sample 1. The persistence in earnings returns a mean of 0,559 and the mean for the autocorrelation
in earnings changes equals -0,221. Comparing the difference between the earnings persistence
means of the first and the second half of the sample, which amounts to 0,184, results in a statistically
significant value of 0,002. The difference between means for the autocorrelation in earnings changes
also returns a significant p-value.
Sample 3 succeeds in providing support for the conclusion that the temporal behavior of the
coefficients depict a decrease in earnings persistence and an increase of negative autocorrelation in
earnings changes. The significant p-values for both variables support the third hypothesis that during
the course of the research period persistence in earnings has decreased due to poor matching.
Sample 4
The persistence in two-year earnings and the autocorrelation in two-year earnings changes will also
be tested using the fourth sample. Since the results obtained from the second sample could not
strengthen the believe that two-year variables show less pronounced effects due to the fact that the
effects of poor matching resolve over time, it will be interesting to see whether adding the two-year
specification of even years has any changing impact.
Sample 4
Year
Persistence in
earnings
Autocorrelation
Year
Persistence
in earnings
Autocorrelation
1989
0,742
-0,129
2000
0,593
-0,204
1990
0,893
-0,054
2001
0,500
-0,250
1991
0,797
-0,102
2002
0,272
-0,364
1992
0,641
-0,180
2003
0,633
-0,184
1993
0,726
-0,137
2004
0,483
-0,259
1994
0,682
-0,159
2005
0,364
-0,318
1995
0,554
-0,223
2006
0,625
-0,188
1996
0,606
-0,197
2007
0,698
-0,151
1997
0,647
-0,177
2008
0,709
-0,146
1998
0,612
-0,194
2009
0,460
-0,270
1999
0,552
-0,224
2010
0,389
-0,306
50
Persistence in earnings
Autocorrelation
Mean 1989 to 1999
0,677
-0,161
Mean 2000 to 2010
0,521
-0,240
-0,156
-0,079
0,028
0,027
Difference
P-value difference
The temporal behavior of the coefficients of earnings persistence confirm that in the first half of the
sample there is a decreasing pattern, with the ending years being almost 0,200 lower compared to
the years in beginning. The first half of the sample results in a mean of 0,677, which is close to value
of the mean obtained in sample 1. The year 2002 in the second half of the sample has a persistence
in earnings value which is substantially lower than the rest of the value, but also compared to the
value obtained for the same year in the other samples. This decrease has a significant impact on the
mean lowering it to 0,521. This results in a difference between means of 0,156 which is in between
the values obtained for sample 1 and 3. The paired sample t-test returns a significant p-value 0,028.
The difference in means for the autocorrelation in earnings changes amounts to 0,079 and results in
a p-value of 0,027, which is again statistically significant.
Although sample four supports the observation that persistence in earnings has decreased due to
poor matching, the large deviations in the second half of the sample again make it difficult to
conclude that the two-year specifications of the earnings persistence and autocorrelation in earnings
changes show a less pronounced effect.
6.4. Hypothesis 4: Effects of longer time horizon
The fourth observation that resulted from the model is that in the long run the effects of poor
matching are resolved. The driving force behind this is the self-correcting nature of accounting, which
implies that over a longer period of time all mismatching errors will get resolved (Dichev and Tang
2008). By using the assumption that all mismatched expenses will get resolved within one year, it can
be shown that a five year period has relatively less mismatching because the three years in the
middle are already resolved. Using the findings of Dichev and Tang (2008) as support it can expected
that stretching the time horizon eased the effect of poor mismatching, which is the fourth hypothesis
of this study.
In order to test whether the effects of poor matching are less pronounced for longer time horizons,
the test results for two-year specifications of all variables are compared to those of the one-year
variables. The hypotheses that tested two-year specifications of the variables are earnings volatility
and earnings persistence.
The results from the previous two hypothesis show a pattern which is not fully in line with the
expectations. The two-year specifications for the volatility in earnings in sample 2 and 4 return values
which are less pronounced compared to the two-year volatility values that were obtained in sample
1. This supports the idea the effects of poor matching are eases over time. However, the two-year
specifications for the earnings persistence and autocorrelation in earnings changes depict a different
pattern. Both sample 2 and 4 show a mean difference for the persistence in two-year earnings and
for the autocorrelation in two-year earnings changes which is actually more pronounced than the
results for the one-year specification of the variables. As a result of this contradiction it is not
51
possible to formulate a positive answer to the fourth hypothesis and conclude that stretching the
time horizons indeed eases the effects of poor mismatching.
6.5. Additional tests
The results from the main tests show very strong evidence that poor matching indeed caused a
decreasing effect on the contemporaneous correlation between revenues and expenses, increased
the volatility of earnings and decreased the earnings persistence amongst European companies
during the last two and a half decades. During this period the composition of countries that were
amongst the top 1000 in total assets changed in two different aspects. The first is the classification of
the industry to which the companies belong and the second is the country from which the firms
originate. Changes in industry composition could influence the results if companies operating in
certain industries that tend to have more volatility and less earnings persistence take on a more
dominant role in the sample during the research period. The country composition could have an
impact of the results due to differences that might exist in national account regulations. Therefore
the following two additional tests will investigate the effect that changing industry composition and
changing country composition in sample 1 had on the results that were obtained while testing
hypothesis one.
Changing industry composition
Examining the changes in the industry composition is done by using the Standard Industrial
Classification Code list (SIC Code). Companies in the first and in the final sample year of the research
period, which are 1988 and 2009 are listed and weighted, for which the results can be found in
appendix 11. Examining these results shows that some industries experienced large changes in
composition. Both the companies operating in the mining and services industry have doubled their
presence and also the composition of the transportation, communications, electric, gas and sanitary
services has increased substantially. Manufacturing on the other hand lost 12 percent point and
public administration completely disappeared in the final sample.
52
Changing industry composition
Industry classification
Beginning sample year
1988
Ending sample year
2009
Count
Percentage
Count
Percentage
1
0,20
4
0,59
Mining
11
2,24
30
4,39
Construction
36
7,32
48
7,03
286
58,13
317
46,41
Transportation, Communications, Electric, Gas, and
Sanitary Service
72
14,63
126
18,45
Wholesale trade
24
4,88
32
4,69
Retail trade
33
6,70
46
6,73
Services
25
5,08
80
11,71
4
0,81
0
0
Agriculture, Forestry, and Fishing
Manufacturing
Public administration
For the test two subsamples will be constructed, one which comprises of the companies in the
industries that have relatively increased during the research period and one which comprises of the
companies in the industries that have relatively decreased during the research period. The increasing
subsample accounts for 28,85% of the beginning sample in the year 1988 and 41,87% of the ending
sample in 2009. The decreasing subsample on the other hand accounts for 71,15% of the beginning
sample and 58,13% of the ending sample. If the changing industry composition fully accounts for the
results that were obtained while testing the first hypothesis, then there should be substantial
differences in average results across the two subsamples and little temporal variation in the results
within the sample (Dichev and Tang, 2008).
Examining the results, which can be found in appendix 11, clearly shows that the decreasing
subsample follows a similar pattern as the main test. Without going into detail on the behavior of
every single coefficient, it is clear that there is a substantial temporal decline in the coefficient on
current expenses which indicates a decreasing correlation between revenues and expenses. This is
supported by the clear rising coefficients on future expenses, revealing that expenses indeed are
being scattered increasingly across periods. The results of the subsample of increasing industries
however does not show the same pattern as the main test. There is hardly any temporal movement
in any of the three expense coefficients. The coefficient on current expenses decreases only 0,003,
whereas the coefficient on one-year forward expenses remains the same and does not show any
movement. This indicates that the decrease in the economic relation of advancing expenses to earn
revenues is largely influenced by the firms operating in the industries that are included in the
decreasing subsample.
Changing country composition
Besides the impact of changes in the composition of industries in the sample, there is also the
possibility that the results and patterns obtained in the main test are partially accounted for by
changes in country composition. Examining the beginning and ending sample reveals that especially
Germany and Great-Britain lost a substantial share in the composition, whereas Spain and Ireland
53
more than doubled. The ending sample also compromises of three country that aren’t in the
beginning sample, which are Greece, Luxembourg and Portugal.
Changing country composition
Country
Beginning sample year 1988
Ending sample year 2009
Count
Percentage
Count
Percentage
Austria
6
1,22
12
1,76
Belgium
20
4,07
18
2,64
Germany
105
21,34
88
12,88
Denmark
13
2,64
22
3,22
Spain
20
4,07
46
6,73
Finland
26
5,28
29
4,25
France
93
18,90
127
18,59
120
24,39
140
20,50
3
0,61
14
2,05
Italy
19
3,86
58
8,49
Netherlands
29
5,89
44
6,44
Sweden
38
7,72
37
5,42
Greece
0
0
26
3,81
Luxembourg
0
0
8
1,17
Portugal
0
0
14
2,05
Great-Brittan
Ireland
Testing the effects of the changes in country composition will be conducted in a similar way as to
with the industry composition, by constructing a set of subsamples where one subsample comprises
of firms from countries that have relatively decreased during the research period and the other
holding the firms of the countries that have relatively increased. Examining their relative importance
reveals that the decreasing subsample accounts for 81,7% of the beginning sample in 1988 and for
64.28% of the ending sample. The increasing subsample on the other hand accounts for 18,3% of the
beginning sample and for 35,72% of the ending sample.
