facial width-to-height ratios and restatements

Facial width-to-height ratios

and restatements

Loes Goorden

August 13, 2012

Facial width-to-height ratios

and restatements

Master thesis Department Accountancy,

Faculty of Economics and Business Studies,

Tilburg University

Loes Goorden

s425013

August 13, 2012

Supervisor: S. (Stephan) Hollander

Master thesis accountancy

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Table of contents

Introduction 2

Literature review 4

Research question and hypotheses 11

Sample 13

Research method

Variable measurement 14

Prediction of restatements 16

Model 19

Findings 25

Conclusion 36

References 38

Appendices 41


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Introduction

This study is about facial width-to-height ratios and restatements of the financial

statements. The idea is that it is possible to explain some aspects of a persons’ character by

measuring certain points in the face. This is possible because the amount of testosterone of a

person influences his character and it also influences the shape of his face (Verdonck et al.,

1999). To explain some of the aspects of the character of a person, the facial width-to-height ratio

is used in this study. The facial width-to-height ratio is the distance between the left and right

zygion divided by the distance between the upper lip and mid brow. Previous literature found

evidence that males with wide faces, which turns out to be the same as a high facial width-to-

height ratio, are more aggressive, more untrustworthy, have a higher psychological sense of

power and have better financial performance. When the CEO of the company has a wide face,

and the directors have not, this might be a reason to suppose that these companies have a higher

chance of restatements of the financial statements, since the CEO takes the final decisions in the

company and the directors only give advice. So the research question of this study is: ‘Are facial

width-to-height ratios1 of CEOs and directors associated with restatements in the financial

statements of their company?’

To test this research question, the facial width-to-height ratios are measured based on

photographs of the CEO and all male directors of several companies. Then, companies are

indicated to have a higher likelihood than other companies to have to restate their financial

statements of the year 2009 in the near future. When the data for all variables in the model is

collected, a correlation matrix is created and a binary logistic regression is run for the model.

After the regression is run, some independent variables are winsorized to see whether they

significantly influenced the results of the model.

The dummies that measure the facial width-to-height ratio of the CEO and the directors,

which are called DUM1, DUM2 and DUM3, turn out not to be significant in the models, which

1 When the term ‘width-to-height ratio’ is used, there is on the background the underlying definition of characteristic

traits, because characteristic traits are measured by using the facial width-to-height ratio as will be discussed in the

literature review section.


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means that the facial width-to-height ratios of the CEO and the directors are not associated with

the likelihood of restatements of the financial statements. However, the independent variable

LEV has a positive significant coefficient. This means that when the leverage of the company

increases, the likelihood that the company has to restate its financial statements for the year 2009

decreases.

This study is relevant, both academic and societal. It is relevant for academic purposes,

since there already is evidence that certain characteristic traits are associated with the facial

width-to-height ratio. This study investigates the relationship between the facial width-to-height

ratio and accounting, namely, the likelihood of restatements of the financial statements. Aspects

of biology, psychology and accounting are combined in this study, which is a new phenomenon

in the accounting literature. It also is very relevant for society. There are a lot of people that take

decisions based on the financial statements, for example investors. When there is something

wrong in the financial statements which is discovered after the publication, the financial

statements have to be restated. When people already took decisions based on the wrong financial

statements, this may cost huge amounts of money. This, in turn, is very costly for the society as a

whole.

The rest of the paper is organized as follows. The next section is the literature review

section. In this part existing literature is discussed which is related to the topic of this study. The

third section is the research question and hypotheses section. Here the research question and

hypotheses are formulated and motivated. The fourth section is the sample section, which

explains what kind of companies are in the sample. The fifth section is the research method

section. This section consists of three subsections, namely, the variable measurement section, the

prediction of restatements section and the model section. The sixth section is the findings section,

in which the findings of the research are discussed. Finally, the seventh section is the conclusion.

In this section the summary, the conclusion, the limitations and ideas for future research are

discussed. After these seven sections, there is an overview of the literature that is used in this

study. This is called the references section. At the end of the paper there are some appendices.


