ISM UNIVERSITY OF MANAGEMENT AND ECONOMICS
MASTER OF SCIENCE IN FINANCIAL ECONOMICS PROGRAMME
Skirmantė Dapkutė
MASTER'S THESIS
THE EFFECT OF SARBANES - OXLEY ACTS LISTING REQUIREMENTS ON
EUROPEAN COMPANIES VALUE
Supervisor
Doc. Dr. Aušra Jurkštienė
VILNIUS, 2012
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Abstract
Dapkutė, S. The effect of Sarbanes-Oxley Act’s listing requirements on European Companies‘
value [Manuscript]: Master Thesis: economics. Vilnius, ISM University of Management and
Economics, 2012.
The study aims to investigate what effect the Sarbanes – Oxley Act of 2002 (SOX) have on
European companies’ corporate value, measured by Tobin’s Q ratio. The SOX Act is a U.S.
federal law, enacted as a response to major corporate and accounting scandals in early 2000’s.
It increased companies’ internal controls requirements on corporate governance and reporting
standards, to ensure correctness of financial information and strengthen trust in capital
markets. There is no consensus whether SOX benefits overweight huge compliance costs. The
Act is also applicable to European companies cross-listed in any U.S. stock exchange. In the
research financial data of 49 matched pairs of companies cross listed in New York (SOX
applicable) and London (SOX not applicable) stock exchanges for the period 2000-2010 are
analyzed. Different ratios used in the previous researches were collected and summarized into
common factors, representing three main aspects of company: Capital, Performance and Risk.
The results of multiple linear regression indicate SOX has a significant positive impact on
companies’ corporate value. In addition, a positive impact of Capital and Performance factors
and a negative impact of Risk factor on firm value was found. Factor reliability test indicated
that variables used in the study do not represent the whole concept, therefore further analysis
with additional ratios could be performed. This study is limited to the analysis of large
companies, while the Act might have a different effect to the small ones. Thus, research could
be repeated with applying methodological design to matched pairs of small companies. This
study also indicated SOX effect was not consistent: the implementation costs were
significantly higher than expected, but afterwards an evident benefit through improved
corporate governance is observed. Further research could perform a three period (before SOX,
SOX implementation and after SOX) analysis.
Keywords: the Sarbanes – Oxley Act, internal controls, listing requirements, corporate value,
factor analysis.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Santrauka
Dapkutė, S. Sarbanes-Oxley akto reikalavimų poveikis Europos įmonių vertei. [Rankraštis]:
magistro baigiamasis darbas: ekonomika. Vilnius, ISM Vadybos ir ekonomikos universitetas,
2012.
Šio darbo tikslas yra ištirti Sarbanes-Oxley akto (2002) poveikį Europos įmonių vertei,
išreikštai Tobin’s Q rodikliu. SOX aktas - tai JAV federalinis įstatymas, kuris buvo priimtas
kaip atsakas į didžiulius verslo ir apskaitos skandalus tūkstantmečio pradžioje. Aktas padidino
įmonės vidaus kontrolės reikalavimus, susijusius su įmonių valdymo ir atskaitomybės
standartais, siekiant užtikrinti teisingos finansinės informacijos pateikimą ir sustiprinti
pasitikėjimą kapitalo rinkose. Nėra bendros nuomonės dėl SOX reikalavimų laikymosi
naudos ir išlaidų dydžio santykio. Įstatymas taip pat yra privalomas ir Europos įmonėms
listinguojamoms bet kurioje JAV vertybinių popierių biržoje. Šiame darbe analizuojami 49
sugretintų įmonių 2000-2010 metiniai finansiniai rodikliai, kur vienoms įmonėms SOX
reikalavimai yra taikomi, o kitoms ne. Ankstesniuose tyrimuose naudojami rodikliai atrinkti ir
sujungti į apibendrinančius veiksnius, atspindinčius tris pagrindinius įmonės veiklos aspektus:
Kapitalą, Pelningumą ir Riziką. Remiantis daugialypės tiesinės regresijos rezultatais galima
teigti, kad SOX turi reikšmingą teigiamą poveikį įmonės vertei, kartu su Kapitalo ir Valdymo
faktoriais, tuo tarpu Rizikos faktoriaus poveikis yra reikšmingai neigiamas. Atliktas faktorių
patikimumo testas parodė, kad naudoti kintamieji neužpildo visos veiksnių koncepcijos, todėl
gilesnė analizė galėtų būti atliekama įtraukiant papildomus rodiklius. Šiame darbe analizuotas
SOX akto poveikis didelėms įmonėms, tuo tarpu įstatymo poveikis mažoms įmonėms galėtų
būti kitoks, todėl tyrimas galėtų būti pakartotas pritaikant tą patį metodą mažoms įmonėms.
Darbe taip pat atskleidžiama, kad SOX poveikis nėra vienareikšmis: įgyvendinimo išlaidos
buvo daug didesnės nei tikėtasi, tačiau vėliau pastebima akivaizdi nauda dėl efektyvesnio
įmonių valdymo. Tolimesnis tyrimas galėtų būti atliekamas dalijant laikotarpį į tris dalis
(prieš SOX, SOX įgyvendinimo ir po SOX).
Raktiniai žodžiai: Sarbanes - Oxley aktas, vidaus kontrolė, listingavimo reikalavimai, įmonės
vertė, faktorinė analizė.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Table of Contents
Abstract ...................................................................................................................................... 2
Santrauka .................................................................................................................................... 3
List of Figures ............................................................................................................................ 6
List of Tables .............................................................................................................................. 7
List of Abbreviations .................................................................................................................. 8
Introduction ................................................................................................................................ 9
1.
Literature Review.............................................................................................................. 11
1.1
Internal Controls and SOX Background .................................................................... 12
1.2
Sarbanes-Oxley Act Content and Effective Dates ..................................................... 13
1.3
SOX Impact on non U.S. Companies Cross – listing Decision ................................. 14
1.4
SOX Effect on Corporate Value: Benefits and Compliance Costs ........................... 17
2.
Problem Definition............................................................................................................ 20
3.
Methodological Approach ................................................................................................ 21
3.1
3.1.1
Data sample selection ......................................................................................... 22
3.1.2
Data sample description ..................................................................................... 24
3.2
4.
Data Sample Selection and Description .................................................................... 21
Variables Selection and Calculation .......................................................................... 24
3.2.1
The overall SOX effect on variables analyzed ................................................... 25
3.2.2
SOX effect to companies capital ........................................................................ 26
3.2.3
SOX effect to companies performance .............................................................. 28
3.2.4
SOX effect to companies riskiness..................................................................... 29
3.3
Statistical Considerations for Factor Analysis ........................................................... 30
3.4
Parallel Analysis ........................................................................................................ 32
3.5
Factor Matrix Extraction and Validation ................................................................... 33
3.6
Multiple Linear Regression ....................................................................................... 35
Empirical Results .............................................................................................................. 37
4.1
Data Analysis of Selected Variables ......................................................................... 37
4.1.1
Descriptive statistics ........................................................................................... 37
4.1.2
Normality and trend analysis.............................................................................. 39
4.1.3
Correlation among selected variables ................................................................ 41
4.2
Factor Analysis .......................................................................................................... 43
4.2.1
Parallel analysis .................................................................................................. 43
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
4.2.2
4.3
5.
6.
Factor extraction and validation ......................................................................... 44
Multiple Linear Regression Model Conduct and Transformation ............................. 47
Discussion ......................................................................................................................... 50
5.1
Significant Findings Overview and Interpretation .................................................... 50
5.2
Discussion on Research Findings in Context of Literature Reviewed ...................... 53
5.3
Limitations and Implications ..................................................................................... 55
Conclusions ....................................................................................................................... 56
Reference list ............................................................................................................................ 58
Appendices……………………………………………………………………………………63
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
List of Figures
Figure 1. Methodological approach estimating assumptions ............................................. 21
Figure 2. Data sample construction .................................................................................... 23
Figure 3. SOX impact to corporate value in terms of capital, performance and risk factors
............................................................................................................................................ 25
Figure 4. Change in NYSE and LSE cross listed companies Tobin’s Q ........................... 37
Figure 5. Parallel analysis .................................................................................................. 44
Figure 6. Generalized ratios’ impact on companies’ value ................................................ 51
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
List of Tables
Table 1. Summary of papers reviewed........……………………………….……………11
Table 2. NYSE cross listed firms mean ratios before and after SOX…………………..38
Table 3. NYSE and LSE cross listed firms mean ratios before and after SOX………...39
Table 4. Tobin’s Q independent sample t-test……………………………………...…..40
Table 5. Levene’s test for equality of variances………………………………………...40
Table 6. Variables correlation coefficient matrix……………………………………...42
Table 7. Kaiser Meyer Olkin and Bartlett sample adequacy test……………………...42
Table 8. Variable communalities estimates……………………………………………45
Table 9. Common factors accounted variance………………………………………...45
Table 10. Goodness of fit test………………...………………………………………...46
Table 11. Factor pattern matrix…………………………………………………………46
Table 12. Factor correlation matrix……………………………………………………..47
Table 13. Autoregressed model fit summary…………………………………………...48
Table 14. Prais-Winsten estimated regression coefficients………….…………………..49
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
List of Abbreviations
SOX
The Sarbanes - Oxley Act
NYSE
New York stock exchange
LSE
London stock exchange
COSO
The Committee on Sponsoring Organizations of the Treadway Commission
SEC
Security Exchange Commission
PCAOB
Public Company Accounting Oversight Board
GAAS
Generally Accepted Auditing Standards
IPO
Initial public offering
UKLA
United Kingdom Listing Authority
FSA
The Financial Services Authority
CEO
Chief Executive Officer
CFO
Chief Financial Officer
CAPEX
Capital expenditures
ROA
Return on assets
ROE
Return on equity
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Introduction
The efficient market hypothesis suggests financial markets are information efficient, and
prices precisely reflect resource allocation. However, if markets were in reality efficient,
regulating laws would be needless. The reason behind the regulations is equalization of
information among investors, thus the more accurate and transparent information is available,
the more efficient markets are.
The United States financial markets used to be not only the biggest and most attractive, but
also trustworthy place for investors. However, a decade ago numbers of large corporate and
accounting scandals (Enron, WorldCom) were revealed. In response to this, Security
Exchange Commission in 2002 passed a new market regulating law the Sarbanes - Oxley Act
(SOX) – to recover investor confidence and stock exchanges competitiveness. SOX
significantly increased requirements on corporate governance and reporting standards to
ensure the correctness of accounting and financial information. The act, since it has been
announced, attained very different assessments, as it was applied to all firms listed in any U.S.
stock exchange. Many discussions on the optimal level of internal controls were held, coming
to a question whether a law can improve the credibility of financial reporting and was it really
necessary to apply SOX for foreign companies. Although it was broadly supported, and
expected to accrue benefits for companies and to strengthen trust in the capital markets, it had
lots of critics saying it is too expensive. Many researches have been done trying to find out
the real SOX effect on companies’ value, but results are contradictory. The trigger for this
might be in difficulty to separate SOX effect from the other factors and in the diversity of
variables selected for the analysis, as the same features are represented by different ratios.
The main aim of this research is to investigate what effect SOX has on European companies’
corporate value.
Thesis objectives are following:
-
To evaluate the role of SOX in the light of market efficiency theory;
-
To summarize the results of previous researches which investigate the effect of SOX on
the companies’ value and their cross – listing decisions;
-
To develop the model for the analysis of SOX impact on the European companies’
value;
-
To evaluate the impact of SOX on the European cross – listed companies’ value,
considering capital, performance and risk factors.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Annual data for the period 2000 – 2010 of cross listed firms in New York (NYSE) and
London (LSE) stock exchanges will be analyzed. The sample will be divided into three parts:
NYSE cross listed companies before 2002 (SOX not yet applicable), NYSE cross listed
companies after 2002 (SOX applicable) and LSE cross listed companies (SOX not
applicable).
The main interest of the research is to find out SOX effect on corporate value measured by
Tobin’s Q ratio. For this purpose variables considered to have influence on firms’ value will
be selected. Factor analysis will be performed to create summated scales representing
coherent ratios, which will be used for the multiple regression analysis together with dummy
variable SOX.
The structure of the research will proceed as follows. The first part will summarize respective
literature. Then, the second part will trace with problem definition. The third part will include
sample description, variables analysis and methodological framework. In the fourth part the
empirical research findings will be presented, while in the fifth part they will be discussed and
synthesized with the reviewed literature, including limitations and implications of the
research. The sixth part will summarize the research and conclude.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
1. Literature Review
This part of the Thesis provides an overview of internal controls role in the financial markets,
also SOX background, including its contents and effective days. Further, the Act’s impact on
companies cross listing decision is analyzed. It also contains summary of previous researches
findings on SOX effect to corporate value. Table 1 presents summary of the main papers
further analyzed.
Table 1. Summary of papers reviewed
Paper
Aim of the paper
Ratios analyzed
Findings
Strict internal controls reporting
requirements have negative
value to securities market.
Tackett, J. A.,
& Claypool,
G. A. (2006)
To see what was the costs and
benefits of SOX section 404
(internal control disclosure and
management assessment).
Qualitative analysis of
costs and benefits
Duarte, J.,
Kong, K.,
Siegel, S., &
Young, L.
(2007)
To find out the effect of SOX by
examining the decision to cross
list in U.S. and local stock
market reaction to U.S. listing
announcements.
Number of delisted
companies, number of
IPO's, market
capitalization, dummy
SOX, industry dummy.
Piotroski, J.
D., Srinivasan,
S. (2007)
To investigate firms listing
behavior before and after SOX
in U.S. and U.K. exchanges.
Assets, sales, ROA,
book-to-market ratio,
leverage.
Litvak, K.
(2008a)
To analyze long term effect of
SOX (using a matched pairs
technology) to foreign cross
listed companies in US.
Tobin's Q, firm size,
risk, profitability, sales
growth, and GDP per
capita
Litvak, K.
(2008b)
To estimate how SOX affects
foreign firms risk taking.
Volatility of returns,
financial leverage, and
cash.
Bianconi, M.,
& Chen, R.
(2009)
Skaife, H. A.-,
Kinney JR, W.
R., & Lafond,
R. (2009)
To investigate SOX effects on
capital markets, ant to check
SOX effect on firms value.
To find out how SOX imposed
changes in internal control
process determine firms’
riskiness and cost of equity.
To study reasoning and
consequences of being listed in
NYSE and LSE; to check if
benefits have changed over
time.
Dummy SOX, assets,
GDP growth rate of
firms' source country.
Returns, cash flows,
leverage, size (equity),
dividends, book to
market ratio.
Tobin’s Q, sales
growth, total assets,
ownership, leverage,
and Standard Industrial
Classification.
Iliev, P.
(2010)
To find out the effect of SOX
section 404 while disentangling
it from other simultaneous
events effects.
Cash Flows, change in
net income, book to
market ratio, market
capitalization
Ittonen, K.
(2010)
To analyze investors’ reaction
to defects of internal controls
and to examine auditors’
material weakness reports.
Abnormal returns,
change of volatility,
systematic risk, size,
leverage, ROA.
Doidge, C.,
Karolyi, G. A.,
& Stulz, R. M.