The results of the industry composition tests can be found in appendix 12 and shows a decreasing
pattern which is largely in line with the observations under hypothesis one. The decreasing
subsample which holds the three European superpowers Germany, France and Great-Brittan shows a
slightly stronger decrease with a mean difference for the coefficient on current expenses of 0,018
and an increase on one-year forward expenses of 0,013. The subsample that hold the firms of the
countries that have relatively increased in the sample composition reveal a somewhat less
pronounced decrease in the coefficient on current expenses, but a similar increase in the coefficient
on future expenses. The fact that there is no substantial difference in average results across the two
subsamples and substantial temporal variation in the results within the sample indicates that the
observed decrease in the contemporaneous correlation between revenues and earnings cannot be
fully accounted for by the changes in country composition.
54
6.6. Summary
The results of the four hypotheses show that there is enough supporting evidence to conclude that
there is in fact a declining trend in the contemporaneous correlation between revenues and
expenses and that a substantial part of the expenses is scattered to a future period. A resulting
temporal decrease in earnings volatility is also borne out of the data with the confidence that this
decline is not caused by a change in the volatility of the underlying business fundamentals. The
decrease in the persistence of earnings and the increase in negative autocorrelation also require
supporting evidence, but the obtained results depict a clear trend which validates a conclusion that
earnings persistence has declined over time. The two-year specifications are not capable of providing
the evidence needed to conclude that the effects of poor matching are eased over longer-time
horizons. The additional tests provide evidence that the industry composition to an extent accounts
for the results obtained for the main hypotheses.
55
7. Analysis
7.1. Hypothesis 1: Revenues-expense relation
The results from the regression of revenues on one-year back, present and one-year forward
expenses that was performed in order to test whether poor matching decreases the
contemporaneous correlation between revenues and expenses showed mixed results. The temporal
behavior of the expense coefficients in sample 1 indicate that there is not enough evidence to
conclude that the mean of the second half of the sample is significantly lower than the mean of the
first half of the sample. However, the absolute values of the coefficients and the difference in means
clearly indicate that a decreasing pattern is actually occurring. This presumption is confirmed when
examining the results of sample 3, which has four additional year-observations and was added as a
robustness test. Sample 3 clearly shows a similar decreasing pattern and the coefficients values of
the added years are enough to lift the p-value of the difference between means from an insignificant
to a significant level.
The obtained results are mostly in line with the results of Dichev and Tang (2008) and Donelson,
Jennings and McInnis (2010). Both studies find significant evidence that there is a decline in the
correlation between current period revenues and current period expenses. In the study of Dichev
and Tang the present expense coefficients start out at a high and consistent level with values slightly
above 1. During the second half of their sample the average of this coefficient decreases, resulting in
a mean difference of -0,149. Donelson et al. (2010) find a similar pattern over a slightly longer
research period with a total mean difference of -0,137. Both mean differences are highly significant
with p-values below the 0,01 level. The results obtained in this study resemble the findings of Dichev
and Tang (2008) and Donelson et al. (2010), however the resulting differences are more modest .
Sample 1 returns a mean difference of -0,015. Sample 3 returns a somewhat greater mean difference
with an absolute value of -0,021.
There are two parts of the hypothesis for which the results of study do not provide a conclusive
answer. The first is that both the studies of Dichev and Tang (2008) and Donelson et al. (2010) find
highly significant evidence that both the coefficients on one-year back and one-year forward
expenses have increased during the research period, indicating that expenses are increasingly being
scattered across period in the future as well as in the past. Examining the results from both sample 1
and 3, however does show an increase in the mean difference for the coefficient on future expenses,
but does not show a similar effect on the side of the past expenses. The most likely cause for this
absence is the influence that the year-observation of 2009 has on the average of the second half of
the sample. Without this extreme value which is more than double the size of the other coefficients,
there would have been an increase in mean difference which is comparable to the other studies.
The second part that the results of the first hypothesis struggle with is providing significance levels
for sample 1 which are high enough to conclude with statistical certainty that there is in fact a
decrease in the contemporaneous correlation between revenues and expenses. Although the pattern
is clearly visible by examining the absolute values of the coefficients and the mean differences, the
resulting p-values are insignificant. This problem is largely eliminated when adding an additional four
years to the sample, which is the case in sample 3. As a result of this it can be concluded that the
research period of 22 year-observations is slightly too small to provide solid conclusive answers to
the hypothesis. The studies of Dichev and Tang (2008) and Donelson et al. (2010) do not encounter
56
this problem, because the information available on U.S. firms allows them to use a research period
which stretches from 1967 to 2003 and 2005. Regarding this aspect, the added years in sample 3
clearly show their value, since they did not substantially alter the findings, but provided the needed
additional power to the test.
7.2. Hypothesis 2: Volatility of earnings
The second hypothesis examined whether the decline in contemporaneous association of revenues
and expenses and the increase in scattered expenses to other time periods results in an increase of
the volatility in earnings. For this test both the one-year and the two-year specifications of the
volatility variables were examined, implicating that all four samples were tested. Both samples 1 and
2, which fulfill the same selection criteria as the samples used in the studies of Dichev and Tang
(2008) and Donelson et al. (2010), show volatility levels which are constant at the beginning of the
sample around the value 0,020 and steadily increase as time progresses. Sample 1 returns a
difference in means which is highly statistical and confirms that the volatility of earnings increased.
Although the difference in means is equal in sample 2, the corresponding p-value is slightly
insignificant due to the modest amount of year-observations available in this sample. Similar
patterns are found in the robustness samples 3 and 4 which both provide statistically significant
evidence that supports the second hypothesis.
Comparing the result for the second hypothesis with the findings in the studies of Dichev and Tang
(2008) and Donelson et al. (2010) shows this study obtains similar findings but on a more modest
level. Both studies reveal that earnings volatility has substantially increased during their research
period, but they differ on the value of the mean difference for the volatility of earnings, which is used
to depict this increase. Examining the results of Dichev and Tang (2008) shows that they obtain an
increase in mean difference of 0,007. When Donelson et al. (2010) perform the same test on their
sample, they find a mean difference of 0,014 which is double the size compared to Dichev and Tang
(2008). The four samples tested in this study all confirm the temporal trend of an increase in volatility
of earnings, however the mean difference is relatively low ranging from 0,002 to 0,005. This can be
explained by looking at the absolute values of the volatility of earnings in the study of Dichev and
Tang (2008), which depict substantially lower values in the period before 1988. The volatility levels
for the year-observations which overlap the research period of this study are very similar.
The results from the four samples also showed that the increase in earnings volatility is not caused by
an increase in volatility levels of the underlying economic fundamentals. Although the volatility of
revenues and expenses show a somewhat stronger declining pattern compared to the results found
in the study of Dichev and Tang (2008), the values obtained for the correlation between revenues
and expenses neutralizes most of this, leaving the conclusion that the increase of volatility is due to
an increase in poor matching intact. This observation has important implications, because evidence
presented by Dichev and Tang (2009) shows that an increase in earnings volatility reduces earnings
predictability. Analysts therefore can benefit by taking the increasing trend in earnings volatility into
consideration during the preparation of their forecasts.
57
7.3. Hypothesis 3: Persistence of earnings
The declining contemporaneous relation between revenues and expenses also gave rise to the
expectation that over time earnings persistence has declined and that the autocorrelation in earnings
changes has become more negative. The third hypothesis examined this by looking at the temporal
behavior of the slope coefficients from the regression of current earnings on previous period
earnings. The slope coefficients of sample 1 clearly show a decreasing pattern for the persistence in
earnings which results in a substantially lower absolute mean difference. Examining this difference
using a statistical model however produces a significance level which is slightly above the five
percent confidence interval. Sample 3 with its additional years does not encounter this problem and
depicts a mean difference which is somewhat higher and statistically significant. The two-year
variables in the samples 2 and 4 both confirm at a significant level that there is declining trend in
earnings persistence and the autocorrelation in earnings changes.