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Literature review

There are several reputation-damaging events that can harm a company, or the CEO or

directors of the company. One of these events is fraud. The paper by Rezaee (2005) gives the

following definition of financial statement fraud: “Financial statement fraud is a deliberate

attempt by corporations to deceive or mislead users of published financial statements, especially

investors and creditors, by preparing and disseminating materially misstated financial

statements”. The paper by Gerety and Lehn (1997) gives several examples of what they

understand by accounting fraud. Some examples are: failing to file any documents at all with the

SEC, inflation or shifting of revenues and/or earnings, make false or misleading statements about

the financial prospects of the firm, the allegation of inadequate internal controls and improperly

accounting for loan loss reserves. Despite the fact that accounting fraud seems to be a very wide

concept, there are not enough observations for fraud for the year 2009 in the sample to use it as

the dependent variable in this research. That is why the research is based on observations for

restatements for the year 2009, which is another reputation-damaging event. A restatement only

can occur when the auditor failed to detect and prevent all material errors (Blankley, 2012). In

this way, a restatement is closely related to fraud, because this also holds for fraud to occur.

The role of the CEO of the company is different from the role of the directors of the

company. The two major roles of the directors are monitoring and advising. The CEO takes the

final decisions. This means that the CEO and the directors of the company have to work closely,

and that they should have a good professional relationship with each other. From recent literature,

however, it becomes clear that the relationship between CEOs and directors is not always in

balance (Wong, 2011). In some cases, the directors do not stand up against the CEO when

necessary, because they think that their renown is less than that of the CEO. When this is the

case, directors find it difficult to question the CEO’s judgment. In other cases the relationship can

be disturbed when the directors feel that the CEO’s executive star rises. In this case, directors

may find its presence and authority diminish over time. That the relationship between CEOs and

directors is not always in balance is alarming news, because if directors have no influence on

decisions of the CEO, the CEO decides on important matters on his own. If a CEO then is a

person who is dealing in his own interest, the whole company may suffer because of this. So it is


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important to be able to predict the characteristic traits of the CEO of the company. The

characteristic traits may explain what actions a CEO will take in certain situations. So if

characteristic traits of the CEO are known by the company, the company may be able to find

directors who are able to cooperate with the CEO and stand up against him when necessary. This

is important for the company as a whole.

According to the paper by Rezaee (2005), a combination of five factors is a prerequisite

for the commission of financial statement fraud, which is closely related to restatements as is

discussed in the first paragraph. These five factors are cooks, recipes, incentives, monitoring and

results. The first factor indicates who is involved in the financial statement fraud. It seems that in

the majority of cases, the CEO and/or CFO of the company are associated with financial

statement fraud. The second factor, recipes, explains that there are several ways to commit

financial statement fraud. The paper by Gerety and Lehn (1997) also gives examples of fraud.

The third factor, incentives, gives the most common incentives of people why they commit

financial statement fraud. The most common incentive is the economic incentive. Companies,

and therefore also the CEO and the directors, want to meet expectations of investors. It is very

expensive for the company if expectations are not met, even when the results are close to the

expectations (Skinner and Sloan, 2002). Monitoring, the fourth factor, is a very important

mechanism to prevent and detect financial statement fraud. If the monitoring part is not good, the

chance of financial statement fraud is higher than when monitoring is good. The fifth factor,

results, gives the main reason for financial statement fraud. If the results of the company are not

as expected, and it looks like the expectations are not going to be met, it is tempting for the CEO

and the directors to cook the books and commit fraud. From the combination of these five factors,

the incentives and opportunities of the CEO and the directors to commit fraud become clear. Of

course, not every CEO or director will commit fraud when the expectations are not going to be

met. It depends on the character of the person. So again, it is important for the company to know

the characteristic traits of their employees, especially the higher level employees who really have

influence.


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There is recent existing literature that examines the characteristic traits of people based on

facial characteristics. Facial characteristics can be measured in several ways. One possibility is to

first compose groups of people that have the same self-reported personality traits. Then, you

should delineate feature points in the face, like the eyes and the mouth, on each individual face in

the group. If these points of each individual are compared with each other for the whole group, an

average for each point can be calculated. These averages indicate the points that a person with a

particular characteristic trait has. This is the method like it is used by Penton-Voak et al. (2006).

Another method that is used more recently, is the method of using the facial width-to-height ratio.

This is the ratio of the distance between the left and the right zygion on the one hand and the

distance between the upper lip and the mid brow on the other hand. From figure 1 it becomes

clear how these points are measured. The two pictures at

the bottom show that the distance between the upper lip

and the mid brow is about the same for the two persons.

