EXPLAINING THE USE OF GRAPHS IN
CORPORATE TAKEOVERS
Keith Houghton
ANU College of Business and Economics, The Australian National University,
Canberra, ACT 0200, Australia.
McCombs School of Business. University of Texas,
Austin, Texas 78712, USA.
and
Stephen Smith
Department of Information Systems, The University of Melbourne,
Melbourne, VIC 3010, Australia.
June 2006
Acknowledgement
The authors gratefully acknowledge the assistance of Dr Liz Roberts and Professor
Christine Jubb for comments on an earlier draft of the paper. Remaining errors are the
responsibility of the authors. Comments on the present draft are welcome to either
author. Please do no quote present version of the paper without the authors’
permission.
EXPLAINING THE USE OF GRAPHS IN CORPORATE TAKEOVERS
ABSTRACT
The inclusion of graphs in financial reports is widespread yet little is known of what
may motivate or explain their presence. This study analyses the presence and extent of
the use of graphs in formal documents which, for regulatory reasons, are required to
be issued during corporate control (takeover) contests in Australia.
The results suggest that in a takeover contest, the presence and frequency of the use of
graphs is not a “neutral” or random phenomenon. Several factors explain the extent
(and, to a lesser extent, the presence) of the use of graphs in takeover documents.
These factors include: the nature of the takeover contest (hostile or friendly), the
financial performance of the target, and takeover bid value. Inconsistent with
expectations, the ownership dispersion (a proxy for the degree of sophistication of
users of the financial reports) did not have a significant effect.
The results are consistent with the position that graphs are used by corporate
management as a “tool” in corporate takeovers to positively manage the information
signals provided to users of financial reports.
2
EXPLAINING THE USE OF GRAPHS IN CORPORATE TAKEOVERS
1
INTRODUCTION
For modern corporations, the appearance and content of financial reports is closely
managed by those responsible for their production. This is true both for disclosures
which are produced as a consequence of regulatory requirements, as well as those
which are voluntary and produced at the discretion of management. The presence and
extent of graphical disclosures contained in financial reports are almost always
discretionary in the hands of corporate management; although if included the nature
of the disclosures is subject to certain constraints. While we know a great deal about
the information content and relevance of conventional financial measurements and
related disclosures; by contrast relatively little is known about matters relating to nonconventional information cues contained in financial reports including graphical
representations. This study develops and tests several propositions about the
circumstances under which graphs1 are used in a certain class of financial reports. It
seeks to contribute to a fuller understanding of the role of graphs in the
communication of financial information.
The documents examined in this study are those issued during corporate control
contests commonly referred to as takeovers. This setting was chosen because: (1)
decisions made on the basis of these documents are often economically significant,
and (2) the sometimes adversarial nature of these contests means that various
participants have observable (or at least inferable) and differing economic incentives.
2
2.1
BACKGROUND
Use and regulation of graphical information in accounting
Graphs are said to be a common way of illustrating corporate performance including
financial performance (Beattie & Jones, 1996). Some of the impetus behind
graphically presenting financial data may be driven by the perceived needs of
1
stockholders. This may be the motivation behind Section 409 of the Sarbanes-Oxley
legislation2, which encourages the use of graphs as a way to make reports easier to
understand. Indeed as observed in Kosslyn (1994), graphs may assist in attracting and
holding the reader’s attention. Editors and graphic designers frequently use
information graphics to entice the reader into a story and also assist in the
understanding of its content3 (Schriver, 1996). Similarly, it can be argued that
corporate managers may find graphs a useful way of drawing attention to selected
performance indicators in information releases such as annual reports (Beattie &
Jones, 1992).
While the inclusion of a graph in a financial report is largely, if not exclusively in
some circumstances4 at the discretion of corporate management, once included there
are constraints in what the graph (or other non-conventional information) may depict.
Specifically, there are constraints that require the absence of contradictions between
the graphical representations and the underlying financial data. In many countries
auditing standards constrain the way in which graphs can be constructed. Auditing
standards in Australia (AUS 212), the United Kingdom (SAS 160), Hong Kong (AG
3.255), New Zealand (AS 518), Singapore (SSA 14 (1996), South Africa (AU 322),
the United States (SAS 8), and the International Standards on Auditing (ISA 720)
require that “other information” does not contradict the audited numerical financial
statements5.
Legislative requirements also influence graphical representations. For example, in
Australia the relevant legislation, the Corporations Act (2001), specifically prohibits
all types of misleading or deceptive conduct in relation to disclosure documents
including prospectuses used in takeover contests. Moreover, investors have
demonstrated their willingness to litigate where omissions or misstatements occur in
certain related financial reports (prospectuses) (see Baxt, 1995; Foster, 1996). The
Corporations Act covers formal takeovers and, in Section 670A, prohibits the issuing
of a document if there is “a misleading or deceptive statement in the document”.
Importantly, the Act (Section 9) defines the term “statement” very broadly to include
any information that “… conveys a message”. While there is no case law on the
2
matter known to the authors it seems reasonable to conclude that this broad definition
would include non-conventional information representations including graphs.
2.2
Impression management in accounting
The provision of graphs in reports can be analysed from the perspective of a wider
body of literature dealing with the management of perceptions generally, sometimes
referred to as “impression management”. This wider set of literature tells us, for
example, that subjects make differing decisions when presented with tabular
information (somewhat like conventional accounting reports) as opposed to
graphically presented data (see Dilla & Steinbart, 2005). Also we know that
improperly designed graphs can affect a reader’s choices (Arunchalam, Pei &
Syeinbart, 2002). The importance of research into the various methods of altering
perceptions of company performance has been acknowledged; “… while corporate
annual reports have been transformed from minimal and legalistic exercises in
financial statistics into a creative and flamboyant mix of text, images, and accounting
data, such changes have gone largely unexamined in the research literature”
(Hopwood, 1996, p55). Some would argue that little has changed since Hopwood
made these observations. Hopwood further asserted that accounting research into
corporate reports have, to date, been narrowly focussed on the accounts. This work he
argues, typically examines accounting methods while ignoring the wider influences of
the documents, and the type and method by which information is conveyed. These
comments seem equally applicable to a wider range of financial information sources
including prospectuses, takeover documents and other public corporate documents.