The results obtained for the third hypothesis resemble the findings of both Dichev and Tang (2008)
and Donelson et al. (2010). The results from the study of Dichev and Tang (2008) reveal a steady
downward shift in both earnings persistence and autocorrelation. The authors find a decrease in
mean difference for the persistence in earnings of -0,150 and an increase in the mean difference for
the negative autocorrelation of -0,215. Both of these differences are highly statistically significant at
the 0,001 level. Donelson et al. (2010) only examined the changes over time for the persistence in
earnings and did not perform any tests on the autocorrelation in earnings changes. Although they do
not elaborate on their decision to omit this test, it is likely that they questioned the extra value since
a negative autocorrelation in earnings changes logically results when the persistence in earnings
decreases. Their obtained mean difference for the earnings persistence amounts to -0,125 which is
again highly statically significant. The mean differences obtained for the four samples tested in this
study range from -0,108 to -0,184. Both samples 2 and 4 two-year closely mimic the findings of
Dichev and Tang (2008) with mean difference just above -0,150.
Although the absolute results from the regression of current earnings on previous period earnings
tempt to conclude that the third hypothesis needs to be confirmed, the first sample returned a mean
difference which is slightly insignificant. As already explained this is mainly due to the limited
numbers of year-observations in the research period for this sample. The absolute values of the
coefficients depict a trend which is clearly decreasing and sample 3 clearly indicates that only a few
additional years is already sufficient to shift the p-value of the mean difference to a significant level.
It can therefore be expected that if the availability of information would allow to stretch the research
period even further, this would provide additional power to the result without altering the findings
substantially.
7.4. Hypothesis 4: Effects of longer time horizon
The fourth hypothesis examined whether the effects of poor matching are resolved in the long run by
comparing the two-year results of the earnings volatility, the autocorrelation in earnings changes and
the earnings persistence from the second and third hypothesis with the results from the one-year
variables. The results for the second hypothesis were in line with the expectations that two-year
specifications of the volatility variables would show a less pronounced effect. This supports the idea
the effects of poor matching are eased over time. However, the two-year specifications for the
earnings persistence and autocorrelation in earnings changes returned coefficient levels which were
58
higher than the coefficient levels for the one-year variables. As a result the observations did not
provide the evidence needed to validate the hypothesis.
Comparing these observation to the findings of Dichev and Tang (2008) shows that the results are
the exact opposite of each other. The two-year specifications of the earnings persistence variables in
the study of Dichev and Tang (2008) clearly satisfy the expectations, whereas the two-year
specifications for volatility struggle to provide the evidence needed. The reason that the two-year
specifications do not always result in effects which are less pronounced, might be because two years
are not enough to accurately simulate a longer horizon. Dichev and Tang (2008) tried to examine this
proposition by creating four-year specifications, but had to abandon these specifications because it
led to unacceptable loss in the comparability of the data. The available data in this study also does
not allow the creation of variables with higher year specifications, because three-year variables
would already require fourteen preceding years in order to calculate one year observation.
7.5. Additional tests
Next to the testing of the hypotheses, this study also looked at the possible effects that changes in
industry composition and changes in country composition had on the results. The influence of
industry and country composition were tested separately but in a similar way by creating a set of
subsamples in which one sample holds the industries or countries that have increasing firm count
over time and the other holds the industries or countries that have decreasing firm count over time.
Examining the results of the country composition indicates that the two subsample show a temporal
trend which is similar to the results in the main samples. Both the increasing as well as the
decreasing subsamples depict a negative trend in the coefficients on current expenses and a positive
trend in the coefficients on future expenses. In the decreasing subsample the declining trend is
slightly more pronounced, but overall the results show a strong resemblance. The fact that both
subsamples demonstrate similar results indicates that the changing country composition does not
account for the documented result in the main hypotheses. This means that the differences between
countries and the freedom that they have to maintain a level of national sovereignty when it comes
to setting accounting standards and interpreting standards set by the IASB do not change the results
obtained in this study.
The results for the industry composition reveal a completely different picture compared to the
country composition. The decreasing subsample shows a temporal trend which is substantially
stronger than the results found for sample 1 and virtually identical to the results obtained from
sample 3. The subsample comprising of the industries that have increased in firm count on the other
hand show virtually no change in the coefficients on past, current and future expenses. This means
that there is a substantial difference in average results across the two subsamples, which indicates
that the industry composition to a large extent does account for the results obtained for the main
hypotheses. Since the industries present in the increasing subsample show no signs of a decrease in
contemporaneous correlation between revenues and expenses it can be concluded that the results in
main tests are largely caused by firms operating in the industries present in the decreasing
subsample, which are construction, manufacturing, wholesale trade and public administration. With
no presence in the sample of 2009 and only four firm counts in the sample of 1988 it is likely that
that public administration did not have a significant role in the formation of the results.
59
Although the study of Dichev and Tang (2008) used a different industry classification and did not find
any evidence that the changing industry composition had an effect on the results of the main tests,
the study does provide some insights as to what could cause this pattern. The study finds clear
evidence that there is a pronounced increase in the behavior of past expense coefficients and links
this with evidence of increased conservatism over time presented by Givoly and Hayn (2000). This
effect however is not present in the test results for the four samples used in this research. Despite of
this absence, there is a substantial increase in the temporal behavior for the coefficients on future
expenses for the three industries present in the decreasing subsample. Possible causes for this
increase could be underprovisioning of current expenses which require a catch-up in future periods.
Examples of underprovisioning could for instance be understating current depreciation expenses or
underprovisioning current warranty expenses or bad debt expenses.
Notwithstanding the silent evidence that the three dominant industries in the decreasing subsample
account for most of the results obtained in the main tests, the question that can be put forward is
whether the division into two subsamples based on decreasing or increasing firm count can be used
to draw conclusions. Countries and industries with different characteristics are present in the same
subsample. Combining Great-Britain and France means that the sample holds countries with a history
based on both common law as well as civil law. This obviously has implications for the role and
positioning of national accounting regulations. The same reasoning goes for the industry composition
where it is obvious that firms in the construction sector are faced with completely different
accounting questions and challenges compared to the firms operating in wholesale trade. Although it
is a valid question the method used in this study, which is similar to the method used by Dichev and
Tang (2008), is used because given the limited availability of data in the early years of the research
period, research on a more individual level would possibly infringe the statistical assumption of
normality. Looking at both composition, only four countries as well as four companies fulfill the lower
boundary of 30 observations. As a result of this a sample composition was chosen which can be used
to signal whether there are large differences between countries and firms which might interest
future research into this field.
7.6 Summary
Analyzing the obtained results shows that there is a large degree of similarity with previous research
conducted by Dichev and Tang (2008) and Donelson et al. (2010). The first three hypotheses depict a
trend which is similar, but with more modest absolute result values. A possible cause for this could
be that the research period is substantially smaller. A conclusive answer to the hypothesis which
examined whether the effects of poor matching are resolved in the long run cannot be given,
possibly because the two-year specification of the variables are not enough to accurately simulate a
longer horizon. The results for the additional tests on the possible effects that changes in industry
composition and changes in country composition had on the results clearly show that industry
composition to some extent accounts for the results obtained in the main test. These findings are
unique and not in line with the evidence presented by Dichev and Tang (2008).
60
8. Conclusion
This study focused on the idea that an increase in poor matching amongst European firms over the
last twenty year had a decreasing impact on the contemporaneous correlation between revenues
and expenses. From the theory of perfect matching a model was created by Dichev and Tang (2008)
to depict the effects of poor matching. The model indicates that poor matching acts as noise in the
economic relation of advancing expenses to earn revenues. As a result of this the mismatched
expenses have an increasing effect on the volatility of earnings and a decreasing effect on the
persistence of earnings. Linked to the decrease of earnings persistence is an increase in negative
autocorrelation in earnings changes. Finally the model also gives rise to the idea that for longerhorizon definitions of earnings the effects of poor matching will be less pronounced. These ideas
suggest a decline in earnings quality which could have serious implications for several actors in the
financial markets, since earnings is considered to be the single most important output of the
accounting system (Graham, Harvey, and Rajgopal 2005).
A series of tests using samples comprising the top 1000 firms from 15 European countries were
performed to observe the temporal change in the quality of matching. The results of these tests
show that there is enough supporting evidence to conclude that there is in fact a declining trend in
the contemporaneous correlation between revenues and expenses and that a substantial part of the
expenses is scattered to a future period. A resulting temporal decrease in earnings volatility is also
borne out of the data with the confidence that this decline is not caused by a change in the volatility
of the underlying business fundamentals. The decrease in the persistence of earnings and the
increase in negative autocorrelation also require supporting evidence, but the obtained results depict
a clear trend which validates a conclusion that earnings persistence has declined over time. The twoyear specifications are not capable of providing the evidence needed to conclude that the effects of
poor matching are eased over longer-time horizons.
Although the results support most of the hypotheses there are some limitations to this study. The
first and foremost is that the research period is limited to a total of 22 year observations for the main
samples due to the availability of data. The stringent criteria derived from the study of Dichev and
Tang (2008) for selecting the companies which make up the samples in combination with the fact
that Thomson One Banker does not hold data on accounting variables for European companies
before the year 1980 results in a research period which has its earliest year in 1988. Because of this
the first sample struggles on several occasions to obtain a statistical significant value for the
difference in means, making it difficult to conclude that the hypothesis can be supported. From the
use of the other samples it can be concluded that a research period of 22 years is just on the edge of
being large enough to perform a solid time-series research. A larger research period would provide
the additional power that some of the findings lack.