The two pictures on the right show that the distance

between the left and the right zygion is smaller for the girl

than for the boy. As is concluded in the paper by Carré et

al. (2010), discussed further on in this study, the facial

width-to-height ratio is a superior measure compared to

other measures to examine characteristic traits from facial

characteristics. Furthermore, the advantage of facial

width-to-height ratio compared to the method as used by

Figure 1 Weston, Friday and Liò (2007) Penton-Voak et al. is that for the facial width-to-height

ratio it is not needed to make assumptions about self-reported personality traits. Another

advantage of the facial width-to-height ratio compared to the method of Penton-Voak et al. is that

it is not needed to form groups of people with certain similarities. Also, the facial width-to-height

ratio is more applicable for this research, because only the photographs of the faces are needed

and not the persons themselves or their opinions. One disadvantage of using the facial width-to-

height ratio is that not all photographs are taken perfectly from the front, which gives some

difficulties in measuring the left and the right zygion and the upper lip and the mid brow. In the

section of the research method it will be discussed how this difficulty is solved.


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As is stated in the previous section, it is possible to examine characteristic traits of people

based on facial characteristics. This, however, only holds for male. This is because facial

characteristics are related to the level of testosterone a male has. One of the papers that examined

this is the paper by Verdonck et al. (1999). This paper compared boys with delayed puberty with

a control group that exists of boys at different stages of pubertal development. The boys with

delayed puberty were treated with testosterone to see what happened. The conclusion of the paper

is that cranial growth is related to levels of testosterone in adolescence for male. Another paper

that examined the relationship between facial characteristics and the level of testosterone is the

paper by Weston, Friday and Liò (2007). They use the facial width-to-height ratio. It is measured

in a way that it is independent on body size. They find that bizygomatic width (the distance

between the left and the right zygion) diverges at puberty between males and females, but upper

facial height (the distance between the upper lip and mid brow) does not. This means that males

have a higher width-to-height ratio than females. From the first paper it can be concluded that, for

male, it is possible to determine the level of testosterone by looking at facial characteristics,

namely the width-to-height ratio. From the second paper it can be concluded that males have a

higher width-to-height ratio than female. So these two papers find evidence that the facial width-

to-height ratio only is applicable for males.

Furthermore, the level of testosterone determines particular characteristic traits for male,

like aggressive behavior, untrustworthiness, psychological sense of power and leadership success.

The term ‘aggression’ is defined by Baron and Richardson (1994). The definition is as follows:

“Any form of behavior directed toward the goal of harming or injuring another living being who

is motivated to avoid such treatment”. One of the papers that find evidence of the relationship

between facial width-to-height ratio and aggressive behavior is the paper by Carré and

McCormick (2008). This paper looks at the width-to-height ratio of photographs of people

participating in a laboratory setting and of photographs of hockey players. The idea behind this

paper is to examine whether it is possible to determine what kind of people, based on the width-

to-height ratio, are ‘aggressive’. In the laboratory setting, people were asked to choose between

three options, which stand for ‘reward earned’, ‘aggression’ and ‘protection’. The character trait

‘aggressive’ for hockey players is measured using the number of penalty minutes per game

obtained over a season. The result of the paper is that “for men variation in the width-to-height


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ratio from neutral faces may be an honest signal of propensity for aggressive behavior”. Carré

and McCormick extended their research with the paper by Carré, McCormick and Mondloch

(2009). In this paper, participants had to assess the level of aggression, dominance, masculinity,

trustworthiness and attractiveness of male based on neutral photographs. The photographs were

assessed based on a 7-point Likert scale. In this paper, Carré, McCormick and Mondloch also

found evidence of the relationship between width-to-height ratio on the one hand and aggression

and dominance on the other hand. They further extended their research with the paper by Carré et

al. (2010). In this paper they did three experiments. In the first experiment, participants saw the

unmanipulated photographs, the chin-forehead cropped version, the side cropped version, the

blurred version and the scrambled version of the photographs. Then the participants had to assess,

by using a 7-point Likert scale, how aggressive the person on the photograph would be if

provoked. From the first experiment it can be concluded that the width-to-height ratio is an

important factor for estimating aggression. The second experiment examined whether other facial

metrics than the width-to-height ratio could explain variability in estimates of aggression. To

examine this, the unmanipulated photographs of the first experiment were used to provide

numerical values for 61 facial metrics. The numerical values were calculated by using software.