(2009)
11
Increased management
accountability increased
investor’s value more than the
costs firms suffered because of
less concentrating on business
and innovation.
No arguments that the Act has
shifted incentives for foreign
firms to cross - listed in U.S.
Cross - listing premium
significantly declines after SOX
adoption. Foreign companies'
loses are bigger for those which
are subject to SOX.
SOX compliant companies risk
declines significantly; In
addition, negative effect was
bigger for riskier companies.
For cross – listed companies
positive SOX effect is observed.
Companies where internal
control deficiencies were found
have significantly higher risk
and cost of equity.
Before SOX foreign companies
cross - listed in U.S. have had
16.5% higher Tobin's q ratio.
Also, cross - listed firms have
bigger growth opportunities.
Significant increase in firms’
indirect costs, also lower
discretionary earnings for both
U.S. and foreign firms are
observed.
Increase in volatility changes
and positive investors reaction
is observed after disclosure of
material weaknesses.
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
1.1
Internal Controls and SOX Background
The efficient market hypothesis implies share prices incorporate all available information, and
“ideal is the market in which prices provide accurate signals for resource allocation” (Fama,
1970). However, behavioral economists contradict, naming information bias as one of the
reasons for market imperfection. The Information Technology bubble in early 2000’s and the
Housing bubble in late 2000’s reactivated discussions on the market efficiency. Eugene Fama
himself replied “the financial markets were a casualty of the recession, not a cause of it”
(Cassidy, 2010).
The market efficiency cause investor’s confidence and increase number of participants, what
implies better credit availability, an increase in investments and overall economy growth.
However, if markets were efficient itself, no regulations would be needed. Financial market
regulation is a tool for equalizing information among investors, and the more transparent
information is available, the more efficient markets are. Regulative laws set requirements for
market participants to provide all relevant information, and is a part of companies’ internal
controls. The Committee on Sponsoring Organizations of the Treadway Commission (COSO)
defines internal controls as “a process, effected by an entity's board of directors, management
and other personnel, designed to provide reasonable assurance regarding the achievement of
objectives”, one of which is “compliance with applicable laws and regulations” (“Internal
Control Integrated Framework,” 1992).
A number of large corporate and accounting scandals in 2000 – 2002 (Enron, WorldCom)
were revealed, resulting in trillions of dollars losses for investors. This was a warning sign to
U.S. Congress, as Enron could be, and in fact was, not the only case. There were many large
public companies that had liquidity problems and a very high debt to equity ratio at that time.
Moreover, it was still increasing, whereas being a signal of increasing corporate risk.
Of course, firms like Enron had to violate some accounting rules in order to hide their
liquidity problems and other financial difficulties. Most likely this would not have happened
if auditors had not supported these misconducts. In late 2001 Security Exchange Commission
(SEC) started a fraud case against Arthur Andersen what was the biggest audit failure in the
world at that time.
In response to accounting and audit scandals, United States Congress in 2002 passed a new
law - Public Company Accounting Reform and Investor Protection Act (official name in
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Senate), or Corporate and Auditing Accountability and Responsibility Act (official name in
House of Representatives) – commonly known as the Sarbanes-Oxley Act, those main
purpose is straightaway stated in the title “to protect investors by improving the accuracy and
reliability of corporate disclosures made pursuant to the securities laws” (Sarbanes & Oxley,
2002). SOX played an important role in the United States (U.S.) stock market legal
environment, as it was the first law regulating Securities Industry since the middle of 20th
century (U.S. Securities and Exchange Commission, 2002). The Act definitely had a huge
impact not only on U.S. businesses (all U.S. public firms must be SOX compliant), but also
on international capital markets, as it is applied to foreign companies cross listed in any U.S.
stock exchange too. Moreover, following SOX other countries like Canada, Japan, Germany,
Australia, India, Turkey and Italy released similar laws for the local financial market
regulation.
1.2
Sarbanes-Oxley Act Content and Effective Dates
Although initial SOX act was just a few paragraphs long law, it was developed to a set of
requirements consisting of 11 titles, each of those including several sections. Hereafter
presented a short summary of SOX content with the most important effective days taken into
account.
According to the Act’s title section, non-governmental Public Company Accounting
Oversight Board (PCAOB) was established, as it was necessary to have a regulatory
institution for this large and complex Act implementation and further control. The main
purpose of PCAOB is to oversee auditors of public companies (all public audit or accounting
firms, that are intended to perform audits for companies which are subject to SOX have to
register with PCAOB), while SEC continues to oversee listed companies.
The second title of SOX act defines basic roles of auditors, emphasizing the importance of
their independence from clients. Section 201 was famous by prohibiting some activities to
audit companies (book keeping, internal audit, management services, etc.) they were used to
do. This diversification has been done in order to avoid conflict of interest and to lessen fraud
possibilities.
The third title refers to corporate responsibility for financial reports, and obliges the executive
officers to confirm on the financial statement correctness and accuracy. This was considered
as a score of increased indirect costs, as a chain of approvals to comply was created (top level
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
management needs to get approval from lower level managers for their responsibility area,
who need to get approval from their subordinates, and so forth). This resulted in a huge time
spending on reporting and meetings for control issues. However, section 302 is considered to
increase ethical culture, due to each employees increased responsibility for actions taken, and
CEO and CFO taking ownership of financial statements.
Title four covers disclosure of financial statement and internal control process. Probably the
most famous SOX section is 404 – Management Assessment of Internal Controls. The main
purpose of it is to reduce corporate fraud possibility, by establishing internal controls and
management assessment process, which should ensure correctness of financial reporting.
Most companies admit they even had to increase head count or use external service providers
to create the structure of internal controls that satisfy SOX requirements. Moreover, even
more employees were needed to perform these controls, whereas according to the Act every
transaction has to be properly documented (saved as evidence). This section was followed
with abundance of discussions, yet because compliance of this part is extremely expensive.
The next titles of the Act define SEC responsibility and authority, also minimum requirements
for attorneys. These parts of SOX are for legal and public regulation purposes and do not
cover this research field of interest, thus will not be further detailed.
The last titles indicate criminal penalties for everybody with an intention to mislead or falsify
any record in any document. In fact, legal responsibility was not innovation of SOX, it just
increased from 10 years of imprisonment and $1 million fine to 20 years and $5 million
respectively. Also, criminal penalties and fines were specified for companies CEO and CFO,
emphasizing their personal responsibility. It is considered that this section might cause more
cautious and less risky corporate governance decisions.
For majority of SOX sections the effective dates of compliance were from the second half of
2002 to the first half of 2003, except section 301 (Public Company Audit Committees) which
was effective since 31st July 2005 and section 404 (Management Assessment of Internal
Controls) – effective since fiscal year ending after 15th July, 2006.
1.3
SOX Impact on non U.S. Companies Cross – listing Decision
The Act, since it was announced, attained large number of economical, political and legal
discussions with a largely differentiating opinion on the overall effect and necessity. Even
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
president Georg W. Bush said SOX was “the most far-reaching reforms of American business
practices since the time of Franklin Delano Roosevelt.” (Signing Statement of George W.
Bush, 2002). However, others argued there was not much innovation in the Act, thus even
before accounting scandals revealed, there were existing civil and criminal laws for fighting
against the fraud, and maybe the problem was not in the lack of regulation, but in the lack of
implementation on them.
The time period before SOX is characterized with by Dot-com bubble, economy growth, and
increasing corporate profitability. The earnings management became more and more popular
phenomena, as management were pressed to meet profit targets, by receiving stock-based
bonuses. This way both, shareholders and investors were satisfied with compensations they
get. Not surprisingly, the evaluation of risk taken was not a priority. Cohen, Dey, & Lys
(2005) analyze earnings management, by testing “opportunistic behavior hypothesis”, saying
that compensation and incentives paid to managers have impact on accounting practice they
choose. Author’s point that the ratio of change in accounts receivables or inventory compared
to changes in sales, also frequency of special items reported increased dramatically before
Sarbanes - Oxley Act (1997-2002), and decreased significantly just right after the passage of
the Act. Another piece of evidence for risk growth, is huge rise in accounting restatements –
U.S. General Accounting Office report (2002) shows number increased dramatically, what
could be one of the reasons for decreased investor’s confidence.
Auditor’s conflict of interest was a supplement - Levitt (2002) points auditors became more
inclined to ignore fraud of their clients, as their compensation increased with additional
consultancy services companies bought. In addition to this, competition among audit
companies increased, which led to reduction of fees clients paid, lower salaries, and lower
knowledge and skills of employees (Shelton & Whittington, 2008). Naturally, audit
companies became much more dependant from their client bounty, and therefore less
attentive. Still, a lack of regulations could be a pretext and not the cause. Tackett, Wolf, &
Claypool, (2006) propose that no new rules were needed for audit requirements, as it would
have been enough to apply correctly the existing ones, such as Generally Accepted Auditing
Standards (GAAS).
The Act for sure had an impact on U.S. stock exchange’s competitiveness, as imposed strict
regulations, which simultaneously were very expensive, could not be attractive to business. In
fact, in the next few years after SOX passed there was an increase in number of NYSE
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
delisted companies, while at the same time LSE listing increased (see Appendix 1). Moreover,
at the same time number of initial public offerings (IPO) in NYSE significantly reduced,
whereas in LSE enlarged. However, going to deeper analysis, one can notice that numbers for
LSE Main Market (market for largest, successful and well-known companies) move very
similar direction to NYSE. The growth is observed only in LSE Alternative Investment
Market (market for smaller, fast growing companies). Piotroski & Srinivasan, (2007) find out
that SOX was not the only consideration influencing companies cross - listing decision
between U.S. and U.K. Exchange specific feature, firms size, profitability and registration
country are also significant factors determining costs and benefits of being listed in particular
exchange.
Doidge, Karolyi, & Stulz, (2007) find substantial governance benefits for firms cross listed in
U.S., while being listed in U.K. does not provide any premium. U.S. cross - listing
requirements improved corporate governance by reduced controlling shareholders ability to
extract private benefits. As a consequence, funds were used to increase investment, resulting
in increased growth opportunities. Authors find that resources saved most probably cover
cross-listing costs. An additional finding is stock price increase for firms which choose to be
cross listed in U.S. Similar results were presented by Berger, Li, & Wong (2005) – they
showed that stock market reaction to companies from countries with weak private investor
rights was more positive after SOX, and investor’s protection had a positive impact on firm
value. Another research supporting previous findings was done by Duarte, Kong, Siegel, &
Young, (2007). They conclude that increased management accountability increased investor’s
value more than the costs firms suffered because of less concentrating on business and
innovation.
In a contrast to this, Zolnor (2009) states that investors do not pay attention to information
provided according to SOX 404 section, therefore no benefits from the Act could be found,
simply costs of overregulation. Supporting the results, La Porta, Lopez-De-Silanes, & Shleifer
(2006) point that not all securities laws work. Researchers find that laws defining public
enforcement (such as having an independent regulator or criminal penalties) do not benefit
financial markets. On the other hand, they show that law matters, and that securities markets
cannot be left for self-regulation, but important is only legal acts mandating disclosure and
liability rules. Authors provide indices of securities law variables for 49 countries, including
disclosure requirements, liability standards, rule-making power of the supervisor, criminal
sanctions applicable to directors, distributors, and accountants and public enforcement, and
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
conclude that U.S. securities market is more regulated by all listed variables compared to
U.K. However, in a context of all countries U.K. still seems to be a country with high level
controls (have all variables higher than mean, except criminal sanctions).
U.K. financial markets are regulated by U.K. Listing Authority (UKLA), which is a division
of the Financial Services Authority (FSA). UKLA is responsible for listing applications
approval and admission, also for monitoring and ensuring companies’ compliance with
regulations applicable. The main regulation for securities market is UKLA’s non-statutory
Combined Code of Corporate Governance, which is kind of equivalent to SOX.
In a past couple years there appeared discussions about proper degree of investor’s protection
and operational effectiveness of listing requirements resulting in suggestions that changes of
tightening regulations are needed. In January 2012 UKLA passed a consultation paper
“Amendments to the Listing Rules, Prospectus Rules, Disclosure Rules and Transparency
Rules” proposing changes for reverse takeovers, sponsors, transactions, financial information
and externally managed companies regulation (Financial Services Authority, 2012). The main
objective of proposed changes is to maintain and strengthen integrity and competitiveness of
U.K. financial markets. The consultation period closes at the end of April, and FSA expects
the new rules to become effective still in 2012.
If changes will be adopt the way it is proposed, companies listed in LSE will face a huge
increase in internal controls documentation requirements. Critics already discuss on increased
future costs and pure benefits, while upholders say changes are necessary. However, situation
appears to be similar to the one that happened in U.S. ten years ago.
1.4
SOX Effect on Corporate Value: Benefits and Compliance Costs
Majority agrees on SOX positive effect on investors’ confidence and strengthening trust in
capital markets. However, benefits to companies (especially foreign ones), and effect on
corporate value are still under discussion.
Most researches use Tobin’s Q ratio to measure companies’ value (Bauer, Guenster, & Otten,
(2004), Doidge, Karolyi, & Stulz, (2009), Litvak, (2008a) and others). This ratio was
developed by James Tobin in 1969 and is counted as the ratio between market value and
replacement costs of assets (book value). There are several reasons for Tobin’s Q being used
frequently: it requires just basic accounting and financial information, also explains number of
17
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
important financial indicators, like investment and diversification decisions, relationship
between managerial equity ownership and firm value, also financing, dividends and
compensation policies (Kee H. Chung & Stephen W. Pruitt, 1994).
Taken a deeper look, SOX compliance has had a dual effect on corporate value. From one
side it reduces value by implying huge direct costs of implementation and controls
performance (decrease in cash flows and liquidity) also management concentration on
requirements fulfillment rather than doing business (possible decrease in growth of sales). On
the other hand, strengthened corporate governance reduces private benefit possibilities, what
increases investors’ value (increase in ROA, ROE) and determine lower risk taking (Litvak,
2008b). This leads to opposite side impact and discussions on overall SOX effect. Further
described different author’s findings on main SOX affected variables.
Ahmed, McAnally, Rasmussen, & Weaver, (2010) analyzed both direct and indirect SOXrelated costs, such as managers and executives focus on implementation and performance of
the controls, rather than doing business. Authors find the statistically and economically
significant decrease in cash flow and profit (considering that macroeconomic environment or
company specific issues can have effect either), what is average reduction of assets (1.3 %)
and revenue (1.8 %). In general, the total amount of yearly SOX costs ($6 million for small
and $39 million for large companies) significantly differs to the ones ($0.91 million), which
was estimated by Congress in advance of enactment. What is more, different SOX effect
according to firm size, complexity and growth opportunities was shown. Researchers also find
that cost of SOX was much more significant to small firms, the more complex ones and with
lower growth opportunities. No evidence on compliance costs to be different for foreign
companies was found.
Bianconi & Chen, (2009) find that even though SOX had a consistent negative effect on
firms’ value, considering cross listing decision, positive SOX effect is observed. Similarly,
Duarte et al., (2007) point that SOX increased foreign companies value on average by 2%,
while increase for U.S. companies was even higher. However, Doidge, Karolyi, & Stulz
(2004) find a negative effect and calculate that before SOX foreign companies cross-listed in
U.S. had a 16.5% higher Tobin's Q ratio.