Although a number of distinguished scholars have published research into the use of
non-conventional disclosures (including graphs) in financial reports, the absence of a
significant body of literature in this area is somewhat surprising. Our surprise stems
from: (1) graphs, photographs and other graphic design elements have been widely
used by many organisations as “rhetorical” devices in reports to shareholders since the
early 1960s (Graves, Flesher, et al & Jordan. 1996; Preston, Wright, et al. & Young
1996); and (2) a large body of research in diverse disciplines such as cognitive
3
psychology and marketing has established that the format used to present information
significantly influences the message conveyed.
3
THE RESEARCH SETTING OF THE PRESENT STUDY
Corporate takeovers were chosen as the context for this study because they are a
clearly defined event in which certain groups of information providers are required by
legislation to inform specified stakeholders (stockholders) so that these stakeholders
might make a more informed choice as to a particular course of action (generally
retain or sell shares). Formally, a corporate takeover is an economic event that
involves one company (“bidder”) attempting to acquire the shares of another
(“target”) in order to gain a material equity interest. This may either concur with the
wishes of the target’s board of directors (and management), that is a “friendly”
takeover, or without the agreement of the target’s board; a “hostile” takeover. For the
purposes of this study, takeovers are defined as those events that involve applications
to Australia’s corporate regulator, the Australian Securities and Investments
Commission (ASIC), and lodgement of documents pursuant to the relevant legislation
relating to corporate control. This definition means that for any one contest there are
two parties only, a bidder and a target. It is possible there many be multiple bidders
in the same time period with a “shared” target. This study relates only to entities
where the target company is a publicly traded company. The study is also limited to a
narrow period when access to data, in the exact form it was provided to stockholders
and not reworked to conform to a regulatory template, was available.
Given data availability, a study of takeovers avoids several of the problems associated
with other settings where information is proprietary or where a self-selection or
survivor bias may be present. From an accounting perspective, studying information
presentation behaviour in takeovers is also worthwhile because of the absence of
similar research in this context.
The remainder of this paper is organized as follows. In the next section we provide
some theoretical support to suggest that, certain company characteristics, the nature of
4
the takeover (friendly or hostile), and the company’s role in the takeover (bidder or
target) can help explain the presence and extent of graphs in takeover documents. The
hypotheses developed are then tested in the next section. We then provide the results
of the two models developed (one for presence of graphs and the other for extent of
use) and a discussion of those results. Conclusions and implications are then
explained.
4
4.1
THE LITERATURE AND THE DEVELOPMENT OF HYPOTHESES
Signalling and agency theory
As noted above in most instances, there is no obligation to include graphs in
accounting documents. One might conclude that the theory behind the use of graphs is
similar to other voluntary disclosures. This may not be entirely correct as other
voluntary disclosures generally provide new information, whereas a graph generally
reintroduces and/or highlights information already contained elsewhere in the
financial report.
In part, the voluntary disclosure literature uses either signalling theory (e.g. Penman
1980; Dye 1985, 1986; Hughes 1986; Clarkson, Dontoh, et al. Richardson & Sefcik
1992; Dye 1998) and/or agency theory (e.g. Ruland, Tung et al & George 1990; Mak
1996) as the theoretical basis for research into factors influencing the volume, nature
and type of information disclosure. The basic proposition of signalling theory is that
firms with good news (such as positive earnings forecasts) have incentives to
voluntarily disclose that information in order to distinguish themselves from less
desirable firms6 (Verrecchia 1983; Dye 1985; Verrecchia 1986; Dye 1998). Therefore,
by using signalling theory, graph production can be explained as a means of
graphically highlighting selected aspects of performance so that firms can
differentiate themselves from less desirable alternatives.
As noted below, we argue that theoretically, certain company characteristics, their
position in the takeover (bidder or target) and the nature of the takeover (friendly or
hostile) can explain the presence and extent of the usage of graphs.
5
4.2
Company characteristics
Each company involved in a takeover contest has certain characteristics that place
them in differing circumstances. These include profitability, nature of the ownership
of the company and the economic scale of the takeover contest (that is, the size of the
target company).
Profitability
From a signalling theory perspective, companies with higher levels of profitability
have incentives to highlight this performance to distinguish themselves from other
firms. Therefore, one explanation for the usage of graphs is that managers incorporate
them into reports to emphasize success (see Steinbart, 1989, Beattie & Jones, 1992;
1994). The reverse is also true. When performance has been unfavourable, a graph
would highlight poor performance perhaps leading to additional monitoring or other
less desirable actions by principals. Therefore, managers may be hesitant to include a
graph. In the context of this study it is the profitability of the target that is most at
issue for it is the performance of the target that is often central to the arguments in
favour or not in favour of the takeover contest.
Nature of ownership
The type of information released in annual reports and takeovers is likely to be
influenced by a firm’s ownership structure (Jensen & Meckling, 1976). If
shareholders are financially sophisticated investors, then one could argue that detailed
accounting and other technical data are likely to be of greatest benefit7. However,
other types of investors may have different information preferences. While graphics
may be ignored or used with lesser weight by some sophisticated report users, they
may well be used in the buying, holding and selling decisions of less experienced, less
wealthy, less educated and financially unsophisticated investors (Chang & Most 1985;
Epstein & Pava 1993)8. Therefore, the greater the proportion of shares held by this
type of (generally economically smaller) investor, the more likely that graphs will be
included. One can argue that graphs are of more use to this type of investor because
they match this group’s information and information processing circumstances. This
study conjectures that the more disperse the shareholder base, the more likely this
6
type of unsophisticated investor will be represented in the share register. Therefore, it
is argued that the more disperse the shareholdings of the target, the greater the
likelihood that graphs will be provided in takeover documents as shown in this study9.
Size and takeover value
Several studies (Buzby 1975; Chow & Wong-Boren 1987; Cooke 1991) suggest that
firm size is an indicator of the amount of resources available for preparing and
disseminating information10. Applying this to the takeover context, the value of a
takeover rather than the value of each firm per se may be more indicative of the
amount of resources likely to be used by participants to produce and disseminate
information.