Another limitation of this study is that it is unable to appoint what is causing the declining
contemporaneous correlation between revenues and expenses. By dividing total expenses into
separate expense line items the study of Donelson et al. (2010) presents evidence that for U.S. based
companies the changes in the contemporaneous correlation are primarily caused by an increase in
the incidence of large special items. An attempt in finding a comparable categorical division of total
expense using Thomson One Banker turned out to be infeasible. Also the starting point of this
declining trend remains uncertain after this study. Based on previous research it is expected that a
changing view on what needs to be considered the most appropriate accounting approach acts an
61
important determinant in the decreasing quality of matching. Based on this the Framework for the
Preparation and Presentation of Financial Statements issued by the International Accounting
Standards Committee in 1989, which commends the use of the balance sheet approach in favor of
the income statement approach, was regarded as a starting point. However, because the availability
of data does not allow this research to investigate the temporal trend several years before 1989, it is
impossible to conclude that the conceptual framework initiated this trend.
Despite these limitation this study does document a serious declining pattern in the economic
relation of advancing expenses to earn revenues and presents silent evidence that this trend is
dominant in firms operating in a selective group of industries. These findings might induce future
research especially in the area of the consequences that a decline in earnings quality and an increase
in earnings volatility has for parties which, for their decision making process, largely depend on the
accounting information presented by the company.
62
Literature
Altman, E.I., 1968. Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy.
Journal of Finance 23: 589-609.
Berry, W.D., Understanding regression assumptions. Sage university paper on quantitative
applications in the social siences, 07-050. Beverly Hills, CA:Sage.
Bindenga, A.J., Het dilemma van vermogen en resultaat, Rede uitgesproken bij de openbare
aanvaarding van het ambt van buitengewoon hoogleraar in “Financial Accounting” aan Nijenrode,
Universiteit voor Bedrijfskunde.
Bullen, H.G., and Crooks, K., 2005. Revisiting the Concepts: A New Conceptual Framework Project.
Norwalk, CT: Financial Accounting Standards Board.
Cauwenberge, P. van, and Beelde, I. de, 2007. On the IASB Comprehensive Income Project: An
Analysis of the Case for Dual Income Display. Abacus 43 (1): 1-26.
Dichev, I., 2008. On the Balance Sheet-Based Model of Financial Reporting. Accounting Horizons22
(4): 453-470.
Dichev, I., and Tang, V.W., 2008. Matching the changing properties of accounting earnings over the
last 40 years. The Accounting Review 83 (6): 1425-1460.
Dichev, I., and Tang, V.W., 2009. Earnings volatility and earnings predictability. Journal of Accounting
and Economics 47: 160-181.
Donelson, D.C., Jennings, R., and McInnis, J., 2010. Changes over Time in the Revenue-Expense
Relation: Accounting or Economics?, working paper.
Evans, T.G., 2003. Accounting Theory: Contemporary Accounting Issues, South-Western.
Field, A., 2009. Discovering statistics using SPSS, 2009 SAGE Publications.
Financial Accounting Standards Board (FASB 2003) 2003. Proposal for a principles-based approach to
U.S. standard setting, FASB No. 1125-001, Financial Accounting Standards Board, Norwalk, CO,
October.
Financial Accounting Standards Board (FASB 2004a) 2004. The Project to Revisit the Conceptual
Framework, FASB Report No. 378, Financial Accounting Standards Board, Norwalk, CO, December.
Financial Accounting Standards Board (FASB 2004b) 2004. Understanding the Conceptual Framework,
FASB Report No. 378, Financial Accounting Standards Board, Norwalk, CO, December.
Financial Accounting Standards Board (FASB 2006), 2006. Preliminary views: Conceptual framework
for financial reporting. Financial Accounting Series 1260-001.
Fox, A., Grinyer, J., and Russell, A., 2003. Incompatible theoretical bases underlying accounting
standards. Journal of International Accounting, Auditing & Taxation 12: 169-184.
63
Freeman, R.N., Ohlson, J.A., and Penman, S.H., 1982. Book rate-of-return and prediction of earnings
changes: An empirical investigation. Journal of Accounting Research 20 (2): 639-653.
Gaspar, J., and Massa, M., 2006. Idiosyncratic volatility and product market competition. Journal of
Business 79 (6): 3125-2152.
Givoly, D., Hayn, C., 2000. The changing time-series properties of earnings, cash flows and accruals:
Has Financial reporting become more conservative? Journal of Accounting and Economics 29: 287320.
Gore, R., and Zimmerman, D., 2007. Building the Foundations of Financial Reporting: The Conceptual
Framework. The CPA Journal, 2007.
Graham, J.R., Harvey, C.R., and Rajgopal, S., 2005. The economic implications of corporate financial
reporting. Journal of Accounting and Economics 40: 3-73.
Hicks, J.R., 1946, Value and Capital, 2nd Edition: Clarendon Press, 1946.
Hirshleifer, D. 2001. Investor psychology and asset pricing, The Journal of Finance 56 (4): 1533-1597.
Hoogendoorn, M.N., 2003. Tweeledig monisme en fair value accounting: een analyse van de
opvattingen van Bindinga. Maatschappelijk aanvaardbaar, Opstellenbundel aangeboden aan prof.dr.
A.J. Bindenga bij zijn afscheid als hoogleraar Accountancy aan de Faculteit der Economische
Wetenschappen van de Erasmus Universiteit Rotterdam op 24 oktober 2003: 81-95.
International Accounting Standards Board (IASB 2004), 2004. Membership of IASB and FASB,
International Working Group on Performance Reporting Announced, International Accounting
Standards Committee Foundation.
International Accounting Standards Board (IASB 2005), 2005. Performance Reporting (Project Update
December 2005), International Accounting Standards Committee Foundation.
International Accounting Standards Committee (IASB 1989), 1989. Framework for the Preparation
and Presentation of Financial Statements,International Accounting Standards Committee.
Joint International Working Group on Performance Reporting , 2005. Categorization in a Statement
of Earnings and Comprehensive Income, International Accounting Standards Committee Foundation.
Kee, H.L., 2009. The coefficient of the whole is an average of the coefficients of its parts: A special
case of restricted least squares. Economics Letters 104: 136-139.
Klein, A., and Marquardt, C.A., 2006. Fundamentals of accounting losses. The Accounting Review 81
(1): 179-206.
Kvifte, S.S., 2008. Revisiting the Concepts: Time to Challenge the Asset-Liability View. Australian
Accounting Review 18 (44): 81-92.
Miller, P.B.W., and Bahnson, P.R., 2010. Continuing the Normative Dialog: Illuminating the
Asset/Liability Theory. Accounting Horizons 24 (3): 419-440.
64
Paton, W.A., and Littleton, A.C., 1940. An Introduction to Corporate Accounting Standards. Sarasota,
FL: American Accounting Association.
Robinson, L., 1991. The Time Has Come to Report Comprehensive Income. Accounting Horizons June
1991.
Storey, R.K., and Storey, S., 1998. The Framework of Financial Accounting Concepts and Standards.
Norwalk, CT: FASB.
65
Appendix 1.Empirical studies overview
Authors
Object of study
Sample
Methodology
Outcome
Dichev and Tang 2008
Investigate the effects of poor
matching on accounting
earnings
Sample of the 1000 largest US
firms in the period 1967 to 2003
with data available on assets,
revenues, earnings before
extraordinary items and
preceding 9 years of revenues
and earnings
Time-series framework,
regression
Declining contemporaneous
correlation between revenues
and expenses, increased
volatility of earnings, declining
persistence of earnings.
Dichev and Tang 2009
The effects of earnings volatility
on earnings predictability
US firms with a min of $100
million in total assets and with
data on assets, earnings, cash
flow from operations and
preceding 4 years of earnings
and cash flow from operations
for the period between 1984
and 2004
regression
Consideration of earnings
volatility can lead to significant
improvements in the prediction
of both short- and long-term
earnings
Donelson, Jennings and
McInnis 2010
Advancing on the study of
Dichev and Tang to identify
factors responsible for changes
in the revenue-expense relation
Sample of Dichev and Tang
(2008) is duplicated with
additional requirement the
availability of data on cost of
goods sold, selling general and
administrative expenses,
income tax expenses, and
operating income after
depreciation
Time-series framework,
regression, decomposition
framework by Kee (2009), zscore by Altman (1968)
Changes primarily caused by
special items. Also economic
events associated with special
items turn out to be more
important than individual
accounting standards.
66
Appendix 2. Proof of calculations
The proof for both the equations as well as the hypotheses discussed
below are derived from Dichev and Tang (2008):
Proof of Equation 3:
The second equation can be also be written as Et* = (1 - β1*)Ecc + β1*Et-1* + εt.