Litvak (2007) finds SOX had a negative impact on stock price, however he verifies negative
effect was much bigger for riskier companies. For the research, author used matched pairs
methodology – firms were put together taking into consideration size, profitability, leverage,
18
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
volatility and industry. The comparison between cross-listed and non-cross listed companies
has been done according to similar factors and location in the same country. A significant
decrease in cross-listing premium for foreign countries, which are subject to SOX, compared
to those that are not subject to SOX was noticed. Further analyzing SOX affected companies,
Litvak takes three measures - financial leverage, volatility on returns and cash. He finds that
for SOX compliant companies risk declines significantly in assumed variables, and this could
be a reason of stock price decrease for riskier firms.
SOX effect on riskiness and investments was also analyzed by Kang & Liu (2010).
Researchers find smaller and riskier firms became more careful in their spending decisions
after SOX, thus investments level in general decreased. Also, authors noticed that executive
managers of U.S. companies apply bigger discount rate to investment projects after SOX,
while considering executives of U.K. companies’ behavior, it remains unchanged. Thus
findings of the paper suggest SOX had negative effect on corporate investments in U.S. listed
firms, and decrease of riskiness and altered attitude to applied discount rate for investments
could also be a consequence of robust SOX requirements and enhanced manager’s
accountability. On top of this, surprising results were found by Ittonen (2010), as investors
react positively to material internal control weakness announcements, meaning these
companies are more trust worthy.
Carney (2006) propose that no matter how good regulations are, it is still impossible to
prevent all potential fraud, and being rational, nobody would want this, because of extremely
high costs. Author points, that “the goal should be to keep spending on fraud prevention until
the returns on a dollar invested in prevention are no more than a dollar”. Even congressman
M. Oxley during his speech in a few years after the Act presented, admitted it was “far too
bureaucratic”, especially while talking about foreign companies.
Conversely, Alan Greenspan during his speech gave a praise saying “The Sarbanes-Oxley Act
of 2002 appropriately places the explicit responsibility for certification of the soundness of
accounting and disclosure procedures”, as the principle is that corporate managers should be
working on behalf of shareholders to allocate business resources to optimum use (Greenspan,
2005).
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
2. Problem Definition
Looking back a decade, at the time when major corporate and accounting scandals were
disclosed, there was a clear sign that something is wrong with existing regulations, or at least
application of them. After SEC promulgated the SOX act, plenty researches were done on this
topic arguing between diverse beliefs. Ones upheld the opinion of overregulation, saying that
no new restrictions were needed, and that it is enough to apply correctly the valid ones.
Meanwhile, the others supported the new tightened rules, noting it was a proper step taken to
recover investors’ confidence, stock exchange competitiveness and to improve financial
markets efficiency.
Few years later further researches, to measure the costs and benefits of SOX, were done.
However, analyzing the Act’s effect on corporate value (in most cases measured by Tobin’s Q
ratio) contradictory results were found. Variables investigated also differs among researches –
return on equity (Bhagat & Bolton, 2009), market-to-book ratio and free cash flow (Wintoki,
2007), return on assets (Piotroski & Srinivasan, 2007), capital expenditures (Doidge et al.,
2009), cross-listing premium (Bianconi & Chen, 2009), company size measured by total
assets (Cai, Liu, & Qian, 2009), by equity market value (Kang & Liu, 2010), leverage (Litvak,
2008a), sales growth (Carney, 2006), net profit margin (Bauer et al., 2004), stockholders
returns (Bebchuk, Cohen, & Ferrell, 2004) and cash flow (Ahmed et al., 2010).
Despite different results of the overall effect of SOX to companies’ value, the majority agreed
it was very expensive or at least much more expensive then it was expected. Still, many big
European companies choose to be cross listed in U.S., most likely finding that cross listing
premium exceeds the compliance costs.
The main purpose of this research is to find out the effect of SOX to European companies’
corporate value, by constructing data sample of European companies cross listed in NYSE
and LSE in the period before and after SOX. The most frequently analyzed variables – Net
profit margin, return on equity of current and previous year, return on assets, capital
expenditure, total assets, dividend yield, sales growth and leverage will be summarized into
common variance factors to identify possible difference in corporate value for NYSE and LSE
cross listed firms.
Hypothesis of the paper is as follows. There is significant relationship between the effect of
SOX act, European companies’ corporate value and factors representing summarized
variables.
20
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
3. Methodological Approach
This part of the research contains the methodological approach description used to find out
the effect of SOX to European companies value. It includes the description of sample for
SOX applicable and not applicable companies’ selection, also the ratios to be used as
independent variables choice and calculation. Further, relevance and description on selected
approaches is provided. The Thesis methodological approach design is shown in Figure 1.
Research
problem
Data sample
definition
Variables
selection
Statistical data
consideration
Multiple linear
regression
Factor matrix
extraction and
validation
Parallel analysis
/ number of
factors
determination
Estimating
assumptions on
Factor analysis
Regression
transformation
Hypothesis
testing
Figure 1 Methodological approach estimating assumptions
3.1
Data Sample Selection and Description
As it was discussed in the Literature Review part, one of the major problems identifying SOX
effect is separating Act’s impact from other variables. Thus, a comparable sample of SOX
applicable and not applicable firms is needed. Firstly, sample set of SOX applicable firms
were selected. The Act had different effect on small companies, therefore data sample is
constructed from the largest U.S. stock exchange - NYSE cross listed European companies
(for these SOX is applicable since 2002). At this point the control set of sample for SOX not
applicable companies was constructed from largest Europe stock exchange – LSE cross listed
European companies. Firms were selected by matching them according to size (largest only),
age (listed not later than 2000) and industry sector.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
3.1.1
Data sample selection
For a starting point a list of all EU firms cross listed in NYSE was taken. Afterwards,
companies listed in Arca, Euronext, Alternext and Amex directories were excluded, as these
are for small or growing companies, and yet changes in financial ratios are more possibly
affected by firm specific reasons. After reduction there were 82 companies from 16 EU
countries on a list. Taking the next step, financial sector enterprises (banks, insurance
companies, etc.) were removed, as valuation ratios of these firms usually are not comparable
to non-financial ones. Another selection criterion was companies’ age. Although some
researches (Bauer et al., (2004); Bebchuk et al., (2004)) consider firms age as a variable
influencing corporate value, sample contains only firms cross listed before 2000. This has
been done to avoid bias of data missing, and yet means selected firms have at least 11 years of
operation, also secures from including young and fast growing firms. Final list for data
collection contained 49 companies from following industry sectors: Basic Materials,
Consumer Goods, Consumer Services, Health Care, Industrials, Oil & Gas, Technology,
Telecommunication and Utilities.
To counterweight the constructed SOX applicable set, otherwise similar set of SOX non
applicable companies was needed. Data selection method was chosen following Kate Litvak.
He did several researches (2007, 2008a and 2008b) to investigate SOX impact, using sample
construction of matched pairs of cross – listed companies according to country, size and
industry (where one companies were subject to SOX and another were not). This way the
effect of the Act is isolated and broader economic and political trends are managed.
For control sample selection list of EU firms cross listed in LSE Main Market was taken.
After check of data availability for the year 2000 – 2010, LSE Alternative Investment Market
companies were excluded for the same reason as NYSE other directories. Afterwards the list
was sorted by activity field, selecting firms operating in the same industry, sector and even
subsector (where applicable). Further sampling aim was to find most similar company pairs
by size, where market capitalization was used as parameter. Country criterion was excluded,
as on a country level it would be impossible to find similar industry and size company pairs.
This should not result in bias, as Thesis analyze only EU companies, therefore assumption on
similar legal and economic environment was done.
The result is 49 paired companies from largest U.S. and EU stock exchanges with financial
data available for 2000 – 2010. Data sample construction is illustrated below in Figure 2. For
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
NYSE cross listed companies SOX is applicable since 2002, therefore sample falls in 98
observations before SOX (2 year observations for 49 companies) and 441 observations after
SOX (9 year observations for 49 companies), while LSE sample set contains 539 observations
(11 year observations for 49 companies) for SOX not applicable firms. The total sample
consists of 1078 observations.
Figure 2 Data sample construction
The final list of selected firms, with indicated sector and subsector is presented in Appendix 2
(NYSE cross – listed) and Appendix 3 (LSE cross – listed). It contains similar size matched
pairs from following sectors:
-
Basic Materials - 5 matched companies;
-
Consumer Goods - 8 matched companies;
-
Consumer services - 5 matched companies;
-
Health Care - 6 matched companies;
-
Industrials - 6 matched companies;
-
Oil & Gas - 7 matched companies;
-
Technology & Telecommunications - 10 matched companies;
-
Utility - 2 matched companies.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
3.1.2
Data sample description
For historical financial data collection Bloomberg terminal was used. Following Balance
Sheet and Income Statement lines’ items for selected companies were extracted: Cash Flows
from operations, Dividends, Capital expenditure, Earnings before interest and tax, Net
income, Operating expenditure, Revenue, Non current liabilities, Current liabilities and Total
assets. Market capitalization data was extracted from stock exchange reports, and is calculated
as a sum of all classes common stock. If there is more than one common stock class and only
one of them is listed, share price for the year end is applied to unlisted class to determine total
market value. In case two or more common share classes are listed, average share price is
applied to unlisted class.
All extracted financial data is in currency in which security was issued, and not in which it is
traded or pays dividends. Thus, all NYSE sample set is in U.S. Dollars, and LSE set in British
Pounds. As for further analysis ratios are calculated, there is no need for currency conversion.
3.2
Variables Selection and Calculation
Trying to identify SOX effect, another major problem discussed in the Literature Review part,
is numerous different variables analyzed. To solve this issue, most frequently used variables
influencing corporate value from different researches were identified and taken for further
testing.
Following Bianconi & Chen, (2009), Cai et al., (2009) and Gompers & Ishii, (2003),
corporate value is measured by Tobin’s Q ratio, or as a sum of the market capitalization and
total liabilities, divided by the total assets. In general, Tobin’s Q ratio shows two different
valuations of the same assets: numerator is the current market value of exchanging existing
assets and denominator is replacement costs of the same assets (Tobin & Brainard, 1977).
This is the most accurate and widely acceptable way to measure corporate value for such a
large data sample.
Below presented brief description of SOX and other variables (grouped in the way to
represent firms capital, performance and risk), which reviewed literature suggests should have
had impact to firms’ corporate value.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
3.2.1
The overall SOX effect on variables analyzed
Different opinions and valuation of SOX imposed costs and benefits have already been
discussed in previous parts of research. To generalize, below is provided summary and
illustration (see figure 3) of presumable Act’s impact to firms’ value, considering capital,
performance and risk factors.
Leverage
Tangibility
Size
Dividend yield
Cash flow
Liquidity
CAPEX
Sales
Net Profit Margin
ROA
ROE
Figure 3. SOX impact to corporate value in terms of capital, performance and risk factors
Although Piotroski & Srinivasan, (2007) analyzing SOX effect to variables, representing all
three factor categories (liquidity, ROA and leverage) find no changes in non U.S. companies
incentives to cross list in NYSE, most of other academic papers does.
The most apparent effect of SOX is to cash flows: from one side additional requirements
(especially of section 404) dramatically increased indirect costs, while in a long run it should
lead to financial reporting process automation and centralization, what means increase in
efficiency and costs reduction.
Considering SOX impact on corporate performance, majority agrees that it depends on the
performance before the Act. Companies which board structure was not in line with SOX were
“forced to deviate from their optimal governance system” (Cai et al., 2009), and had negative
impact, while companies which board structure corresponded to imposed requirements
already before SOX, experienced more positive effect. However, the Act definitely had
impact on manager’s decisions effectiveness and transparency.
Companies CEO and CFO by section 302 were forced to take personal responsibility for the
financial statements, and this could not remain without consequences on the risk taken.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Litvak, (2008a) confirms it, finding negative SOX effect for higher leveraged and therefore
riskier firms was bigger.
Overall, SOX effect on corporate value can be best seen when analyzing in terms of other
influencing factors. Therefore, for further analysis dummy variable (DUMMY_SOX) was
created to exclude SOX effect. It takes value 0 for all LSE cross listed companies and for
NYSE cross listed companies before 2002, and value 1 for NYSE cross listed companies after
2002.
3.2.2
SOX effect to companies capital
The first group of variables considered to have impact on corporate value is named Capital,
and summarizes liquidity, cash flow, CAPEX and sales growth. Basically it represents funds
which can be employed for income generation or investment. The main financial statement
items used for ratios calculation are cash flows from operations, sales and capital expenditure.
It is definite that SOX imposed both, direct and indirect costs to companies. Direct costs
firstly were reflected in decreased cash flows just after enactment. Ahmed et al., (2010) find
that on average cash flows decline by 1.3 percent of total assets, also that costs were more
significant for complex, smaller and lower growth opportunities firms. However, plenty
researches show significant decrease in cash flows just in a short term, and a reasonable
explanation for it could be high implementation costs, while after some years costs decreased
due to compliance process optimization.
On the other hand, in a long time perspective SOX should have had a positive impact on cash
flows, as better governance should result in better business decisions and reduced
management private benefits extraction ability, what means increase in revenue.
Although there are several ways to define Cash flow ratio, in this research it was calculated
as a scale of cash flows from operations over total assets.
In general, Liquidity ratio can be defined as an ability to meet short term liabilities or simply
pay off creditors timely. There are several ways to measure liquidity ratio, which have
different interpretations. The quick and very often used is a ratio of short term assets to short
term liabilities. Though, in case of urgency, usually it is hard to sell asset at its full value,
26
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
therefore more precise way to calculate liquidity is Quick ratio. The difference comes from
deducting inventories, accounts receivable and prepaid expenses from short term assets and
then dividing by short term liabilities. This is more accurate way to estimate firms’ ability to
pay debts, as it includes only ready to sell assets. However, meeting current liabilities through
cash flows is even a better solution rather than asset sale, thus liquidity ratio was defined as a
cash flow from operations scaled to short term liabilities.
Another ratio should be considered is Capital expenditures (CAPEX). Following Gompers
& Ishii, (2003) the ratio was calculated as capital expenditures scaled by sales.
In general, CAPEX is companies resources used either to acquire fixed assets or to add value
to existing assets by upgrading its features or extending useful life. In any case, this type
expenditure should increase firms’ benefits in the future.
The amount of funds particular company allocates for capital expenditure depends not only on
individual specific, but also on industry it operates (most capital intensive –
Telecommunications, Oil & Gas and Utility sectors) and managerial decisions. Classical
literature (Williamson, 1964) suggests that increase in capital expenditure not necessary mean
company gains – managers may accept inefficient projects in order to get private benefits.
Gompers & Ishii (2003) analyzed relationship between capital expenditure and corporate
governance, and provided empirical evidence that firms with stronger shareholders rights had
lower capital expenditure and higher corporate value at the same time.
Sales growth, or change in sales over a time period is highly associated to firms’ value and
profitability, and can be used as a measure for growth opportunities. Following Litvak
(2008b) sales growth was defined as a two-year geometric average of annual growth in sales.
Increased sales growth opportunities, as a consequence of better corporate governance, for
companies cross listed U.S. were found by Doidge et al. (2004).