It is argued, that a target company’s profitability and ownership structure and the
value of the takeover each explain the presence and extent (number) of graphs
disclosed in takeover documents by information suppliers. This leads to the following
formal hypothesis:
H1
The value of the takeover, the target firm’s profitability and ownership
structure will be positively and significantly related to the frequency of
the presence of graphs and extent of graph usage in financial reports
issued by bidder and target companies in the context of a takeover
context.
4.3
Adversarial conflict and the presence and extent of graphs
In a takeover, the level of adversarial conflict essentially depends on whether a
takeover is “hostile” or “friendly”. A hostile takeover is distinguished by the presence
of mutually exclusive actions by the bidder and the target (accept or reject). In this
circumstance, the bidder and target send different signals to shareholders to support
these differing proposals. To gain support (accept or reject) from shareholders, bidder
and target management also have incentives to interfere with each other’s signals.
Adversarial conflict is likely to be high because the outcome often has a significant
impact on an organisation’s future, but whether the takeover is successful or not is
7
often dependent on the actions of many individuals and corporations not under the
direct control of management of either the target or the bidder11. The association
between adversarial conflict and information release suggests that graphs are more
likely to be used in hostile rather than friendly takeovers.
In a friendly takeover, documents from the bidder and the target send complementary
signals about the bid. So, rather than having an adversarial relationship, the firms are
likely to jointly emphasise the benefits of synergy and the disadvantages of individual
existence. Because the firms involved recommend the same course of action
(complementary signals), less information needs to be produced and both produce
similar price signals and so do not need to counter noise created by the other’s
documents as happens in a hostile takeover. Therefore, graphs are less likely to be
used in friendly takeovers because (1) less information will be produced in total
(information can be jointly produced); and (2) the lower adversarial conflict reduces
the likelihood that impression management devices such as graphs will be used.
The previous discussion on the effect of the nature of the takeover (friendly or hostile)
will influence the presence and extent of graphs. This leads to the following
hypothesis:
H2
Adversarial conflict will be positively and significantly related to the
frequency of the presence of graphs and the extent of graph usage in
financial reports issued by bidder and target companies in the context of a
takeover context.
4.4
Role in the takeover
A number of studies indicate that management may manipulate the contents of
financial reports for strategic purposes12. This can be observed in one form where
competing parties choose to issue markedly different sets of accounts for the same
entity in support of their respective arguments (McBarnet D & J,. 1992; McBarnet,
Weston et a. 1993).
8
Agency theory provides an explanation for different stakeholders to behave
differently in respect of information issuance. It proposes that one reason takeovers
occur is to replace managers who are not maximising shareholder wealth (Jensen
1988)13. For this reason, differential information release strategies will occur between
bidder and target companies as the management of the latter faces the possibility of
replacement (Mørck, Shleifer et al & Vishny 1988). In these cases, the larger quantity
of information produced and highlighted by managers of the target resisting the bid
may be part of an entrenchment strategy (Shivdasani 1993)14.
This leads to the following hypothesis:
H3
The presence of graphs and the extent of graph usage will be significantly
greater in financial reports issued by target companies compared with
bidder companies.
5
RESEARCH METHOD
The research questions identified are tested using archival data held by Australia’s
corporate regulator, ASIC. The sample was taken from a time period when the data
available was in a form that exactly replicated the materials that were provided to
stockholders (as opposed to filings which are provided in a defined template). The 62
takeovers in the sample are listed in Appendix A. It comprises all companies in
which a public offer was made for ordinary shares of a corporation listed on the
Australian Stock Exchange in a one year period in the 1990s15. (This is one of the last
periods of time that access was available to these documents via the Corporate
Adviser Securities Data library [CASD])16.
Copies of all documents were obtained from the CASD library. The documents
provided information in respect to whether the document was lodged by a company
seeking to acquire another company (takeover bidder) or a company that was being
sought by another company (takeover target). These documents also revealed whether
9
the target’s management accepted or rejected the initial takeover proposal by the
bidder. These takeover documents contained a substantial amount of data in a variety
of formats, including graph information. Additional information was sourced from the
Australian Stock Exchange (through its Datadisc service) and the SDC Silver Platter
Takeover database.
A total of 750 completed takeovers were registered in the Silver Platter database over
that defined period. The subset used in this study excludes share repurchase or buyback schemes, changes to the structure of a group of related companies, takeovers of
proprietary or private companies (685), and takeovers which were abandoned by the
bidder soon after the initial announcement and so did not involve both parties
producing a complete set of documents (3). The sample that remains might be
characterised as being the takeovers where there was significant public interest and a
competitive and “arms-length” bidding process.
6
6.1
MODELS AND VARIABLE MEASUREMENT
Models
To test the hypothesis two models were used: (1) to test the presence of graphs; and
(2) to test the extent of usage.
Graph presence (1/0) =  + 1 hostile/friendly takeover + 2 bidder/target +
3 profit/loss + 4 share dispersion + 5 takeover value; and
Extent (count)
=  + 1 hostile/friendly takeover + 2 bidder/target +
3 profit/loss + 4 share dispersion + 5 takeover value.
Binary logistic regression was used to estimate the presence/absence model, and
TOBIT regression was used to estimate the extent (count) model. TOBIT regression
uses an iterative maximum log likelihood procedure to estimate models where the
distribution of the dependent variable is truncated (in this case with a minimum value
of zero and no upper limit). In both cases, tests of the residuals using procedures
10
described by Pagan & Vella (1989) indicate that assumptions have not been
violated.17
6.2
Dependent variable measurement
As implied in the two models, there are two dependent variables: (1) as a binary
presence/absence variable to indicate whether any graphs were present in each
document; and (2) as a count of the number of graphs (extent of graph use) in each
document in the sample. Both presence/absence and count variables were used
because presence/absence and magnitude are related but different phenomena.