The third equation is the result of substituting (1 - β1*)Ecc with β0* and keeping Ecc and β1* constant.
Proof of Hypothesis 1:
Under perfect matching the contemporaneous correlation between revenues and earnings is:
Corr(Revt*, Expt*) = Cov (Revt*, Expt*) / (Std(Revt*) x Std(Expt*))
Under poor matching this correlation is:
Corr(Revt*, Expt) = Cov (Revt*, Expt*) / (Std(Revt*) x Std(Expt* + νt))
Since the denominator for the correlation in the case of bad matching is larger than the
denominator for perfect matching and increasing in the variance ν, it can be concluded that poor
matching decreases the contemporaneous correlation between revenues and earnings.
Proof of Hypothesis 3:
From the third equation it followed that perfect matching can be depicted as:
(3)
Et* = β0* + β1*Et-1* + εt
In a situation of poor matching noise is added to the economic relation of advancing expenses to
earn revenues which results in Et = Et* - νt and Et-1 = Et-1* - νt-1
Substituting these into equation (3) results in the following:
Et + νt = β0* + β1*(Et-1 + νt-1) + εt
Which can be rewritten as:
Et = β0* + β1*Et-1 + (εt + β1*νt-1 - νt)
Re-arranging this equation using the fact that νt = τt – τt-1 and νt-1 = τt-1 – τt-2 leads to:
Et = β0* + β1*Et-1 + (εt - τt+ (1 + β1*)τt-1 - β1*τt-2)
67
In this equation both the third as well as fourth term in the error term are negatively correlated with
the regressorEt-1. This is due to the fact that Et-1 = Et-1* - τt-1 + τt-2.
The error term, εt - τt + (1 + β1*)τt-1 - β1*τt-2, can be substituted by λ which results in:
Et = β0* + β1*Et-1 + λ
An ordinary least squares estimation produces an inconsistent and biased estimate of β1*, because
the error term is correlated with the independent variable.
From the OLS expression is can be concluded that the slope coefficient β1 decreases in a situation of
poor matching, because the summation term in the numerator is negative and the negative
autocorrelation in the τ terms increases this effect.
68
Appendix 3. Accounting variables
Total assets
Sales
Earnings before
extraordinary items
: Thomson One Banker item TotalAssets
: Thomson One Banker item Sales
Earnings
Revenues
Expenses
: Earnings before extraordinary items divided by average total assets
: Sales divided by average assets
: Sales minus earnings before extraordinary items divided by average
assets
Vol (Earnings)
: Earnings volatility, standard deviation of the deflated earnings for
the most recent 5 years
: Revenues volatility, standard deviation of the deflated revenues for
the most recent 5 years
: Expenses volatility, standard deviation of the deflated expenses for
the most recent 5 years
Vol (Revenues)
Vol (Expenses)
: Thomson One Banker item IncomeBefExtraItemsAndPfdDiv
Corr (Revenues, Expenses)
: Correlation between revenues and expenses, correlation between
the deflated revenues and the deflated expenses for the most
recent 5 years
Two-year earnings
Two-year revenues
Two-year expenses
: Average of deflated earnings for the current and previous periods.
: Average of deflated revenues for the current and previous periods.
: Average of deflated expenses for the current and previous periods.
Vol (two-year earnings)
: Volatility in two-year earnings, standard deviation of two-year
earnings for the most recent 5 non-overlapping two-year periods
: Volatility in two-year revenues, standard deviation of two-year
revenues for the most recent 5 non-overlapping two-year periods
: Volatility in two-year expenses, standard deviation of two-year
expenses for the most recent 5 non-overlapping two-year periods
Vol (two-year revenues)
Vol (two-year expenses)
Corr (two-year revenues,
two-year expenses)
For each sample year
Persistence in earnings
Autocorrelation in
earnings change
: Correlation between two-year revenues and two-year expenses,
correlation between two-year revenues and two-year expenses for
the most recent 5 non-overlapping two-year periods
: Slope coefficient from the regression of current deflated earnings
on the previous period earnings on a cross-section basis
: Cross-sectional correlation between current earnings change and
past earnings change
69
For every other sample year
Persistence in
two-year earnings
Autocorrelation in
two-year earnings change
: Slope coefficient from the regression of current two-year earnings
on past two-year earnings on a cross-section basis
: Cross-sectional correlation between current two-year earnings
change and past two-year earnings change
70
Appendix 4 Selection criteria
Sample one based on one-year specification
Thomson One Banker Search criteria
Passed
1
AllCompanies
1046584
2
IsNA(tf.PrivateIndicator)
81996
3
Not Contains(tf.GeneralIndustryClassification, "04")
78450
4
Not Contains(tf.GeneralIndustryClassification, "05")
77457
5
Not Contains(tf.GeneralIndustryClassification, "06")
71989
6
IsInList(tf.CountryCode, "FRA", "DEU", "NLD", "BEL", "LUX", "GBR", "IRL", "ITA", "DNK",
"FIN", "GRC", "ESP", "SWE", "AUT", "PRT")
8746
7
SelectTop(tf.TotalAssets[Yt],1000,false)
1000
8
Not IsNA(tf.TotalAssets[Yt])
9
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt+1])
10
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt])
11
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-1])
12
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-2])
13
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-3])
14
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-4])
15
Not IsNA(tf.Sales[Yt+1])
16
Not IsNA(tf.Sales[Yt])
17
Not IsNA(tf.Sales[Yt-1])
18
Not IsNA(tf.Sales[Yt-2])
19
Not IsNA(tf.Sales[Yt-3])
20
Not IsNA(tf.Sales[Yt-4])
21
Contains(tf.GeneralIndustryClassification, "")
22
Contains(tf.PrimarySICCode, "")
71
Sample two based two-year specification
Thomson One Banker Search criteria
Passed
1
AllCompanies
1046584
2
IsNA(tf.PrivateIndicator)
81996
3
Not Contains(tf.GeneralIndustryClassification, "04")
78450
4
Not Contains(tf.GeneralIndustryClassification, "05")
77457
5
Not Contains(tf.GeneralIndustryClassification, "06")
71989
6
IsInList(tf.CountryCode, "FRA", "DEU", "NLD", "BEL", "LUX", "GBR", "IRL", "ITA", "DNK",
"FIN", "GRC", "ESP", "SWE", "AUT", "PRT")
8746
7
SelectTop(tf.TotalAssets[Yt],1000,false)
1000
8
Not IsNA(tf.TotalAssets[Yt])
9
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt+1])
10
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt])
11
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-1])
12
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-2])
13
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-3])
14
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-4])
15
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-5])
16
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-6])
17
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-7])
18
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-8])
19
Not IsNA(tf.IncomeBefExtraItemsAndPfdDiv[Yt-9])
20
Not IsNA(tf.Sales[Yt+1])
21
Not IsNA(tf.Sales[Yt])
22
Not IsNA(tf.Sales[Yt-1])
23
Not IsNA(tf.Sales[Yt-2])
24
Not IsNA(tf.Sales[Yt-3])
25
Not IsNA(tf.Sales[Yt-4])
26
Not IsNA(tf.Sales[Yt-5])
27
Not IsNA(tf.Sales[Yt-6])
28
Not IsNA(tf.Sales[Yt-7])
29
Not IsNA(tf.Sales[Yt-8])
30
Not IsNA(tf.Sales[Yt-9])
31
Contains(tf.GeneralIndustryClassification, "")
32
Contains(tf.PrimarySICCode, "")
72
Appendix 5 Firm years in sample
Sample 1
Thomson One banker firm-years with available data on volatility
in earnings, revenues and expenses
21.999
Firm-year observations after eliminating missing values and
companies with a primary SIC code between 6000 – 6999
21.388
Firm-year observations of one-year variables where firms are also
present in the two-year sample
13.386
Firm-years in the one-year sample after eliminating the top 1 percent
and the bottom 1 percent of all one year variables
12.306
One-year firm years in sample 1:
12.306
Sample 2
Thomson One banker firm-years with available data on volatility
in two-years earnings, two-year revenues and two-year expenses
14.125
Firm-year observations after eliminating missing values and
companies with a primary SIC code between 6000 – 6999
13.717
Firms-year observations of two-year variables for each
odd-year from 1989 to 2009
6.779
Firm-years in the two-year sample after eliminating the top 1 percent
and the bottom 1 percent of all one year variables
6.248
Two-year firm years in sample 2:
6.248
73
Appendix 6 Descriptive statistics
Sample 1
Variable
N
Mean
Standard
Deviation
Median
Earnings
12.306
0,0401
0,0447
0,0378
Revenues
12.306
1,0595
0,5623
0,9685
Expenses
12.306
1,0195
0,5589
0,9266
Vol (Earnings)
12.306
0,0230
0,0216
0,0162
Vol (Revenues)
12.306
0,1851
0,1295
0,1518
Vol (Expenses)
12.306
0,1791
0,1276
0,1449
Corr (Revenues, Expenses)
12.306
0,9782
0,0667
0,9976
Persistence in Earnings
22
0,619
Autocorrelation in Earnings change
22
-0,191
Sample 2
Variable
N
Mean
Standard
Deviation
Median
Two-year Earnings
6.248
0,0382
0,0403
0,0360
Two-year Revenues
6.248
1,0338
0,5592
0,9419
Two-year Expenses
6.248
0,9956
0,5570
0,9022
Vol (two-year Earnings)
6.248
0,0222
0,0173
0,0176
Vol (two-year Revenues)
6.248
0,2499
0,1662
0,2080
Vol (two-year Expenses
6.248
0,2409
0,1648
0,1975
Corr (two-year Revenues, two-year
Expenses)
6.248
0,9906
0,0293
0,9989
Persistence in two-year Earnings
11
0,623
Autocorrelation in two-year Earnings
change
11
-0,188
74
Sample 3
Variable
N
Mean
Standard
Deviation
Median
Earnings
18.834
0,0383
0,0452
0,0362
Revenues
18.834
1,0879
0,5939
0,9885
Expenses
18.834
1,0496
0,5904
0,9489
Vol (Earnings)
18.834
0,0228
0,0219
0,0159
Vol (Revenues)
18.834
0,1973
0,1413
0,1606
Vol (Expenses)
18.834
0,1913
0,1395
0,1543
Corr (Revenues, Expenses)
18.834
0,9792
0,0654
0,9980
Persistence in Earnings
26
0,651
Autocorrelation in Earnings change
26
-0,175
Sample 4
Variable
N
Mean
Standard
Deviation
Median
Two-year Earnings
12.645
0,038
0,040
0,036
Two-year Revenues
12.645
1,017
0,547
0,928
Two-year Expenses
12.645
0,979
0,545
0,890
Vol (two-year Earnings)
12.645
0,022
0,018
0,018
Vol (two-year Revenues)
12.645
0,246
0,163
0,205
Vol (two-year Expenses)
12.645
0,238
0,162
0,196
Corr (two-year Revenues, two-year
Expenses)
12.645
0,990
0,030
0,999
Persistence in two-year Earnings
22
0,599
Autocorrelation in two-year Earnings
change
22
-0,200
75
Appendix 7 Tests for normality
Sample 1
Statistics
Earn.