Also, the facial width-to-height ratios were calculated with this software, to see whether the

width-to-height ratios, as computed by the software, correspond to the width-to-height ratios in

the first experiment. The results of the second experiment indicate that there are eight facial

metrics that are correlated with estimates of aggression. After regression analysis it turned out

that facial width-to-height ratio is the only metric that uniquely predicted these estimates. So, the

second experiment in this paper proves that facial width-to-height ratio is a very good measure,

compared to other measures, to examine characteristic traits from facial characteristics. In the

third experiment, faces were created with software and participants had to judge on the degree to

which the person on the photograph appeared threatening. Threat is a variable that is closely

associated with aggression. Then, the facial width-to-height ratios of the photographs were

measured. The result of the third experiment is that the more threatening a face, the higher the

width-to-height ratio is. This is evidence that the width-to-height ratio is associated with

perceived threat. Besides the three papers by Carré, there is another paper that examined the

relationship between facial width-to-height ratio and estimates of aggressive potential, namely

the paper by Short et al. (2011). This paper is different from the other three papers that are


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discussed above, because it looks at differences between face races. The conclusion of this paper

is that there is no own-race advantage when estimating on aggressive potential. This means that it

does not matter whether you estimate the aggressiveness for other face-races or for own face-

races.

But aggression is not the only characteristic trait that is associated with facial-width-to

height ratio. Another paper that examines the relationship between width-to-height ratio and

characteristic traits is the paper by Stirrat and Perrett (2010). They examine the relationship

between facial width and untrustworthiness. In their first two experiments they let participants

play a game from which the measures ‘trust’ and ‘trustworthiness’ can be measured. The only

information available was a photograph of their counterpart. The outcome of the first experiment

is that males with higher facial width-to-height ratios were more likely to exploit their

counterpart’s trust than males with lower width-to-height ratios. The outcome of the second

experiment is that males width ratio is a cue to male trustworthiness. Males with wider faces are

less trusted by participants than males with smaller faces. This is also proven by the third

experiment, in which the width-to-height ratios of photographs were manipulated and participants

had to choose which photograph looked more trustworthy. Taken the three experiments together,

this paper provides evidence that there is a positive relationship between facial width-to-height

ratio and untrustworthiness for males.

There is also a very recent paper that examines the relationship between facial width-to-

height ratio and psychological sense of power, or how powerful one feels, namely the paper by

Haselhuhn and Wong (2011). They conducted two studies. In the first study, participants were

buyers and sellers. They have had instructions about when to buy and when to sell. The

negotiation was via mail, so that they could not see each other’s face. The conclusion of this first

study is that the width-to-height ratio for males is associated with the use of deception. In the

second study, participants had to roll two dices, count the dices together and fill the number into

the survey. They had the opportunity to cheat, because they could fill in whatever they want. The

outcome is that there is a significant positive relation between width-to-height ratio and cheating

for males. This is an indirect measure, because the width-to-height ratio of the face is related to

psychological sense of power, namely, the wider the face the more powerful one feels, and

psychological sense of power results in unethical behavior.


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The last characteristic trait, leadership success, is examined by several researchers. One of

the papers that examined the relationship between facial width-to-height ratio and leadership

success is the paper by Wong, Ormiston and Haselhuhn, (2011). They used a sample of 55

publicly traded Fortune 500 organizations to measure whether there is a relationship between

width-to-height ratio of CEOs and financial performance of their organizations. The outcome of

this research is that CEOs with wider faces have better financial performance than CEOs with

smaller faces. This result is stronger for CEOs who have a leadership team with lower levels of

cognitive complexity. This means that they have lower degrees of differentiation, which means

that they see issues in black and white, and make decisions very quickly. Rule and Ambady

(2008) also examined the relationship between impressions of CEOs and the financial

performance of their companies. Note that they did not use the width-to-height ratio, but just the

impression of the CEO by using photographs. This means that no points were calculated in the

face, but only the first impression of the person on the photograph was used. Rule and Ambady

found that “the participants’ naïve perception of leadership ability from CEOs faces is

significantly correlated to how much profit those CEOs companies make”. From these two papers

it is clear that facial characteristics of CEOs, whether measured by using width-to-height ratio or

not, are related to financial performance of the company.