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
3.2.3
SOX effect to companies performance
The second group of variables considered to have impact on corporate value is named
Performance, and summarizes Return on Assets (ROA), Return on Equity (ROE) of current
and previous years and Net profit margin. Basically it represents corporate governance, or
evaluation of management activities to achieve business goals. The main financial statement
items used for ratios calculation are Net income, Assets, Shareholders equity and Sales.
In general, all these ratios show company’s operating performance and return to shareholders.
Naturally, higher profitability increase firm value, therefore a positive relationship between
performance factor and Tobin’s Q is expected.
Most of researches suggest SOX was beneficial in corporate governance improvement.
Gompers & Ishii, (2003) find a positive relationship between stronger corporate governance
ant profits. In addition, Doidge, Karolyi, & Stulz, (2010) use ROA as a measure of accounting
performance, and find a significant positive correlation of this ratio with sales growth and
Tobin’s Q. Yermack, (1996) showed that there exists statistically significant relationship not
only between firm valuation and current performance, but also between previous performance
too. This is because firm’s value calculation is mainly based on Balance Sheet items, which
are cumulative and incorporate past results either. Notably, the results found are very similar
to the ones presented by Gompers & Ishii, (2003). However, some contrary conclusions were
made by Bauer et al. (2004), as they find positive relationship between governance and firm
value only for companies with lower governance standards.
Net profit margin measures corporate profitability and is usually calculated as a ratio of
profit and sales (revenue):
Similarly to net profit margin, ROE is considered as an earnings based operating performance
ratio. It is calculated as a rate of net income to shareholders’ equity, and basically shows how
much of the generated profit each unit of shareholders’ equity gets.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
ROA shows the return on invested capital (assets) and is one more way to measure corporate
performance. Return on assets was calculated following:
This is a parallel case to net profit margin and ROE, as all ratios calculation is net income
based.
3.2.4
SOX effect to companies riskiness
The third group of variables considered to have impact on corporate value is named Risk, and
summarizes company size, Leverage, Tangibility and Dividend yield. In general it represents
companies’ debt level and borrowing possibilities. The main financial statement items used
for ratios calculation are Total assets, Long term Liabilities, Current assets, Market
capitalization and Dividends paid.
Dividend payout reduces firm’s cash flow, and so causes the decrease in assets which could
be used for investment. As a consequence, external funds are needed for investment and
company growth. According to Rajan & Zingales (1995), sufficient amount of tangible assets
enables firms to get external financing easier and at a lower interest rate, as they can secure
the loans by using tangible assets as a collateral. Naturally, it encourages companies with high
level of fixed assets to borrow more. On the other hand, firms with high tangible assets ratio
are usually big ones, and they can issue equity at fair price compared to the low tangible
assets level firms, which have information asymmetry problem resulting in issuing more
underpriced debt (Harris & Raviv, 1991). Skaife, Collins, Kinney JR, & Lafond, (2009) found
that companies with higher level of internal control deficiencies and leverage have
significantly higher risk what results in higher cost of equity.
High leverage ratio usually refers to a higher risk of the company, as it increases possibility to
run off gains and losses. Litvak (2008a) found SOX have had more negative effect on riskier
firms, also that firms which were riskier before SOX lost more of their value (Litvak, 2008b).
This is possibly a consequence of strengthened requirements and accountability.
Leverage describes capital structure of a company, in other words which part of firms’ assets
is financed by debt. There are many ways to measure leverage, but the main difference
between those is whether debt is compared to market or book value of company. In this paper,
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
following Rajan & Zingales (1995), leverage was calculated as ratio between long term
liabilities and book value of firm.
Although there are few ways to measure firm size: by market value of equity (Kang & Liu,
2010) or sales (Carney, 2006), in this research following Bhagat & Bolton, (2009), Cai et al.,
(2009) and Piotroski & Srinivasan, (2007) total assets were used. Natural logarithm of actual
values was taken, as this helps to reduce scale to more manageable.
Size = ln ( Total Assets )
Size is important variable to analyze in terms of SOX, as most of compliance costs are fixed,
and should not differ a lot among small and large firms. There are no exceptions or facilitation
for small companies, therefore the effect of the Act is different and much more significant for
them (Kang & Liu, 2010). Thus, Litvak, (2007) point reaction of investors to large companies
was more favorable than reaction of small companies’ investors.
Dividend yield is a financial ratio presenting the yearly amount of dividends paid compared
to a stock price, and was calculated following:
Lastly, Tangibility is denoted as a ratio of non current assets to total assets, and is calculated
following:
In consistent with theory, risk related ratios are expected to be negatively correlated with
corporate value.
3.3
Statistical Considerations for Factor Analysis
To explore how defined explanatory variables affect the dependant variable Tobin’s Q,
multivariate statistical techniques should be used. The simplest way to perform such analysis
is multiple linear regression. However, model with 13 independent variables (12 observed and
30
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
dummy variable SOX) is not representative and the effect of SOX could be hardly identified,
thus better understanding of structure and interrelationship between variables is needed.
One of the dimension reduction models is Factor analysis, which is basically used to see if
there exists the relationship between large number of variables, and whether these variables
can be summarized into smaller groups – common factors.
The purpose of factor analysis used in this research is to reduce data sample by creating new
variables (summated scales) which would have the same features, and could replace the
original ones. To make sure factor analysis can be performed, certain assumptions should be
considered:
-
Sample size – the minimal requirement is 10:1 ratio, but the same researchers even
propose 30:1 cases per item (this means, that for example if there is 12 variables,
ideally it should be not less than 360 observations);
-
Statistical issues – normality and linearity needs to be considered;
-
Correlation – there should be at least some variables with correlation greater than 0,3
and correlations should not be equal (this mean grouping variables into a structured
factor is possible). Another way to perform correlation check is by evaluating partial
correlations among variables. For this purpose SPSS (statistical analysis software,
used to conduct this research empirical part) provide Anti-image correlation matrix,
where diagonals should be not less than 0.5, and preferable 0.7.
-
Bartlett test of sphericity shows if there is a statistically significant correlation among
at least some of the variables in the correlation matrix (absolute value of this test is
not influential, but it is important to check the result to be statistical significant).
-
Measure of sampling adequacy checks if intercorrelation level between variables is
appropriate for factor analysis. In SPSS Kaiser Meyer Olkin Measure of sampling
adequacy is used, and should take value not less than 0,5 (though the larger, the
better).
If the data sample meets the requirements, the next step before performing factor analysis is
selection of factoring method to decide which part of variance for variables needs to be
presented. In statistics, variance is defined as a measure of the dispersion of values from its
mean. There are three types of variance in a sample defined. The first one is shared and
appears between correlated variables, as they have shared variance equal to squared
31
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
correlation. The other two types of variance are the unique variance, which differs for each
variable and error variance, which can appear due to data collecting or measurement errors.
Either Principal Component or Common Factor analysis should be chosen depends on the
variance desired to be analyzed. Principal Component analysis includes total variance and is
usually used to summarize original variance of variables into smaller number of factors for
prediction purpose. Contrarily, Common Factor analysis is used when interest is only in
variance shared between variables. As Hair, Black, & Anderson (2006) suggest, common
factor analysis is more relevant when:
-
the initial objective is to identify the latent dimensions or constructs represented in
the original variables, and
-
there is little knowledge about the amount of specific and error variance and
therefore it would be better to eliminate these variances.
Relying on these arguments, with a primary aim to identify common factors best reflecting
original ones, and due to large sample size limited knowledge of individual variance, common
factor analysis will be applied.
3.4
Parallel Analysis
After decision on factoring approach is made, the next important step is defining the number
of factors to be retained. Some researchers argue that defining number of factors is even more
important than decision on which type factor analysis to apply. Hayton, Allen, & Scarpello
(2004) says factor analysis “needs to balance parsimony with adequately representing
underlying correlations, so its utility depends on being able to differentiate major factors from
minor ones (…), also there is conceptual and empirical evidence that both specifying too few
factors and specifying too many factors are substantial errors that affect results”.
Although there are many ways suggested in literature to decide on how many factors one
should extract, the most popular one is based on Latent Root (Eigenvalue) criterion. Hair et
al., (2006) explains that “the rationale for the latent root criterion is that any individual factor
should account for the variance of at least a single variable if it is to be retained for
interpretation”. This simply means that only factors with eigenvalue higher that one will be
extracted. However, this method attained a lot of critics, including Zwick & Velicer, (1986)
arguing this is less reliable method. Authors analyze five rules for determining number of
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
components, concluding that Parallel analysis and MAP methods were the best in all
situations analyzed.
Parallel analysis in general is based on Monte Carlo simulation approach, generating a data
sample consisting of the same number of variables for the same sample size, just with random
numbers. Eigenvalues for this made-up data set is created, repeating the process 1000 or even
5000 times. Afterwards, the derived eigenvalues are compared to the one observed from the
real data, and factors indicating higher value are considered to be worth extracting. However,
if for some factors the true values are very slightly above the derived ones, this could be a
warning sign, as it might be not statistically significant (e.g. correlated residuals).
This analysis is not itself available on common statistical software as SPSS, however the
procedure can be quickly performed by using the script developed by O’Connor, (2000).
3.5
Factor Matrix Extraction and Validation
SPSS (similarly to other statistical software packages) provide seven different factor
extraction methods. The first one – principal components – is fundamentally different, and
was already discussed, while selection between the others (Unweighted least squares,
Generalized least squares, Maximum likelihood, Principal axis factoring, Alpha factoring and
Image factoring) is much more complicated. Most literature does not distinguish for
advantages and disadvantages of these methods, reasoning there is no substantial difference.
However, if data sample is normally distributed, probably the best method is Maximum
Likelihood. This was supported by Fabrigar, Wegener, MacCallum, & Strahan, (1999) saying
that “primary advantage of Maximum Likelihood is that it allows for the computation of a
wide range of indexes of the goodness of fit of the model [and] (…) also permits statistical
significance testing of factor loadings and correlations among factors and the computation of
confidence intervals for these parameters”.
The next important step is selection of factor rotation pattern. In general, factor rotation is
needed for variance redistribution among extracted factors to make factor pattern more clear
and meaningful. Not rotated factor extraction would provide the first factor with the largest
amount of all variable variance loaded on it, thus this one would be very significant, while the
other factors would be loaded only with variance residuals on them. Therefore, factor variance
redistribution is particularly important to get comprehensible results available for further
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
interpretation. Hair et al., (2006) describes the process of rotation: “In a factor matrix,
columns represent factors, with each row corresponding to a variable’s loading across the
factors. By simplifying the rows, we mean making as many values in each row as close to
zero as possible (i.e., maximizing a variable’s loading on a single factor). By simplifying
columns, we mean making as many values in each column as close to zero as possible (i.e.,
making the number of high loadings as few as possible)”.
There are two different ways to rotate factors: orthogonal, which does not allow factor axis to
be correlated and oblique, which does not maintain mathematical factor independence and
allow them to be correlated. SPSS provides five different factor rotation options containing
three orthogonal (Quartimax, Varimax and Equimax) and two oblique (Direct oblimin and
Promax) solutions. Real world data is always correlated at some amount, thus it would be
irrational to choose one of orthogonal method. Distinguishing between Direct oblimin and
Promax is more complicated as the difference between those is foggy. SPSS Statistics Base
User’s Guide set out that Direct Oblimin method is available to manipulate delta, thus
choosing more or less oblique rotation, while for Promax manipulation of kappa is available
(Levesque & SPSS Inc., 2009). As explanation of the difference and rationality in
manipulating delta or kappa is not explained in a traditional literature, more described Direct
Oblimin method, with a default setting of delta equal zero was chosen.
The last step before factor loadings can be used for further analysis is results validation.
Factor loading shows correlation between variable and a factor, thus squared factor loading
indicates variables’ amount of variance accounted by the factor (minimal requirement to be
considered significant is 0.3 – 0.4 loading). Another thing worth consideration is
communalities, which identify each variable’s amount of variance loaded on the factor
(preferably should be not less than 0.5). Small values of communalities should be a signal that
variable has no sufficient explanation. Factor cross-loading is frequent and normal appearance
for a real world data, therefore should not be rejected, but it is advisable to check that loadings
for one variable on a different factors would have a gap of at least 0.2.
After completing factor analysis, the loadings are saved as variables for regression. Creation
of a single complex measure containing several individual variables is known as summated
scale concept. This concept, used together with theoretical justification, has several benefits:
-
Reduces measurement error probability – this is done by “using multiple indicators
(variables) to reduce the reliance on a single response. By using the average or
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
typical response to a set of related variables, the measurement error that might occur
(…) will be reduced” (Hair et al., 2006).
-
Several variables, which have the same concept, are presented by a single factor.
This is very useful, especially when number of variables to be included in
multivariate regression is large. Even though statistically significant results may
appear, interpretation of such model becomes complicated, and the desirable result
hardly can be seen. Therefore combining variables in some groups according to
common feature makes regression simpler and better understandable.
3.6
Multiple Linear Regression
After original variables have been summarized to factors, the results can be used for a
multiple linear regression. As statistical original data pre-check (test for normality,
heteroscedasticity, multicollinearity) was already done before performing factor analysis, and
loaded factors were validated, following multiple regression analyzing the relationship
between company value as dependant variable and SOX, together with the three factors, as
independent variables, can be run:
Tobins_Qi,t = β0 + β1 Dummy_SOXi,t + β2 Factor_1i,t + β3 Factor_2 i,t + β4 Factor_3 i,t + ε i,t
Where:
Tobins_Qi,t : represents measure of company (i) value in year (t);
Dummy_SOXi,t : an independent dummy variable representing SOX. It was given value 1 for
SOX applicable companies (the ones cross listed in NYSE, and only since 2002), and value 0
for SOX non applicable companies (all LSE cross listed, and NYSE cross listed before 2000).
Factor_1i,t : Represents the first factor loadings for company (i) value in year (t).
Factor _2i,t : Represents the second factor loadings for company (i) value in year (t).
Factor_3i,t : Represents the third factor loadings for company (i) value in year (t).
ε i,t : represents random error.
Factors will be loaded from these variables:
-
ROA : represents companies assets profitability in generating revenue.
-
ROE : represents corporate performance measured by return on equity.
-
ROE of previous year : represents corporate performance for previous year measured
by return on equity of previous year.
-
Net Profit Margin : represents companies’ profitability.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
-
Liquidity : representing ability to meet short term liabilities through cash flow.
-
Cash Flow : represents cash flows from operations scaled to assets.
-
CAPEX : represents companies investments to long term assets.
-
Sales Growth : Represents the percentage annual change in growth of sales.
-
Size : represented by companies’ total assets.
-
Leverage : represents level of the debt used for companies’ performance.
-
Tangibility : represents level of tangible assets and borrowing possibilities.
-
Dividend Yield : represents amount of dividends paid.
After regression is run, to solve residual autocorrelation problem AREG (autoregression)
procedure estimating true regression coefficients with first-order autoregressive errors AR(1)
have been used. This is a useful tool, as time series data usually have some trend caused by
outside factors. In case series have the same trend, correlation will appear regardless
causality. There are three algorithms for serial correlation removal: Prais-Winsten, CochraneOrcutt (in both cases regression is transformed) and Maximum-likelihood estimation (follows
ARIMA (1, 0, 0) algorithm). Prais-Winsten and Maximum-likelihood methods were applied.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
4. Empirical Results
The result part of the Thesis provide empirical research findings, and will be organized
following. Firstly, statistical data analysis is provided, including normality and correlation
tests. Afterwards, specific tests for factor analysis possibility are described. Next trace the
results of parallel analysis together with discussion on number of factors to extract. Then
factor analysis output and its relevance for further analysis is presented, followed with
multiple linear regression and its transformation results. Finally, hypothesis is checked to
answer the research question.