Rev
Exp
Vol
Vol
Vol
[Y00]
[Y00]
[Y00]
(Earn)
(Rev)
(Exp)
[Y00]
[Y00]
[Y00]
Corr
(Rev,Exp)
[Y00]
N Valid
12306
12306
12306
12306
12306
12306
12306
N Missing
0
0
0
0
0
0
0
Skewness -,064
1,244
1,263
2,540
1,616
1,659
-6,152
Std. Error ,022
of
Skewness
,022
,022
,022
,022
,022
,022
Kurtosis
2,136
2,101
2,161
9,266
3,210
3,384
45,987
Std. Error
of
Kurtosis
,044
,044
,044
,044
,044
,044
,044
Test of normality
Kolmogorov-Smirnov
Statistic
df
Sig.
Earnings[Y00]
,077
12306
,000
Revenues[Y00]
,088
12306
,000
Expenses[Y00]
,087
12306
,000
Vol (Earnings)[Y00]
,162
12306
,000
Vol
(Revenues)[Y00]
,117
12306
,000
Vol (Expenses)[Y00]
,123
12306
,000
Corr (Revenues,
Expenses)[Y00]
,372
12306
,000
76
Sample 2
Statistics
2y
Earn[Y01
]
2y
Rev[Y01
]
2y
Exp[Y01
]
Vol (2y
Earn)[Y01
]
Vol (2y
Rev)[Y01
]
Vol (2y
Exp)[Y01
]
Corr (2y
Rev, 2y
Exp)[Y01
]
N Valid
6248
6248
6248
6248
6248
6248
6248
N
Missing
0
0
0
0
0
0
0
Skewnes
s
-,042
1,260
1,278
2,204
1,531
1,577
-6,917
Std.
Error of
Skewnes
s
,031
,031
,031
,031
,031
,031
,031
Kurtosis
1,801
2,106
2,148
7,720
2,913
3,093
61,083
Std.
Error of
Kurtosis
,062
,062
,062
,062
,062
,062
,062
Test of normality
Kolmogorov-Smirnov
Statistic
df
Sig.
Two-year
Earnings[Y01]
,066
6248
,000
Two-year
Revenues[Y01]
,088
6248
,000
Two-year
Expenses[Y01]
,089
6248
,000
Vol (Two-year
Earnings)[Y01]
,130
6248
,000
Vol (Two-year
Revenues)[Y01]
,110
6248
,000
Vol (Two-year
Expenses)[Y01]
,116
6248
,000
Corr (Two-year
Revenues, Two-year
Expenses)[Y01]
,375
6248
,000
77
Histograms and P-P plots
78
79
Appendix 8 Multicollinearity
Sample 1
Year
VIF
t-1
VIF
t
VIF
t+1
DurbinWatson
1988
7,019
8,602
2,054
1,880
1989
8,865
15,422
7,208
1,840
1990
11,466
20,415
8,658
1,894
1991
7,892
8,902
1,924
1,960
1992
8,183
16,221
9,010
1,908
1993
11,067
26,546
12,164
1,882
1994
13,176
24,874
9,482
1,920
1995
11,382
21,163
10,845
1,887
1996
10,240
18,912
7,403
1,938
1997
9,688
11,277
2,402
1,847
1998
7,369
12,997
5,824
1,993
1999
9,402
14,561
5,357
2,027
2000
6,063
11,697
6,613
2,131
2001
8,202
18,601
9,277
1,997
2002
8,972
27,457
14,558
1,984
2003
11,627
25,047
10,849
1,808
2004
13,119
23,512
6,678
1,846
2005
10,533
17,288
6,576
1,914
2006
9,868
19,849
9,103
1,865
2007
8,941
19,234
8,307
1,958
2008
11,951
15,036
9,254
1,942
2009
9,990
18,040
15,878
1,891
80
Appendix 9 Revenue-expense regression
Sample 1
Year
R
R2
F
F
Sig.
β1
Sig.
Β2
Sig.
Β3
Sig.
1988
0,998
0,995
35487
0,000
0,000
0,000
0,003
1989
0,998
0,996
39280
0,000
0,003
0,000
0,899
1990
0,998
0,997
44754
0,000
0,000
0,000
0,020
1991
0,997
0,995
29795
0,000
0,273
0,000
0,017
1992
0,997
0,994
30401
0,000
0,003
0,000
0,000
1993
0,997
0,994
26274
0,000
0,007
0,000
0,000
1994
0,995
0,994
30624
0,000
0,027
0,000
0,402
1995
0,997
0,994
29069
0,000
0,544
0,000
0,000
1996
0,997
0,995
38692
0,000
0,003
0,000
0,000
1997
0,998
0,995
41961
0,000
0,000
0,000
0,042
1998
0,997
0,995
36099
0,000
0,307
0,000
0,000
1999
0,998
0,995
38122
0,000
0,013
0,000
0,002
2000
0,997
0,994
28921
0,000
0,045
0,000
0,001
2001
0,996
0,992
22908
0,000
0,426
0,000
0,000
2002
0,997
0,993
25896
0,000
0,027
0,000
0,000
2003
0,997
0,993
26474
0,000
0,366
0,000
0,000
2004
0,997
0,994
32772
0,000
0,245
0,000
0,000
2005
0,997
0,994
32118
0,000
0,001
0,000
0,030
2006
0,997
0,993
31771
0,000
0,012
0,000
0,004
2007
0,997
0,994
33437
0,000
0,001
0,000
0,000
2008
0,996
0,992
28335
0,000
0,021
0,000
0,000
2009
0,996
0,992
27499
0,000
0,000
0,000
0,000
81
Sample 3
Year
R
R2
F
F
Sig.
β1
Sig.
Β2
Sig.
Β3
Sig.