6.3
Independent variable measurement
Takeover type
Takeover type was used to represent the degree of adversarial conflict. Documents
were classified as either hostile or friendly depending on whether the target’s
management accepted or rejected the initial takeover proposal by the bidder. This was
determined by examination of the relevant takeover documents, and verified using the
SDC Silver Platter Takeover database. Where management rejected the initial offer
the takeover was classified as hostile. Where the reaction was to not reject the offer
the takeover was classified as friendly.
This included five instances where
management had no opinion, but according to the independent expert’s report
provided in the takeover documentation, management had stated that the offer was
fair but did not recommend acceptance or rejection.
Role in takeover (bidder/target)
Takeover documents were coded according to the producer of the document. All
information accompanying the offer document (bidder), or the reply to the offer
document (target), was treated as a single document.
11
Takeover value
Takeover value was based on the initial bid price and its accompanying documents.
The advantage of this approach is that the graphs in many of these documents
illustrate the bid per share offered.
Target stockholder dispersion
Stockholder dispersion of the target firm was used as an indicator of the level of
financial sophistication of stockholders. This was based on the assumption that the
more disperse the stockholder base, the greater likelihood that financially
unsophisticated stockholders are present. If only a few stockholders dominated the
share register in a stock exchange listed company, less sophisticated investors were
likely to be fewer in number. Dispersion was measured using a Herfindahl index of
share dispersion similar to that used in Santerre & Neun (1986).
n
HERF   Pi / T 
The index is described by the formula:
2
i 1
where Pi is the fraction owned by the ith largest stockholder, and T is the total number
of ordinary shares issued. If a single stockholder holds all shares, the index will equal
one. It approaches zero as the number of stockholders and the evenness of holdings
increases18.
To calculate the level of dispersion, P (the fraction of shares held) was obtained for
each of the top 20 stockholders of each target company. This information is included
for each target company. Because the proportions often changed substantially as a
result of the takeover (regardless of whether or not the attempt was successful), the
proportions of shares obtained were based on the last disclosure made prior to the
announcement of the takeover. For most companies, the disclosures were within one
month of the announcement.
12
Target profitability
As discussed earlier, signalling theory predicts that successful companies were more
likely to highlight their performance to distinguish themselves from less successful
firms. One way to operationalize this relationship was to use net profit before interest
and tax on the basis that profitable companies have incentives to highlight this
performance to differentiate themselves from unprofitable companies. Unfortunately,
net profit is of limited direct usefulness as a performance measure because net profit
can also be an indicator of the size and value of a company and may be highly
correlated with these variables. This relationship was problematic because any model
that incorporates company size or other related variables its net profit will be at least
partially redundant, and may give rise to multi-collinearity issues.
To avoid such problems, this study used a binary variable to indicate whether the
company was profitable or not in the most recent year. This approach eliminated the
size component but maintains a reasonably high correlation with the original measure
(r = 0.42). Another option was to deflate net profit by size (some preliminary analysis
was conducted using that variable). However, it was excluded in the final analysis
because it proved to be a less parsimonious option. That is, it provided no explanatory
power beyond that obtained using the conceptually less complex profit/loss indicator
variable19.
7
7.1
RESULTS
General characteristics of the sample
As indicated in Panels A to C in Table 1 there were 62 takeover contests (23 hostile
and 39 friendly) involving 61 bidders (one bidder launched two takeovers), and 55
targets (7 targets were involved in two separate takeovers). From these contests, 124
sets of documents were produced. Of these 124 documents, 32 contained one or more
graphs. Of the 32 documents containing graphs, 19 were produced for hostile
takeovers. Interestingly, these documents contained 75 per cent of all graphs in the
entire sample. The targets in takeovers produced approximately 67 per cent of all
13
graphs, the majority of which (53 per cent of all graphs) were produced or hostile
takeovers. In all, 104 of the original 124 documents were produced for takeovers of
profitable target companies, 28 of which used graphs
The majority of takeovers in the sample involved modest monetary values generally
less than $A50 million (mean $A48.18 million, sd. 81.06). The majority of takeovers
were expected therefore to involve relatively low levels of complexity and limited
resources. The level of shareholder dispersion was high with the mean dispersion
index at .807 (sd .195). Some 71% of target companies are loss-making with a mean
level of profitability of $A3.06 million (sd. 10.59).
Approximately one third of the takeovers (22 of 62) involved companies operating in
the primary resources sector (mining, forestry, etc.). This is not surprising given that a
high proportion of publicly listed companies in Australia were involved in this sector.
The remainder of the sample were fairly evenly distributed over a wide variety of
other industries, such as media, transport, property, and manufacturing.
Table 2 indicates that the majority of the graphs produced were either bar graphs
(46%), or line graphs (34%) or a combination of the two (6%). Three-dimensional
perspectives were uncommon (5%), and of the 3D bar charts, only one was a true
three-dimensional graph with an x, y and z-axis.
Place Tables 1 and 2 here
The correlation coefficients for the dependent and independent variables are shown in
Table 3. Notable aspects of the data included (1) the high correlation between profit
and takeover value (0.72) suggesting possible multicollinearity issues; (2) the modest
correlations between most other variables; and (3) the negative correlation between
share dispersion and all variables except the type of takeover (friendly/hostile), which
suggests that dispersion may have an effect via an interaction with takeover type.
Place Table 3 here
14
7.2
Testing of hypotheses
Two separate sets of analysis were conducted to test the graph presence/absence and
the extent of graph usage parts of the hypotheses20.
Hypothesis 1 predicted that the presence and extent of graph usage in financial
documents is explained by certain company characteristics including the value of the
takeover and the target firm’s profitability and ownership dispersion. Hypothesis 2
made the prediction that presence and extent of graph usage is determined by whether
the takeover was hostile or friendly. Hypothesis 3 predicted that the source of the
information (bidder or target) influences the presence and extent of graph usage.
7.2.1 Graph presence/absence
The regression analysis in Table 4 shows that the presence/absence model has only
modest explanatory power (r-squared = 0.125). The theory underlying Hypothesis 1
predicted that graphs were more likely to be found in large takeovers, and to be
produced by profitable firms and firms with disperse stockholdings. The regression
model provides little support for this prediction, with the profitable/unprofitable
variable only weakly significant (p<0.1), and dispersion and takeover value not
significant. Results show strong support for Hypothesis 2: the coefficient for the
hostile/friendly variable is both positive (as predicted) and statistically significant (p =
.008). Finally, only marginal support was found for Hypothesis 3, with the
target/bidder variable being weakly significant (p = .067).