4.1
Data Analysis of Selected Variables
The first thing to do before starting research is to make sure data sample is sufficient for the
selected approach. As it was described in a methodological part, the minimal sample size
requirement is 10:1 ratio, but preferably it should be at least 30:1 cases per item. The actual
data sample contains 49 NYSE and 49 LSE cross listed companies with records for 11 years,
which is 1078 observations in total. There will be 12 variables analyzed, so the sample size is
a 90:1 ratio of observations for each. This is an appropriate sample size for factor analysis to
perform. However, some further statistical considerations have to be done.
4.1.1
Descriptive statistics
Data analysis and check for the fundamental statistical assumptions is the next step before
constructing a statistical model. As Tobin’s Q is dependant variable, data analysis starts with
change in value reassessment for both NYSE and LSE cross listed firms (Figure 4).
Figure 4. Change in NYSE and LSE cross listed companies Tobin’s Q
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
It is visible that there is no clear trend or obvious difference between SOX applicable and non
applicable companies Tobin’s Q. Evident decreases in 2002 and 2008 is seen, but it goes
without saying these are consequences of collapses, caused by Technology and Housing
bubbles. Therefore, deeper analysis of other ratios having impact on Tobin’s Q is needed.
Descriptive statistics of variables were performed and is presented in Appendix 4. Since the
main interest of this Thesis is effect of SOX, for comparison data sample was divided into 3
parts: the first consists of data for firms cross listed in LSE (named “SOX not applicable”),
the second one of data for NYSE cross listed firms before 2002 (named “Before SOX”) and
the last one of NYSE cross listed firms after 2002 (named “After SOX”). Mean, standard
error of mean, median and standard deviation was calculated for all groups.
Table 2 represents comparison of NYSE cross listed companies ratios in before SOX and after
SOX periods. Tobin’s Q apparently increased after SOX from 1.7954 to 1,9221 (by 12.67%).
Deeper analysis also indicates other variables growth during compared periods: liquidity
increased by 10.78%, ROE by 10.06%, net profit margin by 3.55% and ROA by 2.29%. Sales
growth is the only ratio which, although slightly, but decreased by 0.07%.
Table 2. NYSE cross listed firms mean ratios before and after SOX
NYSE cross listed companies
Before SOX
After SOX
Difference
Tobin's Q
1.7954
1.9221
12.67%
Liquidity
0.4357
0.5435
10.78%
Cash Flow
0.1078
0.1255
1.77%
CAPEX
0.0934
0.1058
1.24%
Sales Growth
0.1046
0.1038
-0.07%
ROA
0.0490
0.0719
2.29%
ROE
0.0929
0.1935
10.06%
ROE previous year
0.1723
0.1799
0.76%
Net Profit Margin
0.0750
0.1105
3.55%
Size (Assets)
0.4122
0.4285
1.64%
Leverage
0.2960
0.3151
1.90%
Tangibility
0.6390
0.6429
0.39%
Dividend Yield
0.0295
0.0301
0.06%
Table 3 shows LSE and NYSE companies’ descriptive analysis before and after SOX. It is
evident difference in Tobin’s Q during post SOX period (23.10%), though difference between
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
cross listed firms value is also observed during pre SOX period (8.53%), hence overall
increase is 14.57%. Highlighting gaps among other ratios during before SOX period are
following: Assets were 10.58% higher, Leverage and CAPEX were 7.37% higher and
Tangibility was also higher by 5.14%, meanwhile Sales growth was 6.58% lower. While
analyzing after SOX period main differences appears in 10.81% higher Assets, 8.96% higher
CAPEX, increased ROE for current and previous year (accordingly 5.75% and 5.91%), also
5,16% higher Tangibility.
Table 3. NYSE and LSE cross listed firms mean ratios before and after SOX
2000 – 2002
LSE
NYSE
Tobin's Q
1.7101
1.7954
Liquidity
0.4542
Cash Flow
2003 - 2010
Difference
Difference
Change in
Difference
LSE
NYSE
8.53%
1.6911
1.9221
23.10%
14.57%
0.4357
-1.85%
0.5240
0.5435
1.94%
3.80%
0.0804
0.1078
2.74%
0.0962
0.1255
2.93%
0.18%
CAPEX
0.0197
0.0934
7.37%
0.0162
0.1058
8.96%
1.59%
Sales Growth
0.1704
0.1046
-6.58%
0.1048
0.1038
-0.10%
6.48%
ROA
0.0321
0.0490
1.69%
0.0409
0.0719
3.10%
1.41%
ROE
0.0785
0.0929
1.44%
0.1360
0.1935
5.75%
4.30%
ROE previous year
0.1529
0.1723
1.94%
0.1208
0.1799
5.91%
3.97%
Net Profit Margin
0.0472
0.0750
2.78%
0.0809
0.1105
2.96%
0.18%
Size (Assets)
0.3064
0.4122
10.58%
0.3204
0.4285
10.81%
0.23%
Leverage
0.2224
0.2960
7.37%
0.2654
0.3151
4.97%
-2.40%
Tangibility
0.5876
0.6390
5.14%
0.5913
0.6429
5.16%
0.02%
Dividend Yield
0.0273
0.0295
0.22%
0.0285
0.0301
0.17%
-0.05%
In general, analyzing change in difference between variables is more meaningful, as it
eliminates initial inequality of sample firms. Thus, biggest positive shift appears in Sales
growth (6.48%), also in ROE (4.30% for current, and 3.97% for previous year) and Liquidity
(3.80%). Considering variables by groups, one could notice that ratios representing firm’s
capital and performance increased, while representing riskiness increased very slightly or
even decreased.
4.1.2
Normality and trend analysis
Independent samples t-test was performed in order to test if there exists statistically
significant difference in mean and variance of Tobin’s Q ratio before and after SOX. Two
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
variables for the analysis were taken – Tobin’s Q and dummy SOX. Null hypothesis of the
test says there is no significant difference in variance and mean of these variables. Table 4,
representing group statistics for the variables, shows mean of SOX applicable observations is
notably higher (0.225) compared to non applicable ones (0.1817).
Table 4. Tobin’s Q independent sample t-test
Dummy _SOX
N
Mean
Std. Deviation
Std. Error Mean
1
441
0.22526939
0.21335161
0.01015960
0
637
0.18174304
0.21202439
0.00840071
Tobins_Q
Levene’s test for equality of variances (see Table 5) takes p-value of 0,984, therefore null
hypothesis cannot be rejected. As equal variances are assumed, means can be also compared.
Mean difference (0.0435) at the value of t = 3.305 is statistically significant (p-value is
0.001), accordingly null hypothesis for equal means is rejected. Consequently, it appears
significant difference in means for SOX applicable and not applicable firms Tobin’s Q exists.
Table 5. Levene’s test for equality of variances
Levene's Test for
Equality of Variances
Tobin’s Q
Equal
variances
F
Assumed
.000
Not assumed
Sig.
t
t-test for Equality of
Means
Df
95% Confidence Interval of the
Difference
Sig. (2Mean Std. Error
tailed) Difference Difference
Lower
Upper
.984 3.305 1076
.001 .043526
.013167
.017685 .069364
3.302
.001 .043526
.013182
.017655 .069397
942
Data analysis continues with variables testing for normality. Both Kolmogorov-Smirnov and
Shapiro-Wilk tests were run to see if data is normally distributed (results presented in
Appendix 5). Null hypothesis for both tests say data is normally distributed, and p-value
should be lower than 0.05 to reject it. As all p values are very small, null hypothesis cannot be
rejected, what means data is not normally distributed.
To identify potential issues, a closer look to each variable distribution is needed, so that
histograms including normality trends are presented in Appendix 6. Distribution of Tobin’s Q
and Assets are the closest to normal, however the first one is a bit positive skewed and the
second is platykurtic. ROE, Cash flow and Sales growth shows leptocurtosis. Also leptokurtic
distribution is observed for ROA, CAPEX, Dividend yield and Liquidity, just first two are
also skewed negatively, and the second ones skewed positively. Skewness is also observed for
the last two variables: positive for Leverage and negative for Tangibility.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Although variables are not normally distributed,
“for sample sizes that are sufficiently large, violation of the normality assumption is
virtually inconsequential. Appealing to a central limit theorem, the test statistics will
asymptotically follow the appropriate distributions even in the absence of error
normality” (Brooks, 2008).
What is more, to perform factor analysis only correlation between variables is initial premise,
and should be considered uppermost: “The underlying statistical assumptions influence factor
analysis to the extent that they affect the derived correlations. Departures from normality,
homoscedasticity, and linearity can diminish correlations between variables” (Hair et al.,
2006).
4.1.3
Correlation among selected variables
In terms of intercorrelation among variables two conditions should be fulfilled before
performing factor analysis: all correlations cannot be very small (at least some variables with
correlation greater than 0,3 exists) and correlations cannot be equal. In other words – to verify
variables are related and structure exists, thus they can be grouped.
SPSS provides three different bivariate correlation measures: Pearson’s, Kendall’s Tau and
Spearman’s rho. As the last two are better to use with rank ordered or ordinal scale (non
parametrical) data, Pearson’s correlation was chosen for further analysis.
Cross – correlations between all variables which will be used for further analysis is presented
in Appendix 7. Although the vast majority of correlation coefficients do not appear to be
large, they are big enough for factoring. As correlation significance should be also considered,
p-values were also added to correlation matrix. Majority of p-values being very small
confirms presence of significant (at the 0.01 level, 2-tailed) relationship between variables.
Significant positive relationship appears between Tobin’s Q and ROE (0.239), ROE of
previous year (0.190), ROA (0.176), CAPEX (0.178), Cash flow (0.132) and dummy variable
SOX (0.100), while with company size (-0.121), leverage (-0.070), dividend yield (-0.317),
liquidity (-0.066) and tangibility (-0.268) negative correlation observed (see Table 6). Taking
a deeper look to correlation measures, one can notice that in general most of coefficients are
higher among variables, rather than between variables and Tobin’s Q. This is a supplementary
argument for grouping ratios.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
CAPEX
Size
0.18
-0.12
-0.26
0.08
0.05
0.14
0.04
0.16
-0.02
0.16
1.00
0.11
1.00
Leverage
-0.07
-0.07
0.09
0.07
-0.19
-0.01
0.26
1.00
Sales Growth
Dividend Yield
Cash Flow
Liquidity
0.03
-0.32
0.13
-0.07
-0.27
0.07
0.00
0.24
0.29
0.04
0.06
0.12
0.34
0.16
-0.04
0.01
0.11
0.29
0.13
-0.01
0.15
0.06
0.49
0.36
-0.04
-0.06
0.07
0.01
-0.21
-0.22
-0.09
0.31
0.20
-0.00
0.32
-0.09
0.13
-0.10
-0.12
0.38
Tangibility
ROA
1.00
-0.06
0.12
0.19
0.05
1.00
0.16
0.04
0.13
1.00
0.69
0.07
1.00
0.21
Tangibility
1.00
Liquidity
1.00
0.33
Cash Flow
1.00
0.38
0.57
Dividend
Yield
0.35
0.13
0.61
Sales
Growth
Leverage
0.24
0.19
0.18
ROE (prev. year)
ROA
Size
1.00
CAPEX
Net Profit
Margin
1.00
0.03
Tobin's Q
Net Profit Margin
ROE
ROE
Tobin's Q
ROE
(prev. year)
Table 6. Variables correlation coefficient matrix
1.00
An additional test to check correlation significance among variables and relevance for
factoring is by analyzing partial correlations, what means analyzing the part of correlation
which is not substantiated considering the effect of other variables. Partial correlations values,
opposite to normal correlation values, should be small, otherwise it means that variables
cannot be explained by other variables. Thus Anti-image correlation matrix, which provides
negative value of partial correlation, was run (see Appendix 8). Values of the diagonals fall
between 0.504 (for CAPEX) and 0.820 (for ROE), and satisfy theoretical requirement of
being not less than 0.5.
Testing multicolinearity is not that important, as in factor analysis “some degree (…) is
desirable, because the objective is to identify interrelated sets of variables” (Hair et al., 2006).
Table 7. Kaiser Meyer Olkin and Bartlett sample adequacy test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity
Approx. Chi-Square
df
Sig.
0.637
3371.407
66
0.000
Another two steps in terms of intercorrelation measure for factor analysis validation was
taken. The first one is Kaiser Meyer Olkin test for measurement of sampling adequacy, which
as it is shown in table 7, is 0.637 (satisfies the minimum requirement of 0.5), and means
intercorrelation level between variables is appropriate for factor analysis. The second -
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Bartlett test of sphericity – confirms there is a statistically significant correlation among at
least some of the variables in the correlation matrix, as the p-value is very small. Thus, data
sample is appropriate for factor analysis.
4.2
Factor Analysis
After all necessary assumptions were revised, common factor analysis can be performed with
intention to find shared variance among variables. Hereinafter will be described number of
factors selection process, matrix extraction and validation process, also the results found.
4.2.1
Parallel analysis
Following the simplest approach on deciding for the proper numbers of factors to extract, the
results suggest there are 4 factors with eigenvalues greater than 1. As this method was broadly
criticized, parallel analysis for specification was performed. The script written by O’Connor,
(2000) was used for Monte Carlo simulation run. Following strings were added to the script:
-
Number of cases – 1078 (according to the real data size);
-
Number of variables – 12 (according to the real number of variables);
-
Number of data sets – 5000 (literature suggests this is enough, as larger number of
simulations would not give a different result);
-
Random data distribution percentile – 95;
-
Common Factor Analysis method;
-
Raw data permutation.
Figure 5 represents randomly generated eigenvalues against the actual estimated eigenvalues
based on data sample correlation matrix. From the sample output it is clearly seen there are
even five latent roots from real data greater than adequate random data 95 percentile
distribution eigenvalues.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Figure 5 Parallel analysis
Nevertheless, it is advisable to consider if the small values (closest to randomly generated)
should be ranked as factors, as it can be simply correlated residuals and not the real factors.
What is more, in most literature it is recommended not to extract factors with a very small
number of variables loaded, and some sources even say the minimum is 5 variables for 1
factor (Hair et al., 2006). Whereas the actual data sample consists of 12 variables, 3 factors
(each loaded by 4 variables) were extracted.
4.2.2
Factor extraction and validation
Maximum likelihood method was used to perform factor analysis and communality estimates
are shown in Table 8. Initial communalities show which part of variables variance is shared
with the remaining variables, in other words each item is regressed under all others, and the
initial value presented is the R squared for that particular variable. Thus, it is visible that the
biggest common variance is for ROA, which shares 63.2% of its total variance, the second
largest is Cash flow with 61.9% shared variance, then Liquidity with 58.9% shared variance,
and the remaining ones: Net profit margin (45.2%), ROE (42.3%), Tangibility (32.6%),
Assets (30.3%), Leverage (28.4%), CAPEX (20.6%), ROE of previous year (20.1%),
Dividend yield (12.0%) and Sales growth (6.7%). The last two are really small values, sharing
a minor part of their variance, however seeing there is no common rule what should be
included or excluded, and considering that the Anti-image correlation matrix reported
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
sufficient coefficients of the measures of sampling adequacy on a principal diagonal, these
variables were left for further analysis.