1984
0,998
0,997
62459
0,000
0,000
0,000
0,566
1985
0,998
0,997
69832
0,000
0,019
0,000
0,016
1986
0,998
0,996
54355
0,000
0,014
0,000
0,013
1987
0,998
0,996
47754
0,000
0,069
0,000
0,000
1988
0,998
0,996
48978
0,000
0,000
0,000
0,031
1989
0,998
0,996
52940
0,000
0,047
0,000
0,944
1990
0,998
0,996
54254
0,000
0,010
0,000
0,057
1991
0,998
0,995
47599
0,000
0,334
0,000
0,000
1992
0,997
0,994
42120
0,000
0,002
0,000
0,000
1993
0,997
0,993
37642
0,000
0,000
0,000
0,000
1994
0,997
0,994
44619
0,000
0,036
0,000
0,000
1995
0,997
0,994
40927
0,000
0,101
0,000
0,000
1996
0,997
0,995
44632
0,000
0,000
0,000
0,000
1997
0,997
0,995
49903
0,000
0,000
0,000
0,026
1998
0,997
0,995
47285
0,000
0,758
0,000
0,000
1999
0,998
0,995
47636
0,000
0,022
0,000
0,000
2000
0,997
0,994
40570
0,000
0,130
0,000
0,000
2001
0,996
0,993
34276
0,000
0,572
0,000
0,000
2002
0,997
0,994
38527
0,000
0,339
0,000
0,000
2003
0,997
0,994
37144
0,000
0,262
0,000
0,000
2004
0,997
0,994
39492
0,000
0,387
0,000
0,000
2005
0,997
0,994
38461
0,000
0,016
0,000
0,000
2006
0,996
0,993
35704
0,000
0,005
0,000
0,000
2007
0,997
0,994
40375
0,000
0,001
0,000
0,003
2008
0,996
0,993
35639
0,000
0,026
0,000
0,000
2009
0,996
0,992
34266
0,000
0,000
0,000
0,000
82
Appendix 10 Persistence in earnings
Sample 1
Year
R
R2
F
F
Sig.
β1
Sig.
1988
0,683
0,466
428
0,000
0,000
1989
0,749
0,560
640
0,000
0,000
1990
0,734
0,538
549
0,000
0,000
1991
0,689
0,474
424
0,000
0,000
1992
0,692
0,480
471
0,000
0,000
1993
0,694
0,482
473
0,000
0,000
1994
0,684
0,468
447
0,000
0,000
1995
0,594
0,352
284
0,000
0,000
1996
0,630
0,396
388
0,000
0,000
1997
0,601
0,361
344
0,000
0,000
1998
0,596
0,355
306
0,000
0,000
1999
0,573
0,328
278
0,000
0,000
2000
0,481
0,231
159
0,000
0,000
2001
0,397
0,158
102
0,000
0,000
2002
0,628
0,394
346
0,000
0,000
2003
0,572
0,327
268
0,000
0,000
2004
0,492
0,242
183
0,000
0,000
2005
0,678
0,460
521
0,000
0,000
2006
0,687
0,471
558
0,000
0,000
2007
0,673
0,453
535
0,000
0,000
2008
0,516
0,266
237
0,000
0,000
2009
0,504
0,255
233
0,000
0,000
83
Sample 2
Year
R
R2
F
F
Sig.
β1
Sig.
1989
0,627
0,393
324
0,000
0,000
1991
0,629
0,395
312
0,000
0,000
1993
0,621
0,385
324
0,000
0,000
1995
0,597
0,357
289
0,000
0,000
1997
0,613
0,376
367
0,000
0,000
1999
0,567
0,321
271
0,000
0,000
2001
0,470
0,221
154
0,000
0,000
2003
0,522
0,273
207
0,000
0,000
2005
0,436
0,190
143
0,000
0,000
2007
0,644
0,415
461
0,000
0,000
2009
0,444
0,197
164
0,000
0,000
84
Sample 3
Year
R
R2
F
F
Sig.
β1
Sig.
1984
0,797
0,635
1146
0,000
0,000
1985
0,750
0,562
852
0,000
0,000
1986
0,640
0,409
459
0,000
0,000
1987
0,706
0,499
624
0,000
0,000
1988
0,724
0,525
730
0,000
0,000
1989
0,758
0,575
899
0,000
0,000
1990
0,752
0,565
844
0,000
0,000
1991
0,682
0,465
618
0,000
0,000
1992
0,663
0,440
609
0,000
0,000
1993
0,711
0,506
779
0,000
0,000
1994
0,675
0,456
646
0,000
0,000
1995
0,590
0,348
401
0,000
0,000
1996
0,616
0,379
453
0,000
0,000
1997
0,640
0,409
529
0,000
0,000
1998
0,580
0,336
362
0,000
0,000
1999
0,556
0,310
310
0,000
0,000
2000
0,462
0,214
190
0,000
0,000
2001
0,451
0,203
186
0,000
0,000
2002
0,630
0,397
476
0,000
0,000
2003
0,601
0,361
413
0,000
0,000
2004
0,553
0,306
321
0,000
0,000
2005
0,683
0,466
652
0,000
0,000
2006
0,673
0,454
637
0,000
0,000
2007
0,663
0,439
604
0,000
0,000
2008
0,519
0,270
298
0,000
0,000
2009
0,469
0,220
227
0,000
0,000
85
Sample 4
Year
R
R2
F
F
Sig.
β1
Sig.
1989
0,627
0,393
325
0,000
0,000
1990
0,714
0,510
488
0,000
0,000
1991
0,629
0,395
311
0,000
0,000
1992
0,491
0,241
157
0,000
0,000
1993
0,620
0,384
322
0,000
0,000
1994
0,686
0,470
468
0,000
0,000
1995
0,589
0,347
278
0,000
0,000
1996
0,585
0,342
300
0,000
0,000
1997
0,611
0,374
363
0,000
0,000
1998
0,596
0,355
327
0,000
0,000
1999
0,554
0,307
253
0,000
0,000
2000
0,561
0,315
258
0,000
0,000
2001
0,468
0,219
152
0,000
0,000
2002
0,239
0,057
33
0,000
0,000
2003
0,520
0,271
206
0,000
0,000
2004
0,490
0,240
178
0,000
0,000
2005
0,396
0,157
113
0,000
0,000
2006
0,607
0,369
378
0,000
0,000
2007
0,635
0,403
439
0,000
0,000
2008
0,624
0,389
425
0,000
0,000
2009
0,445
0,198
165
0,000
0,000
2010
0,470
0,221
205
0,000
0,000
86
Appendix 11 Sample composition
Changing industry composition
Industry classification
Beginning sample year
1988
Ending sample year
2009
Count
Percentage
Count
Percentage
Agriculture, Forestry, and Fishing
1
0,20
4
0,59
Mining
11
2,24
30
4,39
Construction
36
7,32
48
7,03
Manufacturing
286
58,13
317
46,41
Transportation, Communications,
Electric, Gas, and Sanitary Service
72
14,63
126
18,45
Wholesale trade
24
4,88
32
4,69
Retail trade
33
6,70
46
6,73
Services
25
5,08
80
11,71
Public administration
4
0,81
0
0
Decreasing Subsample
Construction
Manufacturing
Wholesale trade
Public administration
Increasing Subsample
Agriculture, Forestry, and Fishing
Mining
Transportation, Communications, Electric, Gas, and Sanitary Service
Retail trade
Services
87
Changing country composition
Country
Beginning sample year 1988
Ending sample year 2009
Count
Percentage
Count
Percentage
Austria
6
1,22
12
1,76
Belgium
20
4,07
18
2,64
Germany
105
21,34
88
12,88
Denmark
13
2,64
22
3,22
Spain
20
4,07
46
6,73
Finland
26
5,28
29
4,25
France
93
18,90
127
18,59
Great-Brittan
120
24,39
140
20,50
Ireland
3
0,61
14
2,05
Italy
19
3,86
58
8,49
Netherlands
29
5,89
44
6,44
Sweden
38
7,72
37
5,42
Greece
0
0
26
3,81
Luxembourg
0
0
8
1,17
Portugal
0
0
14
2,05
Decreasing Subsample
Belgium
Great-Brittan
Germany
Sweden
Finland
France
Increasing Subsample
Austria
Ireland
Denmark
Italy
Spain
Netherlands
Greece
Portugal
Luxembourg
88
Countries – decreasing
Year
Coefficient on past
expenses (β1)
Coefficient on
current expenses
(β2)
Coefficient on future
expenses (β3)
1988
-0,030
1,042
-0,008
1989
0,029
0,982
-0,003
1990
0,053
0,942
0,016
1991
-0,017
1,017
0,007
1992
-0,020
0,980
0,046
1993
-0,035
0,989
0,042
1994
-0,046
1,029
0,009
1995
-0,010
0,920
0,086
1996
-0,063
1,004
0,060
1997
-0,051
1,052
0,005
1998
-0,004
0,940
0,066
1999
-0,023
1,007
0,016
2000
0,017
0,946
0,036
2001
0,010
0,930
0,055
2002
-0,031
0,951
0,067
2003
-0,013
0,980
0,028
2004
-0,008
0,975
0,032
2005
-0,032
1,013
0,018
2006
-0,040
1,006
0,020
2007
0,039
0,939
0,031
2008
-0,038
0,978
0,060
2009
-0,082
0,975
0,112
Mean 1988 to 1998
-0,018
0,991
0,030
Mean 1999 to 2009
-0,018
0,973
0,043
Difference
0
-0,018
0,013
P-value difference
0,964
0,237
0,288
89
Countries – decreasing
Year
R
R2
β1
Sig.
Β2
Sig.
Β3
Sig.