Place Table 4 here
7.2.2 Number of graphs (extent)
The results of the extent of use of graphs reveals a more powerful model (Adj Rsquared = 0.24) with several independent variables being significant. The increased
degree of variability of the dependent variable used here (as compared to the previous
15
0/1 variable) appears to be responsible for this increase in explanatory power. Table 5
shows that both profit/loss and takeover value are significant predictors of the extent
of graph usage. One can interpret the result in the following way: five additional
graphs were produced if the target was profitable and one graph was produced for
every $27 million increase in the value of the takeover. Hypotheses 2 and 3 are also
supported, with the hostile/friendly and target/bidder coefficients both significant and
in the expected direction. Indeed, stockholder dispersion is the only variable of
interest that is not statistically significant.
Place Table 5 here
8
CONCLUSION
Signalling theory and agency theory can be used to explain why certain company
characteristics (profitability, ownership structure, takeover value) and the degree of
adversarial conflict may be associated in the variation in graph presence and usage in
takeover contest documentation. The issues examined relate to what may explain the
presence of graphs in takeover documents and the extent of their use. The possible
explanations examined in this study were:

The value of the takeover, the target firm’s profitability and ownership
structure will be positively and significantly related to the frequency of
the presence of graphs and extent of graph usage;

the presence or absence of adversarial conflict in a takeover; and,

the role of the originator of the documentation (bidder or target).
In respect of the testing of the main effects in Hypotheses 2 and 3, substantial support
exists for the hypothesis that company characteristics and “adversarial” variables have
an effect (especially on the extent of graph usage). The results confirm that the use of
graphs in takeover documents is not an “even” random event and their usage can be
linked with variables that represent economic incentives.
16
However, these results need to be considered in the light of several important
limitations. A number of potential issues stem from the sample of takeovers used. The
year (1996), the limited time frame (one year), the country (Australia), and the
relatively small number of takeovers could all have biased the results. Therefore,
results may be country specific, and may have been confounded by both the range of
industries and the size of the companies used in the sample.
Importantly and somewhat surprisingly, only a relatively small number of takeover
documents contained graphs (32 out of 124, or 26% of the sample). This was
unexpected and raises more questions than answers. Perhaps the companies producing
these documents prefer other means of communicating (including by electronic
means) with user groups. In this connection the increase in the use of the media may
be an area of study warranting further inquiry.
It is also important to note that a relatively small selection of predictor variables was
used to explain corporate behaviour. Incorporating additional variables and
interactions between those variables would no doubt have explained more variance.
For example, the impact of friendly/hostile may be more marked for targets with
disperse stockholdings. In the absence of robust theory, however, such interactions
are difficult to explain and so were not tested.
Additionally, several potential factors make alternative conclusions difficult to draw.
Firstly while we can observe graphs being produced, much of the theory assumes that
decisions regarding the content of documents are made by those who have control of
the economic management of the firm. In truth, content may actually be determined
by an independent party such as a marketing agency or an investment banking
advisor. Secondly, the documents we examined are only a subset of the total amount
of material sent to stockholders by various parties including financial advisors.
Finally, the theory presented here assesses impression-management in terms of the
number of graphs used. It is likely, however, that the issues of what information is
presented graphically and how it is formatted are also important.
17
Much more investigation can be done even within the context of Australian takeovers.
For example, while we might now conclude that the use of graphs in takeover
documents is explainable by certain economic variables, we still know nothing of the
nature or content of these graphs. Is there any contradiction between the graphical
representations and the underlying financial data? If the presence and extent of the
production of the graphs can be influenced by economic variables, it is possible, even
likely, that the content in the graphs will also be non-randomly influenced by
economic factors. These questions are outside the scope of the present study but may
be worthy of investigation.
18
Appendix A: Takeovers used in this study