Table 8. Variable communalities estimates
Initial
Liquidity_OCF
Cash_Flow
Sales_Growth
Net_Prof_Mar
ROE_prev
ROE
ROA
CAPEX
log_Assets
Leverage
Dividend_Yield
Tangibility
Extraction
.589
.619
.067
.452
.201
.423
.632
.206
.303
.284
.120
.326
.999
.577
.059
.378
.157
.374
.975
.055
.462
.309
.158
.279
In Table 9 results for the extracted common factors are presented. Looking at the initial
eigenvcalues it can be seen there are four factors with eigenvalues greater than 1, but as
parallel analysis suggested, 3 factors were retained. The variance accounted by each factor is
set more or less gradually: for the first factor – 15.82%, for the second – 14.06%, and for the
third – 9.99% (this is result of unrotated solution). Cumulative explained variance is 39.87%,
but this should not be a case, as common factor and not principal component analysis were
selected, therefore only common variance is being explained. The last column indicates the
rotated sums of squared loadings, that is show factor loadings after pushing variance for more
even distribution across factors. However, as there was no significant difference on initial
variance loadings, the rotated result does not change dramatically.
Table 9. Common factors accounted variance
Initial Eigenvalues
Factor
1
2
3
4
5
Total
2.977
1.807
1.473
1.123
.945
% of
Variance
24.812
15.062
12.275
9.361
7.873
Extraction Sums of Squared Loadings
Cumulative
%
24.812
39.873
52.148
61.508
69.381
Total
1.899
1.687
1.199
45
% of
Variance
15.821
14.057
9.995
Cumulative
%
15.821
29.878
39.873
Rotation Sums of
Squared Loadingsa
Total
1.698
2.308
1.194
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Maximum likelihood approach also provides a unique test for goodness of fit test (see Table
10). In principle, this test is very similar to Bartlett's test of sphericity, and aims to check
residuals correlation matrix for at least one significant correlation. From a statistical
significance perspective this test shows there is still more shared variance which could be
accounted for by extracting more factors. This was not done remaining on a safe side not to
extract too weak factors, which indeed are not real.
Table 10. Goodness of fit test
Chi-Square
df
555.650
Sig.
33
.000
Factor matrix shows initial variance loadings, but is hard to analyze because of uneven
variance distribution (see Appendix 9). Since Oblique Direct oblimin rotation method was
used, Pattern matrix is much more informative and reasonable to analyze. All three factor
loadings can be clearly seen from Table 11.
Table 11. Factor pattern matrix
Factor
1
Liquidity_OCF
Cash_Flow
CAPEX
Sales_Growth
ROA
ROE
Net_Prof_Mar
ROE_prev
log_Assets
Leverage
Tangibility
Dividend_Yield
2
.975
.561
-.239
.173
.024
-.057
.096
.004
-.124
-.105
.205
-.009
3
.085
.353
.055
.090
.986
.612
.585
.345
.230
-.135
-.074
.083
.136
.197
.024
-.110
-.164
.066
-.105
.158
.603
.529
.516
.379
The first factor mainly consists of Liquidity (0.975), Cash flow (0.561), CAPEX (-0.239) and
Sales growth (0.173), the second of ROA (0.986), ROE (0.612), Net profit margin (0.585) and
ROE of previous year (0.345), the third of Assets (0.603), Leverage (0.529), Tangibility
(0.516) and Dividend yield (0.379). However, these described loadings indicate only the
supreme part, and it can be seen all variables are loaded on all three factors, just with
substantially lower amounts on the rest. In the ideal world each single variable loading should
be done on a single factor, but this never happen with real data. In fact, results are not
46
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
concerning as there are no significant cross loadings (all variables are mostly loaded on one
factor).
The last considerable point in this part is correlation among factors. Table 12 provides
correlation matrix, which indicates that Factor 1 and Factor 2 are positively correlated, and
Factor 3 is negatively correlated with Factor 1 and positively with Factor 2. It is notable, that
positive correlation between Factor 1 and Factor 2 is stronger than between Factor 2 and
Factor 3.
Table 12. Factor correlation matrix
Factor
Factor 1
Factor 2
Factor 3
Factor 1
Factor 2
Factor 3
1.000
.255
-.084
.255
1.000
.115
-.084
.115
1.000
Before concluding on correlation, factor loading signs should be also checked, but as all
factors were loaded positively (except CAPEX in Factor 1, but this will be further examined
in discussion part of Thesis), correlation matrix signs can be interpreted the way it is.
4.3
Multiple Linear Regression Model Conduct and Transformation
Summated scales created from three factor loadings were used to conduct multiple linear
regression. Since correlation coefficients between factor loadings are low, the newly created
items can be considered as valid. Reliability test was also performed for each factor. The
result is shown in Appendix 10 indicates that Cronbach's Alphas for factors meet minimum
requirement to be as high as 0.50 (accordingly 0.507, 0.570 and 0.671).
Null hypothesis of multiple linear regression says there is no significant relationship between
corporate value (Tobin’s Q), the three loaded factors and dummy variable SOX. The test was
performed, and model summary is presented in Appendix 11.
Although adjusted R Square says model is good enough, as 83.3% of Tobin’s Q variability is
accounted by selected variables, the results of Durbin-Watson test indicates there might be
serial correlation problem. Ideally this test should take value 2, and acceptable range is
between 1.5 and 2.5. However, in conducted model value is 0.692 and this should be a
concerning sign.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Residuals autocorrelation can be a subsequent of strong relationship between one year figures
to another (usually when dependant variable is lagging). A solution for this problem is models
using first differences. These approaches are based on assumption that a change of
independent variable from one year to another should be related to the dependent variable
yearly change.
“Transformations of the regression equation with autocorrelated errors may render those
errors independent of one another and may permit best linear unbiased parameter estimation”
(Yaffee & McGee, 2000). Two transformation methods were tested for model correction:
Prais-Winsten and Exact maximum-likelihood estimation, which is based on ARIMA (1, 0, 0)
approach. Both attempts were successful, and produced very similar results (estimated betas
are the same). Due to more readily comprehensible output, Prais-Winsten method is further
analyzed, and output of Exact maximum-likelihood estimation is available in Appendix 12.
Successfully reconstructed model summary is presented in Table 13.
Table 13. Autoregressed model fit summary
R
R Square
Adjusted R Square
Std. Error of the Estimate
Durbin-Watson
.893
.798
.797
.069
2.090
It appears that serial correlation problem was solved, as now Durbin-Watson test takes almost
perfect value of 2.09. R square parameter still shows model is good enough, as 79.8% of
Tobin’s Q variability is accounted by three factors and SOX. Adjusted R square is lower than
original (it takes sample size into account), but the difference is very low, because of small
standard error of the estimates.
Collinearity statistics can be found in the Regression model summary presented in Appendix
11 and shows results of two tests: Tolerance and VIF. As these tests on principle report the
same result (VIF is a function of Tolerance test), for interpretation simplicity only Tolerance
test results are analyzed. All numbers are as high as 0.95 meaning that all independent
variables’ unique variance is at least 95% and cannot be predicted by other independents’
variance. This ratio is assumed to be concerning only when it is less than 0.2, therefore no
multicollinearity problem is identified.
The last and most important part is estimated regression coefficients (see Table 14).
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Table 14. Prais-Winsten estimated regression coefficients
Unstandardized Coefficients
Dummy _SOX
CAPITAL
PERFORMANCE
RISK
(Constant)
Standardized
Coefficients
B
Std. Error
Beta
t
Sig
.028
.031
.063
-.177
.188
.012
.003
.003
.003
.008
.033
.180
.313
-.840
2.369
12.392
21.523
-61.051
24.910
.018
.000
.000
.000
.000
It appears that all independent variables are statistically significant, as shown by both tests: tstatistics do not fall within the range of -2 to 2, and p-values are less than 0,05. Estimated
coefficients were added to multiple linear regression following:
Ln (Tobins_Qi,t) = 0.188 + 0.033 Dummy_SOXi,t + 0.180 CAPITALi,t +
+ 0.313 PERFORMANCE i,t – 0,840 RISK i,t
Results disclose there are two factors which affect corporate value (Tobin’s Q) positively, and
one which affect negatively. However, beta (also unstandardized coefficient) of a negative
factor is significantly larger (0.180, 0.313 and -0.840 respectively), thus we indicate this
factor has stronger effect on Tobin’s Q. It can be also seen that SOX, even though with very
small standardized coefficient (0.033) had a significant positive impact on corporate value.
Regression residuals were checked under normality assumption, and it is seen in Appendix 13
this is not violated. However, standardized residual plots against predicted values show
independence assumption is violated.
The ANOVA test (see Appendix 14) shows how overall model fits data, and observed p-value
is less than 0.01. Thus, the hypothesis is accepted, concluding there is the significant
relationship between SOX, summarized variables and European companies’ corporate value.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
5. Discussion
This part of the Thesis provides overview and discussion on the most significant empirical
research findings. Further synthesis with literature review on SOX impact to companies’
value is done. Lastly, limitations of the study are analyzed and implications for further
research suggested.
5.1
Significant Findings Overview and Interpretation
Change in Tobin’s Q mean of NYSE and LSE cross listed firms over time is not readily
observed. However, taking a deeper look it is noticeable there exists a significant difference in
changes of means during pre SOX and post SOX periods: before 2002 NYSE cross listed
companies had 8.53% higher Tobin’s Q, while during period 2002 – 2010 the difference
increased to 23.10%. In addition, descriptive analyses confirmed there is a significant
difference in means, but no significant difference in variance.
What is more, important differences are observed in other ratios shifts too. Sales growth for
NYSE cross listed companies was 6.58% lower before SOX, while during after SOX period it
almost equalized (difference -0.10%), being a signal of better growth opportunities. Positive
changes are also observed in corporate governance, as difference between ROE increased by
4.30% and 3.97% for current and previous year accordingly. Another meaningful change
observed in Liquidity ratio, as it was lower (1.85%) for NYSE cross listed firms in pre SOX
period and higher (1.94%) in post SOX period. Generally it shows SOX applicable companies
have improved the ability to meet liabilities and pay off debt timely. Shift in Leverage ratio is
also worth mentioning, as it was the only ratio of SOX applicable companies, which
decreased from 7.37% to 4.97%. This indicates substantially lower SOX compliant firms’
riskiness as a consequence of tightened requirements and personal management responsibility.
Figure 6 illustrates the generalized model for the unique contribution of each ratio to the
Capital, Performance and Risk factors, which further have impact on companies’ corporate
value.
50
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Figure 6 Generalized ratios’ impact on companies’ value
The first factor named Capital basically shows companies disposable funds and consists of
following ratios: Liquidity (0.975), Cash Flow (0.561), Sales growth (0.173), and CAPEX (0.239). Liquidity and Cash Flow ratios have significantly higher loadings, though these
variables are dominant in Capital factor. It is notable that CAPEX was loaded negatively,
which means ratios still vary together, but opposite directions, if CAPEX increases, Liquidity
and Cash Flow decrease.
The second factor entitled Performance represents firms’ management and profitability and
is composed of following ratios: ROA (0.986), ROE (0.612), Net Profit Margin (0.585) and
ROE of previous year (0.345). Assets profitability measure is clearly dominant in this factor,
however other variables have relatively high loadings too.
The third factor is named Risk and reflects companies’ debt level and borrowing possibilities.
The composition of ratios is following: Size represented by total assets (0.603), Leverage
(0.529), Tangibility (0.516) and Dividend yield (0.379). No clear hierarchic structure is
identified among variables loadings on this factor, thus all ratios have similar magnitude on
firms’ riskiness.
In terms of correlation among factors, the highest (0.255) is observed between companies’
Capital and Performance. The latter is also positively correlated (0.115) with Risk factor,
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
while Capital is negatively (-0.084) related to riskiness. Thus, the better is corporate
management, the larger are firms’ disposable funds, and the lower need for borrowing.
From factor loadings created summated scales were used for further analysis. This is a
relevant option not only because of simplifying further results interpretation and transparency,
but also this method reduced reliance on a single variable, this way reducing measurement
errors (such as differences in accounting rules applied) probability. CAPEX was the only
variable loaded negatively, therefore while creating summated scales distribution of this ratio
was reversed, still keeping distribution characteristics.
The results of multiple linear regression indicated Capital, Performance and Risk factors
together with dummy variable SOX account for 83.3% of Tobin’s Q variability, but serial
correlation problem appeared. This is a usual phenomenon, as time series data might have a
trend caused by outside factors, and if series move the same trend correlation appears
regardless causality. Looking at Tobin’s Q time series graph (figure 4) for NYSE and LSE
cross listed companies, a clear trend caused by macroeconomic factors was observed: both
ratios move together with a significant decrease in 2001 - 2002 caused by Dot-com bubble
burst, traced with increase during 2003 – 2007 explainable by Real Estate bubble and decline
in 2008 – 2010 due to Financial crises.
Regression
transformation
for
residual
autocorrelation
removal,
using
first-order
autoregressive errors provided linear unbiased parameter estimations. In the transformed
model independent variables still account for 79.8% variability of corporate value, and is
slightly lower than original, due to small standard errors of estimates.
The most important finding of this research is regression coefficients, showing the impact of
each factor to corporate value. The unstandardized coefficients are measured the same way as
variables and show difference in Tobin’s Q per unit change in Capital, Performance or Risk.
These coefficients are good to analyze for prediction purposes, but as the interest is in
historical impact and factors strength comparison, standardized coefficients were considered.
The results indicate there is significant positive relationship between corporate value and
SOX (β1 = 0.033), also between corporate value and Capital (β2 = 0.180), and between
corporate value and Performance (β3 = 0.313), while significant negative relationship between
corporate value and Risk (β4 = -0.840) appears.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
5.2
Discussion on Research Findings in Context of Literature Reviewed
It is consequential to start overview by verifying the research findings with original SOX act
titles’ intentions, which had direct impact on company value. Thus, the purpose of the second
title of the Act is to lessen fraud possibilities, and yet the overall increase in Capital factor
(especially cash flows) is observed. Title three seeks to increase management responsibility
and thus better results appear in higher Performance factor. The fourth title refers to
enhanced internal controls process resulted in huge time and resources spending on
implementation what had effect on decreased sales growth and capital investment. Criminal
penalties and fines, described in the last titles of the Act, decreased cross listed firms Risk
factor, and this can be seen from the lower leverage ratio.
In context of other researches analyzing SOX impact to corporate value, contradictory results
were found. The Thesis provided empirical evidence on SOX positive effect to corporate
value contributing to Duarte et al., (2007), Leuz, (2007) and Berger et al., (2005). In general,
the Act increased trust in public firms and investors’ value increased more in managers’
accountability, then costs of less concentrating on business and innovation.