As can be concluded from the previous section, by looking at facial characteristics it is

possible to tell something about particular characteristic traits for males, like aggressive behavior,

untrustworthiness, psychological sense of power and leadership success. In this research, the

dependent variable is the likelihood of restatements of the financial statements. Therefore, it is

important to determine characteristic traits which are probably associated with making

restatements. As is discussed before, the characteristic traits aggressiveness, untrustworthiness,

psychological sense of power and leadership success all are associated with the facial width-to-

height ratio. So, these characteristic traits can be useful for this research.


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Research question and hypotheses

The research question of this study is: ‘Are facial width-to-height ratios of CEOs and

directors associated with restatements in the financial statements of their company?’

To examine this research question, two hypotheses are formulated. The first thing that

should be examined is whether facial width-to-height ratios of CEOs are associated with

restatements. As is stated in the previous section, Verdonck et al. (1999) and Weston, Friday and

Liò (2007) found evidence that the facial structure of males, the facial width-to-height ratio,

depends on the level of testosterone the person has. Other research has shown that characteristic

traits of males, like aggressive behavior, untrustworthiness, psychological sense of power and

leadership success, are associated with the level of testosterone the person has. So, it can be

concluded that characteristic traits are associated with the facial width-to-height ratio for males.

Males with wider faces are found to be more aggressive, more untrustworthy, have a higher

psychological sense of power, which is associated with more unethical behavior, and have better

financial performance.

These characteristic traits might all be associated with restatements of the financial

statements. Aggression, as defined by Baron and Richardson (1994), is “any form of behavior

directed toward the goal of harming or injuring another living being who is motivated to avoid

such treatment”. When a restatement is made, there were such material errors in the financial

statements without being detected and prevented by the auditor, that it is plausible that someone

wanted to harm or injure others, like the investors of the company. This means that aggression

should be positively associated with restatements. Untrustworthiness implies that someone would

lie if that is better in that situation. When a restatement is made, something was not right in the

financial statements. One possibility of this is that someone is not doing the right thing and thus

is untrustworthy. So, untrustworthiness should be positively associated with restatements.

Psychological sense of power is associated with unethical behavior, as is discussed in the

literature review section. Unethical behavior can be, for example, hiding or changing certain

actions in such a way that a restatement is necessary when these facts are discovered. This means

that psychological sense of power should be positively associated with restatements. When a


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company has a better financial performance (leadership success) than other, comparable

companies, there might be a sign that the company cooked the books or that something in the

financial statements is not as it should be. When this is the case and it is discovered, a restatement

is needed. So, leadership success should be positively associated with restatements.

All four characteristic traits, aggressive behavior, untrustworthiness, psychological sense

of power and leadership success, are associated with higher facial width-to-height ratios and

should be associated with restatements. So, the first hypothesis is:

H1: Facial width-to-height ratios of CEOs are positively associated with restatements of the

financial statements of their companies.

The second point that should be examined is whether facial width-to-height ratios of

directors are associated with restatements. For this purpose, a conditional hypothesis is

formulated. For males, the same story as for CEOs is applicable, so here also the width-to-height

ratios can be used. For females, this does not hold. Unfortunately, there are not enough

companies in the sample of which the board consists only of males. That is why a control

variable is included in the model to control for the percentage of females in the board. The

second hypothesis is:

H2: High facial width-to-height ratios of CEOs are positively associated with restatements of

the financial statements of their companies, when facial width-to-height ratios of

directors are low.


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Board

Low High

CEO Low Base Dummy 3

High Dummy 1 Dummy 2

Sample

The sample of this study consists of companies from the Standard & Poor 1000 of the

year 2009. For all companies, the names of the CEO and the directors are needed. These are

obtained from Execucomp. Based on the names, a photograph is collected for each CEO and each

director. All companies for which the names are unknown or the photographs cannot be found or

are not useable are deleted. Also, all companies with a female CEO are not used in this study,

since the facial width-to-height ratio does not hold for females as is explained in the ‘literature

review’ section. There are 310 companies in the sample for which all facial width-to-height ratios

are measured. A logit regression will be run with the dummy variables for the facial width-to-

height ratios and the control variables FSize, FreeC, FinRaised, No_Quarters_EPSGrowth, LEV,