Hostile/
Acquirer (Predator) Name
Target Name
Takeover
No. of
Friendly Value ($m) graphs
Australian Industrial Development Corp. AIDC Ltd
Friendly
42.49
1
Email Ltd
Atlas Steels Ltd
Hostile
99.30
18
Marmon Australia Pty Ltd
Atlas Steels Ltd
Hostile
94.90
0
Evans Deakin Industries Ltd
Austoft Holdings
Hostile
48.40
9
Australian Metal Holdings Pty. Limited
Australian Agricultural Company
Hostile
115.30
4
Hollyment Pty Ltd
Australian Agricultural Company
Friendly
115.30
5
Dori Pty Ltd
Australian Consolidated Equities
Hostile
15.03
1
Mayne Nickless Ltd
Australian Medical Enterprises
Hostile
138.40
8
Vietnam Frontier Fund
Barlile Corp Ltd
Friendly
5.60
0
Intermin Resource Corporation Limited
Black Mountain Gold NL
Friendly
2.30
0
Caxton Holdings Limited
Blackwood Industries Limited
Friendly
3.00
4
Vitalaire Australia Pty Ltd
Braids Ltd
Friendly
11.00
0
Welltop Pty Ltd
Broken Hill Holdings Ltd
Friendly
16.30
2
Weston Investments Limited
Cable Television Services Limited
Friendly
2.80
4
QIW Retailers Ltd
Composite Buyers Ltd
Friendly
37.00
0
Mt Edon Gold Mines (Australia) Ltd
Consolidated Resources
Friendly
19.90
0
Forsayth NL
Coolawin Resources Ltd
Friendly
2.90
0
Schaffer Corporation Limited
Delta Corporation Limited
Friendly
2.30
2
GWA Group Ltd
Gainsbrough Hardware Industries Ltd
Friendly
21.90
0
Coeur d'Alene Mines Corp
Gasgoyne Gold Mines NL
Hostile
42.70
27
Belmont Holdings Ltd
Gearhart Australia Ltd
Hostile
6.00
5
GPG Pty Ltd
Global Funds Management Australia Ltd Friendly
0.50
0
Otter Gold Mines Ltd
Gold Resources
Friendly
4.10
0
BT Australia Ltd
Hamilton Island Ltd
Hostile
17.00
16
Pivot Group Ltd
HMA Ltd
Friendly
46.08
0
Homestake Mining Co
Homestake Gold of Australia
Friendly
162.20
33
19
Hostile/
Takeover
No. of
Acquirer (Predator) Name
Target Name
Friendly Value ($m) graphs
Winwood Forest Industries Pty Ltd
Hudson Timber and Hardware Ltd
Hostile
0.20
8
Eagle Mining Corp NL
Hunter Resources Ltd
Hostile
53.60
0
Nodapt Pty Ltd
Inprint Ltd
Hostile
9.30
0
Woodox Pty Ltd
Inprint Ltd
Hostile
11.40
0
Strategic Equity Pty Ltd
Keygrowth Ltd
Friendly
1.50
0
Haoma Mining NL
Kitchener Mining NL
Friendly
5.80
2
Thorney Holdings Pty Ltd
Lanes Ltd
Hostile
6.00
0
Friendly
25.00
0
Mercantile Mutual Life Insurance Co Ltd Le Fort Capital
Bidvest Group Ltd
Manettas Ltd
Hostile
16.90
0
Foster's Brewing Group Ltd
Mildara Blass Ltd
Friendly
341.90
0
HydroMet Corp Ltd
Mineral Estate Ltd
Friendly
6.80
0
Allders PLC
MS McLeod Holdings Ltd
Hostile
24.10
0
Swissair Associated Companies
MS McLeod Holdings Ltd
Friendly
23.90
0
Arklow Pty Ltd
Offset Alpine Printing Ltd
Hostile
46.60
0
Fobiti Pty Ltd
Offset Alpine Printing Ltd
Friendly
43.80
0
Glenbaye Pty Limited
Oliver J Nilsen (Australia) Ltd.
Friendly
5.30
1
Australian Resources Ltd
Oresearch NL
Friendly
10.00
5
Forrestania Gold NL
Oresearch NL
Friendly
11.10
1
Sons of Gwalia NL
Orion Resources NL
Friendly
74.80
0
Goldfields Ltd
Pancontinental Mining Ltd
Hostile
273.40
53
Dollar Sweets Holdings Ltd
Players Group Ltd
Friendly
0.50
0
Pivot Group Ltd
Primac Holdings Ltd
Hostile
1.90
5
Alliance Properties
Prudential Investment Co
Friendly
4.40
0
Sigma Co Ltd
QDL Pharmaceuticals Ltd
Friendly
38.10
0
Westgold Resources NL
Ramsgate Resources Ltd
Hostile
11.00
9
Boral Ltd
Sagasco
Hostile
128.50
22
Goldspear Australia
Sedelco Ltd
Friendly
1.50
0
Sime Darby Bhd
Sime Darby Australia
Friendly
14.60
0
Capital Energy NL
Stirling Resources NL
Hostile
5.00
4
RG Capital Australia
Sunshine Broadcasting Network Ltd
Friendly
62.50
6
20
Hostile/
Takeover
No. of
Acquirer (Predator) Name
Target Name
Friendly Value ($m) graphs
Seven Network Ltd
Sunshine Broadcasting Network Ltd
Friendly
82.40
0
Parbury Ltd
Toby Industries Ltd
Friendly
0.70
0
Broken Hill Proprietary Co Ltd
Tubemakers of Australia Ltd
Hostile
446.90
14
Memory & Electronic Components PLC
University Paton Ltd
Friendly
11.10
0
William Resources Inc
Valdora Minerals NL
Friendly
14.80
0
Pegasus Gold Inc
Zapopan NL
Friendly
99.00
0
21
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28
Table 1: Descriptive Statistics
Panel A: Descriptive Statistics – All cases
Std.
Min
Takeover Value ($m)
Net Profit
Stockholder Dispersion
Max
0.20 446.90
Skewness
Kurtosis
Mean Deviation Statistic Std. Err Statistic Std. Err
48.18
81.06
3.11
0.22
13.56
0.43
-29.84
63.50
3.06
10.59
2.76
0.22
19.57
0.43
0.14
1.00
0.81
0.19
-1.51
0.22
4.71
0.43
(N = 124)
Panel B: Descriptive Statistics – Graph Present/Absent
Std.
Min
Takeover Value ($m)
Absent
(N = 91)
Graphs
Net Profit
0.20 446.90
Kurtosis
Mean Deviation Statistic Std. Err Statistic Std. Err
37.77
70.44
4.08
0.25
18.78
0.50
-29.84
63.50
1.51
9.50
2.49
0.25
21.76
0.50
Stockholder Dispersion
0.14
1.00
0.82
0.19
-1.77
0.25
2.82
0.50
Takeover Value ($m)
0.20 446.90
76.88
100.72
2.03
0.41
4.85
0.80
present Net Profit
(N = 33)
Max
Skewness
Stockholder Dispersion
-2.20
63.50
7.34
12.32
3.35
0.41
13.57
0.80
0.33
0.99
0.78
0.20
-1.02
0.41
0.17
0.80
29
Panel C: Descriptive Statistics – Graph Present/Absent by Takeover Type
Takeover
Min
Type
Takeover Value ($m)
Friendly
N = 64
Net Profit
Max
Mean
Std.