Contrary, opponents found SOX was ineffective, unnecessary and extremely costly
overreaction (Ribstein, (2005); Romano, (2005)), as it was proposed in a rush and lack of
debate, trying to calm the media frenzy over huge corporate scandals. Doidge et al., (2004)
also concluded on negative SOX impact to foreign cross listed firms’, stating Tobin’s Q was
16,5% higher before SOX. However, this research was done just couple years after SOX, and
possibly only high compliance costs and no benefits were considered.
Litvak, (2007) also observes negative SOX effect, but notices it was much bigger for riskier
companies. This partially coincide to this Thesis results, as it also shows decreased leverage
level and at the same time increased Tobin’s Q. Similar findings were done by Kang & Liu,
(2010), saying riskier firms became more careful on their spending decisions after SOX.
However, authors find that investment level also decreased, while Thesis result suggest NYSE
cross listed firms CAPEX, compared to LSE, increased. This also contradicts to Gompers &
Ishii, (2003) results, which say capital expenditures dropped down.
Sustaining to this study findings, Doidge et al., (2009) and Duarte et al., (2007) confirm on
positive relationship between SOX and corporate performance. The positive effect of SOX
appears in increased investors’ value through better corporate performance and cross-listing
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
premium, as SOX reduces controlling shareholders ability to extract private benefits.
However, Zhang, (2005) end up with different conclusions, proposing firms do not benefit
from tightened governance.
Considering Capital factor Thesis findings are diverse to Ahmed et al., (2010) who observe
significant decrease in firm cash flow and profits. Although these two ratios stayed almost at
the same level as before SOX, still slight increase is observed. Iliev, (2010) also found that
SOX (particular section 404) caused significantly higher indirect costs, and decrease in cash
flows.
Overall SOX effect could hardly be measured at this time, though the Act definitely had both
benefits and costs, but these cannot be accurately weighted (Prenice & Spence, (2007). Even
some common tendency in reviewed researches results, according to the aging, can be noticed
– the later research done, the more likely positive effect is found. In addition, “invisible”
value also should not be forgotten, as SOX “helped to disclosure some fraud cases and to
discourage others” (Prentice, 2007).
Researches investigating firms’ cross listing decisions found that in the next few years after
SOX was passed there was increase in New York stock exchange (NYSE) number of delisted
companies, while at the same time LSE listing increased. However, still no evidence was
found that NYSE became less competitive in the global market, as significant governance
benefit for companies remains and listing premium did not fall (Doidge et al., 2009).
Moreover, it seems U.K. is going the same direction – recently UKLA passed a consultation
paper, proposing much more rigorous requirements to market participants. If changes will be
adopted the way it is proposed, companies listed in LSE will face a huge increase in internal
controls documentation requirements. Critics already discuss on increased future costs and
pure benefits, while upholders say changes are necessary. Either way, situation appears to be
similar to the one that happened in U.S. ten years ago.
Eugene Fama, after financial crises of 2008, generalized need of new rules saying a lot
depends on people reaction, and nobody could say what regulations would be proper. The
reason is that companies can always find a solution how to overcome a law, and “to do things
that are in the letter of the regulations but not in the spirit” (Cassidy, 2010). Allan Greenspan
endorses considering all depends on nature of human being, as some are with “enviable
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
standards”, and some “continuously seek to cut corners” (Greenspan, 2005). Regulations are
created to set a standard and govern behavior, but they cannot change the substance.
5.3
Limitations and Implications
After existing literature review was done and empirical research conducted, some limitations
are identified together with suggestions for further implications.
The first thing noticed while analyzing literature is contradicting results found on SOX impact
to company value. Taken a deeper look, one could notice that researches with most different
results were done at the earliest after SOX passed: Ribstein (2005), Doidge et al. (2004),
Gompers & Ishii ( 2003), Romano (2005), Zhang (2005). Authors of these papers consider
high costs which although are real, but overstated due to implementation period. What is
more, benefits of the Act are most likely overlooked, as at that time it could be hardly seen.
For further implication research by analyzing SOX effect dividing time into three following
periods: before SOX, SOX implementation and after SOX could be done. This way the real
costs and benefits of Act could be more precisely observed.
Further, the research was restricted with analysis of SOX effect to large European companies,
leaving on a side the ones cross listed in NYSE Arca, Euronext, Alternext and Amex, also
LSE Alternative Investment Market. As SOX is applicable without any exception on
company size, most possibly compliance costs were more significant to the small ones. A
similar empirical research could be executed taking small companies for a sample.
The last limitation was observed in Empirical Results part analyzing factors reliability.
Relatively low Cronbach’s Alphas indicate created summated scales do not represent all
concepts of companies’ Capital, Performance and Risk. There is definitely more variables
which account for these factors. For further research, additional ratios sharing variance with
extracted factors could be added, to increase concept reliability.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
6. Conclusions
The Thesis aimed to verify the hypothesis, whether financial markets regulating law, the
Sarbanes-Oxley Act of 2002, together with representative factors, have impact on European
companies’ corporate value. To answer the research question, different variables for period
2000 - 2010 affecting firms’ value were selected and analyzed. The sample was constructed
by matching SOX compliant NYSE cross listed European firms according to their size and
industry with LSE European cross listed firms, for which the Act is not applicable.
The reviewed literature provides diverse beliefs on overall costs and benefits of the Act.
Critics argue it was enough to apply the existing laws properly, and SOX was simply
expensive overregulation, meanwhile the others upheld opinion the Act improved corporate
performance and accountability, recovered investors confidence and improved financial
markets’ efficiency. Contradictory results most likely appear due to different companies
selected for data sample and different ratios selected to represent the same features.
To identify SOX effect and to avoid variables diversity bias, most frequently analyzed ratios
were identified. Parallel analysis revealed there exists three common factors with shared
variance among variables. Factor analysis generalized several dimensions into three
summated scales. The first factor is named Capital and represents company’s disposable
funds. It was loaded by shared variance of Liquidity, Cash Flow, CAPEX and Sales Growth
ratios. The second factor shows corporate profitability and was entitled as Performance. This
one contains common variance of ROE for current and previous year, ROA and Net Profit
Margin. The last factor is named Risk, and represents firms’ debt level and borrowing
possibilities. It consists of Size (represented by total assets), Leverage, Tangibility and
Dividend Yield ratios shared variance. The results of multiple linear regression indicate SOX
together with Capital and Performance factors have significant positive effect on European
companies’ corporate value (measured by Tobin’s Q ratio), while Risk factor have significant
negative effect.
However, relatively low Cronbach’s Alpha indicate factors do not represent all concepts of
firms’ Capital, Performance and Risk, therefore further research by adding more ratios with
shared variance could be done. Also, a separate analysis for small European SOX compliant
companies would be purposeful (the research considered only large companies), as the Act
could have different effect on those. What is more, some tendency in reviewed researches
results according to the aging was observed, as the later research is done, the more likely
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
positive effect is found. Therefore, further analysis separating periods to before SOX, SOX
implementation and after SOX would be a rational way to identify compliant costs and
benefits more precisely.
Overall, it is clear SOX effect on corporate value was not consistent. Tightened requirements
and increased penalties resulted in increased corporate governance, and led to better business
decisions. Huge SOX implementation costs encouraged companies to centralize and automate
financial reporting systems, thus compliance costs should be decreased to minimum in time
and simply become part of companies internal control process. Thus, in a long run SOX
should be beneficial for business growth, if applied properly. This result in a more transparent
management, more ethical culture, making every employee being responsible for action taken
and gains to both, shareholders and managers.
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The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
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http://research.chicagobooth.edu/igm/research/papers/8Leuz-WastheSarbancesOxleyAct.pdf
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35. Litvak, K. (2008a). Long-Term Effect of Sarbanes-Oxley on Cross-Listing Premia.
European Financial Management, 14, 875–920.
36. Litvak, K. (2008b). Defensive Management: Does the Sarbanes-Oxley Act Discourage
Corporate Risk-Taking? Law and Economics Research Paper, 108. Retrieved from
http://67.208.89.102/files/2007/09/28/20070925_070928LitvakPaper.pdf
37. O’Connor, B. P. (2000). SPSS and SAS programs for determining the number of
components using parallel analysis and Velicer’s MAP test. Behavior Research Methods,
Instruments, & Computers, 32, 396–402.
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38. Piotroski, J. D., & Srinivasan, S. (2007). Regulation and Bonding: The Sarbanes-Oxley
Act and the Flow of International Listings. Journal of Accounting Research, 46, 383 –
426.
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Governance: How Wise is the Received Wisdom? Georgetown Law Journal, 95, 1843 –
1910.
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Oxley Act and the exchange listing requirements on firm value. Journal of Corporate
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number of components to retain. Psychological Bulletin, 99, 432–442.
62
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 1. Number of delisted companies and number of IPO’s
450
400
350
300
250
200
150
100
50
0
NYSE
LSE
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Number of NYSE and LSE delisted companies
700
600
500
NYSE
LSE
400
300
200
100
0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Number of NYSE and LSE Initial Public Offerings (IPO’s)
63
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 2. NYSE cross - listed companies selection
Industry
Basic Materials
Consumer Goods
Consumer Services
Health Care
Industrials
Oil & Gas
Technology
&
Telecommunications
Utilities
Sector
Basic Resources
Basic Resources
Basic Resources
Basic Resources
Chemicals
Automobiles & Parts
Food & Beverage
Food & Beverage
Food & Beverage
Food & Beverage
Food & Beverage
Personal & Household Goods
Personal & Household Goods
Media
Retail
Retail
Travel & Leisure
Travel & Leisure
Health Care
Health Care
Health Care
Health Care
Health Care
Health Care
Construction & Materials
Construction & Materials
Industrial Goods & Services
Industrial Goods & Services
Industrial Goods & Services
Industrial Goods & Services
Oil & Gas
Oil & Gas
Oil & Gas
Oil & Gas
Oil & Gas
Oil & Gas
Oil & Gas
Technology
Technology
Technology
Technology
Telecommunications
Telecommunications
Telecommunications
Telecommunications
Telecommunications
Telecommunications
Utilities
Utilities
Company name
ArcelorMittal
Tenaris S.A.
Rio Tinto PLC
BHP Billiton PLC
Syngenta AG
Autoliv Inc.
Anheuser-Busch InBev N.V.
Coca-Cola Hellenic Bottling Co. S.A.
Unilever N.V.
Diageo PLC
Unilever PLC
Luxottica Group S.p.A.
Natuzzi S.p.A.
Pearson PLC
Philips Electronics NV
Delhaize Group
InterContinental Hotels Group PLC
Carnival PLC
Novo Nordisk A/S
Sanofi
Fresenius Medical Care AG & Co. KGaA
Novartis AG
GlaxoSmithKline PLC
AstraZeneca PLC
CRH PLC
Chicago Bridge & Iron Co. N.V.
Siemens AG
Tsakos Energy Navigation Ltd.
CNH Global N.V.
ABB Ltd.
Total S.A.
CGG Veritas
ENI S.p.A.
Statoil ASA
BP PLC
BG GROUP
ENSCO PLC
Nokia Corp.
Alcatel-Lucent
SAP AG
STMicroelectronics N.V.
France Telecom
Telecom Italia S.p.A.
Portugal Telecom SGPS S/A
Telefonica S.A.
Turkcell Iletisim Hizmetleri A.S.
BT Group PLC
Veolia Environnement S.A.
National Grid PLC
64
Ticker
MT
TS
RIO
BBL
SYT
ALV
BUD
CCH
UN
DEO
UL
LUX
NTZ
PSO
PHG
DEG
IHG
CUK
NVO
SNY
FMS
NVS
GSK
AZN
CRH
CBI
SI
TNP
CNH
ABB
TOT
CGV
E
STO
BP
BG
ESV
NOK
ALU
SAP
STM
FTE
TI
PT
TEF
TKC
BT
VE
NGG
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 3. LSE cross - listed companies selection
Industry
Basic Materials
Consumer Goods
Consumer Services
Health Care
Industrials
Oil & Gas
Technology
&
Telecommunications
Utilities
Sector
Basic Resources
Basic Resources
Basic Resources
Basic Resources
Chemicals
Automobiles & Parts
Food & Beverage
Food & Beverage
Food & Beverage
Food & Beverage
Food & Beverage
Personal & Household Goods
Personal & Household Goods
Media
Retail
Retail
Travel & Leisure
Travel & Leisure
Health Care
Health Care
Health Care
Health Care
Health Care
Health Care
Construction & Materials
Construction & Materials
Industrial Goods & Services
Industrial Goods & Services
Industrial Goods & Services
Industrial Goods & Services
Oil & Gas
Oil & Gas
Oil & Gas
Oil & Gas
Oil & Gas
Oil & Gas
Oil & Gas
Technology
Technology
Technology
Technology
Technology
Technology
Telecommunications
Telecommunications
Telecommunications
Telecommunications
Utilities
Utilities
Company name
Svenska Cellulosa AB
BHP Billiton
Anglo American
Antofagasta
Johnson Matthey
GKN
Sabmiller
Diageo
Unilevered
Associated British Foods
Tate & Lyle
Berkeley Group Hldgs
Persimmon
Daily Mail & General Trust
Tesco
Soco International
Millenium & Copthorne Hotels
Stagecoach Group
Consort Medical PLC
Bioquell
BTG
Dechra Pharmaceuticals
Source Bioscience PLC
Skyepharma
Balfour Beatty
Kier Group
Smiths Group
Weir Group
IMI
BBA Aviation PLC
Premier OIL
JKX Oil & Gas
Fortune Oil
Dragon Oil PLC
Cairn Energy PLC
Melrose resources
AMEC PLC
Sage Group
Invesys PLC
ARM HLDGS
Imagination technologies group
Spirent communications
Laird PLC
Cable & Wireless communications
Telecom Plus
KCOM Group PLC
Vodafone Group
Centrica PLC
Severent Trent
65
Ticker
SCAA
BLT
AAL
ANTO
JMAT
GKN
SAB
DGE
ULVR
ABF
TATE
BKG
PSN
DMGT
TSCO
SIA
MLC
SGC
CSRT
BQE
BGC
DPH
SBS
SKP
BBY
KIE
SMIN
WEIR
IMI
BBA
PMO
JKX
FTO
Dragon Oil
Energy PLC
MRS
AMEC
SGE
ISYS
ARM
IMG
SPT
LRD
CWC
TEP
KCOM
VOD
CNA
SVT
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 4. Data sample descriptive statistics
N
SOX
applicable
Before SOX
After SOX
Mean
Std. Error of
Mean
Valid
Missing
not log_Tobins_Q
539
0
.1747662218
.00887859470
.1463731688
.20612877102
Net_Prof_Mar
539
0
.0747843871
.02410414801
.0646944461
.55961090391
ROE
539
0
.1255744166
.01177862507
.1307550718
.27345695932
ROE_prev
539
0
.1146374693
.01195939895
.1285750875
.27765387314
ROA
539
0
.0392619195
.00630212414
.0533907036
.14631246806
CAPEX
539
0
-.1680620113
.01783024096
-.0511952183
.41395353432
log_Assets
539
0
.3178471548
.04091322171
.3259307121
.94985663759
Leverage
539
0
.2575721705
.00848447690
.2489848653
.19697878495
Sales_Growth
539
0
.1046670210
.00621175249
.1035854954
.14421436613
Dividend_Yield
539
0
.0282587368
.00122331852
.0253551254
.02840101977
Cash_Flow
539
0
.0933512630
.00384336973
.0942619707
.08922910733
Liquidity_OCF
539
0
.5113483750
.03176709691
.3617318436
.73751678796
Tangibility
539
0
.5906196824
.00985491857
.6359183424
.22879547055
log_Tobins_Q
98
0
.2201155586
.02418741712
.1604217396
.23944321335
Net_Prof_Mar
98
0
.0750064638
.01214191268
.0704074688
.12019880310
ROE
98
0
.0928848241
.02264267988
.1256995975
.22415109484
ROE_prev
98
0
.1722937343
.02595242515
.1519473640
.25691590132
ROA
98
0
.0489929670
.00687489439
.0467938681
.06805798225
CAPEX
98
0
-.0933914339
.01253205072
-.0762434523
.12406097261
log_Assets
98
0
.4121839044
.06700504664
.4164451246
.66331611992
Leverage
98
0
.2960399428
.01402503676
.2893103542
.13884078035
Sales_Growth
98
0
.1045883429
.00902229552
.1040378188
.08931616878
Dividend_Yield
98
0
.0295023565
.00626799067
.0143331655
.06204994193
Cash_Flow
98
0
.1078234640
.00659336286
.0964414145
.06527096226
Liquidity_OCF
98
0
.4356942876
.04199036331
.3325377302
.41568338901
Tangibility
98
0
.6390326120
.01694610170
.6605631735
.16775784802
log_Tobins_Q
441
0
.2252693931
.01015960051
.2083803481
.21335161068
Net_Prof_Mar
441
0
.1105014637
.00626258733
.0871938688
.13151433393
ROE
441
0
.1934912934
.01173366410
.1682104287
.24640694601
ROE_prev
441
0
.1798969175
.01185653319
.1644223958
.24898719700
ROA
441
0
.0719055869
.00315399532
.0657722145
.06623390173
CAPEX
441
0
-.1058017305
.00848632787
-.0565601793
.17821288522
log_Assets
441
0
.4285385202
.02953676801
.4369160175
.62027212816
Leverage
441
0
.3150579087
.00718564375
.3060329068
.15089851869
Sales_Growth
441
0
.1038489290
.00493113480
.1029016326
.10355383070
Dividend_Yield
441
0
.0301474432
.00190178183
.0208434092
.03993741848
Cash_Flow
441
0
.1254815444
.00300559448
.1174566043
.06311748411
Liquidity_OCF
441
0
.5434817370
.01844332962
.4817552144
.38730992210
Tangibility
441
0
.6428877811
.00750336340
.6600100567
.15757063131
66
Median
Std. Deviation
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 5. Kolmogorov-Smirnov and Shapiro-Wilk normality tests
Kolmogorov-Smirnova
Statistic
Net_Prof_Mar
ROE
ROE_prev
ROA
CAPEX
log_Assets
Leverage
Sales_Growth
Dividend_Yield
Cash_Flow
Liquidity_OCF
Tangibility
log_Tobins_Q
.288
.157
.160
.194
.332
.061
.054
.144
.218
.090
.185
.078
.057
df
Shapiro-Wilk
Sig.