1988
0,998
0,995
0,001
0,000
0,003
1989
0,998
0,996
0,005
0,000
0,697
1990
0,998
0,996
0,000
0,000
0,030
1991
0,997
0,995
0,108
0,000
0,064
1992
0,997
0,994
0,034
0,000
0,000
1993
0,997
0,994
0,005
0,000
0,000
1994
0,997
0,994
0,002
0,000
0,409
1995
0,997
0,994
0,476
0,000
0,000
1996
0,997
0,994
0,000
0,000
0,000
1997
0,997
0,995
0,000
0,000
0,184
1998
0,997
0,994
0,721
0,000
0,000
1999
0,997
0,995
0,028
0,000
0,036
2000
0,997
0,994
0,079
0,000
0,000
2001
0,996
0,992
0,386
0,000
0,000
2002
0,997
0,994
0,020
0,000
0,000
2003
0,997
0,993
0,472
0,000
0,011
2004
0,997
0,993
0,641
0,000
0,001
2005
0,997
0,994
0,008
0,000
0,018
2006
0,997
0,993
0,003
0,000
0,023
2007
0,997
0,993
0,002
0,000
0,004
2008
0,996
0,993
0,008
0,000
0,000
2009
0,996
0,992
0,000
0,000
0,000
90
Countries – increasing
Year
Coefficient on past
expenses (β1)
Coefficient on
current expenses
(β2)
Coefficient on future
expenses (β3)
1988
-0,023
1,029
0,015
1989
0,029
0,982
0,011
1990
0,013
0,985
0,020
1991
0,007
0,952
0,068
1992
-0,069
1,066
0,026
1993
-0,015
1,008
0,025
1994
0,033
0,971
0,019
1995
0,007
0,977
0,025
1996
0,018
0,964
0,028
1997
0,018
0,945
0,039
1998
-0,026
1,015
0,026
1999
-0,020
1,003
0,025
2000
0,018
0,991
0,002
2001
-0,001
0,980
0,029
2002
-0,010
0,942
0,054
2003
0,001
0,919
0,073
2004
-0,034
0,994
0,045
2005
-0,043
1,052
-0,002
2006
0,013
0,962
0,036
2007
0,029
0,955
0,031
2008
0,001
0,932
0,077
2009
-0,079
1,057
0,035
Mean 1988 to 1998
-0,001
0,990
0,027
Mean 1999 to 2009
-0,011
0,981
0,039
Difference
-0,010
-0,009
0,012
P-value difference
0,362
0,572
0,161
91
Countries – increasing
Year
R
R2
β1
Sig.
Β2
Sig.
Β3
Sig.
1988
0,998
0,996
0,239
0,000
0,420
1989
0,997
0,995
0,328
0,000
0,470
1990
0,998
0,997
0,592
0,000
0,331
1991
0,997
0,995
0,773
0,000
0,003
1992
0,998
0,996
0,004
0,000
0,136
1993
0,996
0,993
0,575
0,000
0,343
1994
0,998
0,996
0,234
0,000
0,210
1995
0,997
0,994
0,810
0,000
0,171
1996
0,998
0,997
0,131
0,000
0,032
1997
0,999
0,998
0,163
0,000
0,002
1998
0,999
0,998
0,032
0,000
0,001
1999
0,998
0,996
0,154
0,000
0,010
2000
0,997
0,994
0,244
0,000
0,898
2001
0,996
0,992
0,957
0,000
0,148
2002
0,995
0,991
0,710
0,000
0,012
2003
0,996
0,992
0,969
0,000
0,003
2004
0,998
0,997
0,054
0,000
0,003
2005
0,997
0,994
0,014
0,000
0,894
2006
0,997
0,994
0,591
0,000
0,024
2007
0,997
0,994
0,197
0,000
0,041
2008
0,996
0,992
0,960
0,000
0,000
2009
0,996
0,991
0,000
0,000
0,056
92
Industries – decreasing
Year
Coefficient on past
expenses (β1)
Coefficient on
current expenses
(β2)
Coefficient on future
expenses (β3)
1988
-0,021
1,036
-0,008
1989
0,029
0,979
0,000
1990
0,044
0,960
0,011
1991
0,003
1,006
0,009
1992
-0,029
0,993
0,050
1993
-0,058
1,033
0,029
1994
-0,036
1,047
-0,008
1995
0,001
0,930
0,065
1996
-0,042
1,010
0,037
1997
-0,011
0,992
0,020
1998
-0,022
0,960
0,071
1999
-0,025
1,014
0,015
2000
0,019
0,962
0,022
2001
-0,004
0,937
0,063
2002
-0,032
0,962
0,056
2003
0,013
0,909
0,067
2004
-0,023
0,963
0,051
2005
-0,033
1,016
0,018
2006
-0,036
0,995
0,031
2007
0,052
0,936
0,029
2008
-0,020
0,966
0,060
2009
-0,095
1,039
0,065
Mean 1988 to 1998
-0,013
0,995
0,025
Mean 1999 to 2009
-0,017
0,973
0,043
Difference
-0,004
-0,022
0,018
P-value difference
0,793
0,184
0,040
93
Industries – decreasing
Year
R
R2
β1
Sig.
Β2
Sig.
Β3
Sig.
1988
0,997
0,994
0,018
0,000
0,002
1989
0,997
0,994
0,005
0,000
0,995
1990
0,998
0,995
0,001
0,000
0,368
1991
0,997
0,993
0,850
0,000
0,031
1992
0,996
0,992
0,008
0,000
0,000
1993
0,996
0,991
0,000
0,000
0,022
1994
0,996
0,992
0,020
0,000
0,406
1995
0,995
0,991
0,936
0,000
0,000
1996
0,997
0,994
0,002
0,000
0,000
1997
0,996
0,993
0,271
0,000
0,020
1998
0,997
0,994
0,039
0,000
0,000
1999
0,997
0,994
0,015
0,000
0,047
2000
0,996
0,993
0,095
0,000
0,020
2001
0,994
0,988
0,785
0,000
0,000
2002
0,995
0,991
0,032
0,000
0,000
2003
0,995
0,991
0,464
0,000
0,000
2004
0,996
0,993
0,185
0,000
0,000
2005
0,996
0,992
0,021
0,000
0,126
2006
0,996
0,992
0,026
0,000
0,007
2007
0,996
0,991
0,002
0,000
0,031
2008
0,995
0,991
0,226
0,000
0,000
2009
0,995
0,990
0,000
0,000
0,000
94
Industries – increasing
Year
Coefficient on past
expenses (β1)
Coefficient on
current expenses
(β2)
Coefficient on future
expenses (β3)
1988
-0,058
1,035
0,023
1989
0,020
0,985
0,003
1990
0,039
0,947
0,025
1991
-0,026
1,017
0,010
1992
-0,030
1,024
0,011
1993
0,012
0,926
0,058
1994
-0,008
0,930
0,070
1995
-0,023
0,959
0,063
1996
-0,008
0,906
0,103
1997
-0,072
1,078
0,005
1998
0,010
0,963
0,034
1999
-0,016
0,992
0,023
2000
0,016
0,960
0,027
2001
0,035
0,935
0,031
2002
-0,019
0,930
0,076
2003
-0,041
1,028
0,011
2004
0,005
0,982
0,022
2005
-0,034
1,023
0,012
2006
-0,018
0,998
0,015
2007
0,028
0,945
0,033
2008
-0,037
0,967
0,071
2009
-0,054
0,976
0,084
Mean 1988 to 1998
-0,013
0,979
0,037
Mean 1999 to 2009
-0,012
0,976
0,037
Difference
0,001
-0,003
0
P-value difference
0,931
0,870
1,000
95
Industries – increasing
Year
R
R2
β1
Sig.
Β2
Sig.
Β3
Sig.
1988
0,999
0,998
0,001
0,000
0,194
1989
0,999
0,998
0,381
0,000
0,785
1990
0,999
0,998
0,029
0,000
0,002
1991
0,999
0,997
0,027
0,000
0,134
1992
0,999
0,997
0,053
0,000
0,524
1993
0,998
0,996
0,478
0,000
0,003
1994
0,998
0,997
0,747
0,000
0,001
1995
0,999
0,997
0,285
0,000
0,000
1996
0,998
0,997
0,518
0,000
0,000
1997
0,999
0,998
0,000
0,000
0,128
1998
0,998
0,996
0,514
0,000
0,001
1999
0,998
0,996
0,293
0,000
0,016
2000
0,998
0,996
0,162
0,000
0,068
2001
0,998
0,996
0,034
0,000
0,021
2002
0,998
0,996
0,341
0,000
0,000
2003
0,998
0,996
0,052
0,000
0,415
2004
0,998
0,996
0,776
0,000
0,029
2005
0,998
0,995
0,016
0,000
0,136
2006
0,997
0,995
0,296
0,000
0,138
2007
0,998
0,996
0,040
0,000
0,002
2008
0,997
0,994
0,028
0,000
0,000
2009
0,997
0,993
0,000
0,000
0,000
96