Skewness
Kurtosis
Dev
Stat Std. Err Stat Std. Err
0.50 341.90
31.78
61.63
4.20
0.30
19.34
0.59
-29.84 17.88
0.33
6.95
-1.96
0.30
11.28
0.59
Stockholder Dispersion
0.14
1.00
0.80
0.21
-1.61
0.30
1.91
0.59
Takeover Value ($m)
0.20 446.90
51.97
87.61
3.79
0.45
16.73
0.87
-11.70 63.50
4.32
13.56
3.30
0.45
14.49
0.87
Absent
Hostile
N = 27
Friendly
N = 14
Graph
present
Hostile
N = 19
Net Profit
Stockholder Dispersion
0.61
0.99
0.87
0.12
-1.17
0.45
-0.11
0.87
Takeover Value ($m)
2.30 162.20
51.18
61.28
1.00
0.60
-0.63
1.15
-2.20 27.00
7.02
10.07
1.27
0.60
0.37
1.15
0.66
0.23
-0.33
0.60
-1.19
1.15
95.82 120.12
1.75
0.52
3.02
1.01
Net Profit
Stockholder Dispersion
0.33
Takeover Value ($m)
0.20 446.90
Net Profit
0.98
-2.10 63.50
Stockholder Dispersion
0.61
30
0.99
7.58
14.01
3.89
0.52
16.18
1.01
0.86
0.12
-0.94
0.52
-0.82
1.01
Table 2: Types of Graph Used
Type
Total
Area
%
6
2%
125
46%
3
1%
Bar-Line
15
6%
Line
91
34%
Pie
9
3%
11
4%
Stacked bar
9
3%
Grand Total
269
100%
Bar
3D Bar
3D Pie
31
Table 3
Zero-Order Correlations
Friendly/
Number of graphs
Graph Presence
Friendly/Hostile (0/1)
Num
Graph
Hostile
graphs
Presence
(0/1)
-
0.67
0.32
0.14
-
0.26
-
Bidder/Target (0/1)
Loss/Profit (0/1)
Profit
Dispersion
Bidder/
Loss/
Share
Takeover
Profit
Dispersion
Value
0.22
0.29
-0.06
0.42
0.13
0.22
0.24
-0.10
0.21
0.00
0.27
0.19
0.22
0.21
-
0.00
0.00
0.00
0.00
-
0.42
-0.14
0.24
-
-0.17
0.72
-
-0.07
Target (0/1) Profit (0/1)
-
Takeover Value
N = 124, Bold = significant at p<0.05 (two-tailed).
32
Table 4
Graph presence as explained by Company Characteristics, Adversarial Level
and Role in Takeovers
(joint test of H1, H2 and H3)
Method: Maximum likelihood - Binary Logit
Number of observations
Variable
124
Coefficient
Std. Error
z-Statistic 1-tailed prob.
Hostile
1.111
0.465
2.391
0.008
Target
0.671
0.447
1.501
0.067
Target net profit > 0
0.870
0.612
1.422
0.077
Stockholder Dispersion
-1.544
1.091
-1.415
0.579
Takeover value ($M)
0.003
0.003
1.250
0.106
C
-2.568
0.620
-4.141
0.000
Mean dependent var
0.266
S.D. dependent var
0.444
S.E. of regression
0.420
McFadden R-squared
0.125
Sum squared residual
20.800
LR statistic (5 df)
17.025
Probability(LR stat)
0.004
33
Table 5
Extent of graph usage as explained by Company Characteristics, Adversarial
Level and Role in Takeovers
(joint test of H1 H2 and H3)
Method: Maximum likelihood - Censored Normal (TOBIT)
Left censoring (value) at zero
Number of observations
124
Coefficient
Std. Error
Hostile
8.286
2.828
2.930
0.002
Target
4.681
2.665
1.757
0.040
Target net profit > 0
5.560
3.314
1.678
0.047
-10.018
7.060
-1.419
0.922
0.037
0.018
2.029
0.021
-17.271
4.374
-3.948
0.000
Stockholder dispersion
Takeover value ($M)
C
z-Statistic 1-tailed prob.
R-squared
0.280
Mean dependent var
2.169
Adjusted R-squared
0.243
S.D. dependent var
5.373
S.E. of regression
4.676
Sum squared residual
34
2,558.119
ENDNOTES
1
For the purpose of this study, a graph is defined as a device for pictorially representing or
summarising qualitative data, quantitative data, or both. It facilitates visual comparison of quantities,
and visual analysis of other quantitative aspects of data such as distributional properties and
relationships between variables (The Macquarie Dictionary).
A more complete description of the properties of graphs is contained in the Encyclopaedia Britannica
(2002):
Most graphs employ two axes, in which the horizontal axis represents a group of
independent variables, and the vertical axis represents a group of dependent variables.
The most common graph is a broken-line graph, where the independent variable is
usually a factor of time. Data points are plotted on such a grid and then connected
with line segments to give an approximate curve of, for example, seasonal
fluctuations in sales trends. Data points need not be connected in a broken line,
however. Instead they may be simply clustered around a median line or curve, as is
often the case in experimental physics or chemistry.
If the independent variable is not expressly temporal, a bar graph may be used to
show discrete numerical quantities in relation to each other. To illustrate the relative
populations of various nations, for example, a series of parallel columns, or bars, may
be used. The length of each bar would be proportional to the size of the population of
the respective country it represents. Thus, a demographer could see at a glance that
China's population is about 30 percent larger than its closest rival, India.
This same information may be expressed in a part-to-whole relationship by using a
circular graph, in which a circle is divided into sections, and where the size, or angle,
of each sector is directly proportional to the percentage of the whole it represents.
Such a graph would show the same relative population sizes as the bar graph, but it
would also illustrate that approximately one-fourth of the world's population resides
in China. This type of graph, also known as a pie chart, is most commonly used to
show the breakdown of items in a budget.
In analytic geometry, graphs are used to map out functions of two variables on a
Cartesian coordinate system, which is composed of a horizontal x-axis, or abscissa,
and a vertical y-axis, or ordinate. Each axis is a real number line, and their
intersection at the zero point of each is called the origin. A graph in this sense is the
locus of all points (x,y) that satisfy a particular function.
The Act states “each issuer reporting under section 13(a) or 15(d) shall disclose to the public on a
rapid and current basis such additional information concerning material changes in the financial
condition or operations of the issuer, in plain English, which may include trend and qualitative
information and graphic presentations, as the Commission determines, by rule, is necessary or useful
for the protection of investors and in the public interest”.
2
3
For a somewhat different perspective on how editors, politicians and others use information graphics
as a rhetorical device rather than as a decision aid, see Huff (1954), Tufte (1983), and Wainer (1997).
4
Since 1992, the SEC in the US has required that all listed firms include a graph of shareholder returns
in the proxy statement filed for their annual meeting. This type of requirement is rare and does not
apply to the chosen research setting, corporate takeover offers in Australia.