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
67
Statistic
.348
.809
.807
.656
.342
.970
.924
.836
.618
.943
.721
.955
.969
df
Sig.
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 6. Probability distribution of variables
68
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
69
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 7. Pearson’s correlation matrix
ln_Tobins_ Net_Prof_
Q
Mar
ln_Tobins_Q
ROE
ROE_prev
ROA
CAPEX
ln_Assets
Leverage
Sales_Growth
Dividend_Yield
ROE_prev
ROA
CAPEX
ln_Assets
Leverage
Sales_Growt Dividend_Y
h
ield
Cash_Flow
Liquidity_
OCF
Tangibility
Dummy
_SOX
Pearson Correlation 1
.031
.239**
.190**
.176**
.178**
-.121**
-.070*
.034
-.317**
.132**
-.066*
-.268**
.100**
Sig. (2-tailed)
.315
.000
.000
.000
.000
.000
.022
.264
.000
.000
.030
.000
.001
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1
.346**
.134**
.606**
-.265**
.081**
-.074*
.072*
.005
.239**
.292**
.044
.043
N
Net_Prof_Mar
ROE
1078
Pearson Correlation .031
Sig. (2-tailed)
.315
N
1078
.000
.000
.000
.000
.008
.015
.018
.875
.000
.000
.152
.156
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation .239**
.346**
1
.384**
.574**
.052
.137**
.089**
.065*
.120**
.341**
.162**
-.035
.138**
Sig. (2-tailed)
.000
.000
.000
.000
.089
.000
.004
.033
.000
.000
.000
.248
.000
N
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation .190**
.134**
.384**
1
.325**
.041
.164**
.075*
.012
.110**
.288**
.135**
-.013
.104**
Sig. (2-tailed)
.000
.000
.000
.000
.180
.000
.014
.693
.000
.000
.000
.661
.001
N
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation .176**
.606**
.574**
.325**
1
-.017
.157**
-.191**
.145**
.056
.495**
.357**
-.036
.134**
Sig. (2-tailed)
.000
.000
.000
.000
.581
.000
.000
.000
.064
.000
.000
.240
.000
N
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation .178**
-.265**
.052
.041
-.017
1
.115**
-.014
-.065*
.073*
.014
-.215**
-.218**
.086**
Sig. (2-tailed)
.000
.000
.089
.180
.581
.000
.639
.034
.017
.637
.000
.000
.005
N
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation -.121**
.081**
.137**
.164**
.157**
.115**
1
.264**
-.093**
.307**
.198**
-.003
.323**
.488**
Sig. (2-tailed)
.000
.008
.000
.000
.000
.000
.000
.002
.000
.000
.912
.000
.000
N
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation -.070
*
-.074
*
.089
**
.075
*
-.191
**
1078
-.014
.264
**
Sig. (2-tailed)
.022
.015
.004
.014
.000
.639
.000
N
1078
1078
1078
1078
1078
1078
1078
*
*
**
.072
.012
.145
.264
.018
.033
.693
.000
.034
.002
.003
N
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation -.317
Sig. (2-tailed)
.000
.005
.120
.875
.000
.110
.000
**
.056
.073
.064
.017
-.093
1078
Sig. (2-tailed)
**
-.065
*
-.091
Pearson Correlation .034
**
.065
**
1
*
.307
.000
70
**
-.091
.126
.000
**
**
**
.126
**
-.101
**
-.116
**
.376
**
1078
.144**
.003
.000
.001
.000
.000
.000
1078
1078
1078
1078
1078
1078
1
-.062
1078
-.062
.041
*
*
.047
-.032
.000
.000
.123
.296
1078
1078
1078
1078
.161
.000
**
.195
**
.041
1
.119
**
.044
.133
.146
.000
**
1078
.022
.465
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
N
Cash_Flow
Liquidity_OCF
Tangibility
Dummy _SOX
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation .132**
1078
.239**
.341**
.288**
.495**
.014
.198**
-.101**
.119**
.161**
1
.689**
.073*
.186**
Sig. (2-tailed)
.000
.000
.000
.000
.000
.637
.000
.001
.000
.000
.000
.016
.000
N
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation -.066*
.292**
.162**
.135**
.357**
-.215**
-.003
-.116**
.195**
.044
.689**
1
.209**
.036
Sig. (2-tailed)
.030
.000
.000
.000
.000
.000
.912
.000
.000
.146
.000
.000
.232
N
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation -.268**
.044
-.035
-.013
-.036
-.218**
.323**
.376**
.047
.133**
.073*
.209**
1
.111**
Sig. (2-tailed)
.000
.152
.248
.661
.240
.000
.000
.000
.123
.000
.016
.000
N
1078
.000
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
Pearson Correlation .100**
.043
.138**
.104**
.134**
.086**
.488**
.144**
-.032
.022
.186**
.036
.111**
1
Sig. (2-tailed)
.001
.156
.000
.001
.000
.005
.000
.000
.296
.465
.000
.232
.000
N
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
1078
71
1078
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 8. Anti-image covariance and correlation matrices
Liquidity_
OCF
Anti-image
Covariance
Anti-image
Correlation
Liquidity_OCF
Cash_
Flow
Sales_
Growth
Net_Prof_Ma
r
ROE_
prev
ROE
ROA
CAPEX
log_
Assets
Leverage
Dividend_
Yield
Tangibility
.411
-.267
-.066
-.064
.012
.042
.001
.098
.113
.039
.013
-.131
Cash_Flow
-.267
.381
.013
.065
-.060
-.048
-.086
-.063
-.105
.016
-.054
.051
Sales_Growth
-.066
.013
.933
.035
.017
-.013
-.055
.010
.071
.036
.039
-.054
Net_Prof_Mar
-.064
.065
.035
.548
.048
-.026
-.233
.183
-.031
-.030
.008
.021
ROE_prev
.012
-.060
.017
.048
.799
-.140
-.066
.019
-.048
-.068
-.021
.044
ROE
.042
-.048
-.013
-.026
-.140
.577
-.186
-.026
.037
-.151
-.047
.045
ROA
.001
-.086
-.055
-.233
-.066
-.186
.368
-.053
-.053
.131
.027
-.001
CAPEX
.098
-.063
.010
.183
.019
-.026
-.053
.794
-.096
-.010
-.027
.144
log_Assets
.113
-.105
.071
-.031
-.048
.037
-.053
-.096
.697
-.104
-.171
-.210
Leverage
.039
.016
.036
-.030
-.068
-.151
.131
-.010
-.104
.716
-.019
-.234
Dividend_Yield
.013
-.054
.039
.008
-.021
-.047
.027
-.027
-.171
-.019
.880
-.034
Tangibility
-.131
.051
-.054
.021
.044
.045
-.001
.144
-.210
-.234
-.034
.674
Liquidity_OCF
.581a
-.673
-.106
-.134
.020
.087
.002
.172
.211
.072
.021
-.248
Cash_Flow
-.673
.632a
.022
.143
-.109
-.103
-.229
-.114
-.203
.030
-.094
.100
Sales_Growth
-.106
.022
.734a
.049
.020
-.018
-.094
.012
.087
.044
.043
-.068
Net_Prof_Mar
-.134
.143
.049
.648a
.073
-.046
-.518
.278
-.050
-.048
.012
.034
ROE_prev
.020
-.109
.020
.073
.820a
-.206
-.121
.024
-.064
-.090
-.025
.060
ROE
.087
-.103
-.018
-.046
-.206
.728a
-.403
-.039
.058
-.234
-.066
.072
a
ROA
.002
-.229
-.094
-.518
-.121
-.403
.680
-.098
-.104
.254
.048
-.003
CAPEX
.172
-.114
.012
.278
.024
-.039
-.098
.504a
-.130
-.013
-.033
.196
a
log_Assets
.211
-.203
.087
-.050
-.064
.058
-.104
-.130
.580
-.147
-.218
-.306
Leverage
.072
.030
.044
-.048
-.090
-.234
.254
-.013
-.147
.535a
-.023
-.337
a
Dividend_Yield
Tangibility
.021
-.094
.043
.012
-.025
-.066
.048
-.033
-.218
-.023
.737
-.044
-.248
.100
-.068
.034
.060
.072
-.003
.196
-.306
-.337
-.044
.524a
72
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 9. Unrotated common factor matrix
Factor
Liquidity_OCF
Cash_Flow
CAPEX
Sales_Growth
ROA
ROE
Net_Prof_Mar
ROE_prev
log_Assets
Leverage
Tangibility
Dividend_Yield
1
2
3
.999
.693
-.214
.196
.371
.170
.300
.139
.000
-.119
.207
.045
-.015
.263
.069
.076
.915
.562
.536
.305
.178
-.154
-.123
.049
.000
.166
.069
-.122
-.010
.172
-.026
.212
.656
.521
.470
.392
73
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 10. Factor reliability check (Cronbach's Alpha test)
Capital factor
Cronbach's Alpha
Cronbach's Alpha Based on Standardized Items
N of Items
.334
.507
5
Cronbach's Alpha
Cronbach's Alpha Based on Standardized Items
N of Items
.015
.570
5
Cronbach's Alpha
Cronbach's Alpha Based on Standardized Items
N of Items
.447
.671
5
Performance factor
Risk factor
74
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 11. Multiple linear regression model summary and coefficients
Change Statistics
Model
R Adjusted Std. Error of
R Square
R Square R Square the Estimate
Change
F Change
df1
.833
1336.24
4
1 .913a
.833
.832
.0874780
Unstandardized
Coefficients
Model
B
Std. Error
(Constant)
.193
.004
Dummy _SOX
.017
.006
Factor 1
.036
Factor 2
Factor 3
Sig.
Durbin-
df2
Change
Watson
1073
.000
.692
Standardized
Coefficients
Beta
Collinearity Statistics
t
Sig.
Tolerance
VIF
54.973
.000
.039
3.066
.002
.950
1.053
.003
.162
12.685
.000
.961
1.041
.086
.003
.359
28.197
.000
.962
1.040
-.177
.003
-.818
-65.266
.000
.992
1.009
75
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 12. Estimated Maximum likelihood model description and fit
Model Description
Model Type
Model ID
Tobins_Q
Model_1
ARIMA (1.0.0)
Model Fit
Percentile
Fit Statistic
Mean
Stationary
Rsquared
R-squared
RMSE
MAPE
MaxAPE
MAE
MaxAE
Normalized BIC
Minimum Maximum
.897
.897
.897
.069
125.0
35151.3
.043
.497
-5.315
.897
.069
125.0
35151.3
.043
.497
-5.315
5
10
50
90
95
.897
.897
.897
.897
.897
.897
.897
.897
.069
.069
.069
125.0
125.0
125.0
35151.3 35151.3 35151.3
.043
.043
.043
.497
.497
.497
-5.315
-5.315
-5.315
.897
.069
125.0
35151.3
.043
.497
-5.315
.897
.897
.897
.069
.069
125.002
125.002
35151.371 35151.371
.043
.043
.497
.497
-5.315
-5.315
Model Statistics
Model Fit statistics Ljung-Box Q(18)
Model
Number
Predictors
Tobins_Q-Model_1
4
of Stationary
squared
.897
RStatistics
DF
Sig.
22.261
17
.175
Number of
Outliers
0
ARIMA Model Parameters
Estimate SE
Tobins_Q- Tobins_Q
Model_1
No Transformation
Constant
t
Sig.
.188
.008
24.845
.000
AR
Lag 1
.643
.024
27.157
.000
SOX
No Transformation
Numerator
Lag 0
.028
.012
2.364
.018
Factor 1
No Transformation
Numerator
Lag 0
.031
.003
12.297
.000
Factor 2
No Transformation
Numerator
Lag 0
.063
.003
21.339
.000
Factor 3
No Transformation
Numerator
Lag 0
-.177
.003
-61.005
.000
76
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 13. Standardized residual plot
Standardized residual normal distribution probability plot
Standardized residual scatter plot against predicted value
77
The effect of Sarbanes-Oxley Acts listing requirements on European Companies value
Appendix 14 Analysis of variance (ANOVA)
Model
1
Regression
Residual
Total
Sum of Squares
df
Mean Square
F
Sig.
40.902
4
10.225
1336.244
.000a
8.211
1073
.008
49.113
1077
78