ISA 720 states that “the auditor should read the other information to identify material inconsistencies
with the audited financial statements” (par. 2). A material inconsistency is defined as existing “…when
other information contradicts information contained in the audited financial statements” (par. 3).
5
6
Darrough & Stoughton (1990), and Feltham & Xie (1992) have expanded on the basic hypothesis,
explaining that the type of information disclosed depends on whether capital market forces or product
market forces dominate. When capital market forces dominate, good news is disclosed while bad news
is withheld. When product market forces dominate, bad news is disclosed while good news is withheld
35
in order to deter new competitors from entering the market. Empirical work by Clarkson (Clarkson
1994) and Clarkson, Kao, and Richardson (1994) supports this extension of signalling theory.
7
Analysts and other experienced report users, for example, make extensive use of accounting data and
appear to find graphs far less useful (Epstein and Pava 1993). Gniewosz (1989) gives a detailed
account of analysts’ use of accounting reports, which indicates that relevant information is re-keyed
into databases which then become the primary source for analysis.
8
Additional evidence that graphs are used by some shareholders comes from a 1992 survey of U.K.
retail shareholders in which over 75 per cent of respondents expressed a desire for more graphs and
charts in annual reports to help explain financial performance (1992).
9
Diversity of shareholdings is also likely to be closely related to company size and takeover value in
that larger organisations are more likely to have diverse ownership.
10
Agency theory provides another explanation for the relationship between information production and
size. It states that firms which have high political visibility (e.g. large firms and firms in highly visible
industries) are more likely to use accounting procedures that reduce expected earnings and the variance
of earnings than firms not subject to these political pressures (Gagnon 1967; Watts & Zimmerman
1978). Watts & Zimmerman (1978 p. 223) state that in particular, managers of firms who are blamed
for a “crisis” are more likely to manage earnings in this way. Such changes are not directed towards
shareholders per se, but instead designed to reduce political pressures from other stakeholders such as
regulators, government, labour unions, and the media.
In the same way that political influences create incentives for organisations to vary reported earnings
through accounting policy choices, political influences can affect the appearance of reports. This
includes using graphs and other images to highlight positive aspects of performance in non-financial
politically sensitive aspects of a firm’s operations.
Traditionally, political costs have been proxied by company size (Watts & Zimmerman 1978; Makar &
Alam 1998). However, because this study is concerned with graph use in takeovers, takeover value
rather than the size of each company involved is probably better as a proxy for political visibility.
Although size is likely to be highly correlated with takeover value, the value of the takeover rather than
the size of the corporations involved per se is intuitively more likely to influence visibility. For
instance, a bid either by a large corporation for a small corporation, or by a small corporation for
another small corporation is probably less likely to be politically visible than a bid for a large
corporation.
Unfortunately, the degree of political visibility of a company or industry is highly subjective, with size
being only a crude indicator of the level of political exposure (see Watts & Zimmerman 1978).
Furthermore, any assessment of the extent to which graphs are used to reduce political costs must by
nature be a highly subjective process. Because of the somewhat arbitrary nature of this type of analysis,
it is not conducted in this study. This is not to say that it cannot be done, merely that this study does not
analyse takeovers in this manner.
11
The level of uncertainty may be high for management and shareholders of the target if they perceive
that management will lose their jobs and that the company will undergo significant structural changes.
However, it is also likely that in many cases management of bidder will experience high levels of
uncertainty. For example, the defensive strategies employed by management of the target may reduce
the level of uncertainty they experience and increase uncertainty for the bidder.
12
For example, income increasing accounting practices (data manipulation) have been found across a
variety of threats including financial weakness (e.g. Watts & Zimmerman 1978; Kalay 1982; Begley
1990), and hostile takeovers. As examples of the effect of financial weakness, Moyer (1990),
examining commercial banks, found a positive relationship between proximity to capital adequacy
requirements and the extent to which income increasing accounting methods were adopted. Petroni
(1992), examining insurance firms, found that “financially weak” insurers biased their estimates of loan
losses downwards. Examining the effect of hostile takeovers, Christie & Zimmerman (1994) found that
compared to the control group, firms subject to hostile takeover bids make more frequent use of income
increasing accounting methods.
13
Support for Jensen’s proposition has been found in many studies (see Weisbach 1993, p. 200).
36
14
See, for example, Jensen (1988) and Mitchell & Lehn (1990). Also note that not all hostile takeover
defences are an attempt to entrench existing management. Instead, management may resist a takeover
bid to improve the terms of the takeover offer. Empirical work by Schwert (2000) indicates that many
hostile takeover defences are of this type, and that the hostility is actually a form of aggressive
bargaining by target managers.
15
In the period from 19-Jan-1995 and 19-Jan-1996
16
Until 1999 the CASD library was located in Melbourne, Australia, and all documents held were
publicly accessible. Following the purchase and subsequent restructuring of CASD by the Thomson
Publishing group, the library was relocated to Manila, Philippines, and is no longer publicly accessible.
17
Use of Poisson regression to estimate the count model was also considered. However, although
theoretically appropriate, it was not used in this study because the data set violated the Poisson
estimation requirement that the conditional mean and variance of the dependent variable (DV) be equal
(QMS 1998, p.457). The primary reason for the violation of this assumption is that the majority of
takeovers contain no graphs, resulting in a highly skewed data set for the DV.
18
Following Cronbach (1987) and Jaccard et al (1990), the continuous variables takeover value, and
shareholder dispersion are “centred” to avoid potential multicollinearity problems arising from the use
of bilinear interaction (multiplicative) terms.
19
It should be noted that, conceptually, the profit of the bidder could have been modelled in the same
way. However, to provide a parsimonious solution, and minimise degrees of freedom problems, only
the target company’s profit is used here.
20
Following Cronbach (1987) & Jaccard et al. (1990), the continuous variables takeover value, and
shareholder dispersion are “centred” (The centring procedure recommended by Cronbach (1987)
simply involves subtracting a variable’s mean value from each of its observations) to avoid potential
multicollinearity problems arising from the use of bilinear interaction (multiplicative) terms.
37