%Fem, DUM_PoorQualCEO and %PoorQualBoard. There are three dummy variables that

indicate the facial width-to-height ratios, see table 1. The first dummy takes a value 1 when the

width-to-height ratio of the CEO is high and the average facial width-to-height ratio of the board

is low, 0 otherwise. The second dummy takes a value 1 when both width-to-height ratios are

high, 0 otherwise. The third dummy takes a value 1 when the width-to-height ratio of the CEO is

low and the average facial width-to-height ratio of the board is high. When a facial width-to-

height ratio is high, this means that it is in the highest 20% of width-to-height ratios. When it is

low, it is in the lowest 80% of width-to-height ratios. The 80% cut off is separate for the width-

to-height ratios of the CEOs and the width-to-height ratios of the boards. The regression is run to

see whether the facial width-to-height ratio of the CEO and the average facial width-to-height

ratio of the board have influence on whether or not the financial statements have been restated.

From this it can be concluded whether or not a board with on average a relatively high facial

with-to-height ratio is needed.

Table 1 Dummies facial width-to-height ratios


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Research method

Variable measurement

The first thing that is needed to find an answer to the research question is a photograph of

each CEO and all directors of the companies in the sample. A list with all company names, names

of the CEOs and names of the directors was provided by Stephan Hollander and Yachang Zeng.

Based on this list, six students used Google Images to find the right photograph for the CEOs and

the directors. When there was doubt about whether the photograph was of the right person, the

students had to read the text that belonged to the photograph to find out whether the photograph

belonged to the CEO or director that they searched. In most of the times, some background

information about the CEO or director was needed for this, for example for what other companies

the person worked for. A useful website to find the background information is muckety.com. On

that website the name of the person and the names of the companies where that person worked

for are available and in most cases also the photograph of that person is available. In that way the

students were certain that it is the right CEO or director on the photograph. In cases that there still

was doubt, the persons were deleted from the list.

When all photographs are collected, the facial width-to-height ratios have to be

calculated. For this purpose, one of the students programmed software especially for this project.

Since not all photographs are perfectly from the front, some skewness in the photographs was

allowed. However, when photographs were too highly skewed or when the persons on the

photograph smiled too much, the

photographs were coded as ‘1’,

which means that the quality of the

photograph is ‘poor’. This was

done to be able to control for the

best quality photographs, which

means that they are taken as best

as possible from the front and the

smile is natural. In the software,

Figure 2 Mobach (2012) each picture was uploaded and the


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software automatically drew the line between the left and the right zygion and the line between

the upper lip and the mid brow. Two external persons were hired to measure the facial width-to-

height ratios by exactly putting the lines on the right places in the face. In figure 2 there are two

photographs that show how the lines are drawn. The first photograph is a photograph that is

perfectly from the front and the smile is a natural smile, which means that it is a good

photograph. This photograph is coded as ‘0’. The second photograph is a photograph that is a bit

skewed. This photograph is coded as ‘1’. In both cases the lines are at the right places. There was

a guide that explained how to put the lines in each possible situation of the photograph, so that

the two persons that measured the photographs would do exactly the same. As an extra check for

external consistency, ten percent of the photographs was measured by both persons to see

whether they measured the same way or whether there were differences. Furthermore, there was

also a check for internal consistency. For some CEOs or directors, students collected multiple

photographs to be sure that at least one photograph is good enough to measure the width-to-

height ratio. For these CEOs or directors it was possible to measure the facial width-to-height

ratio based on different photographs. From this internal consistency check it becomes clear

whether the right points in the face, so the left and the right zygion and the upper lip and mid

brow, were recognized. When these two consistency checks were done, all photographs were

measured in the same way and for each CEO and director the facial width-to-height ratio was

known. The facial width-to-height ratios are put in an excel sheet together with the company

names. Other data, like the GVKEY and CIK code of each company, is made available by

Stephan Hollander and Yachang Zeng.

The databases Audit Analytics and Compustat are used to collect data about the

companies in the sample. The first database, Audit Analytics, is used to find all companies in the

sample that have restated their financial statements for the year 2009. To do this, the excel sheet

with CIK codes of each company is uploaded in Audit Analytics. The database automatically

produced a list in excel with all company names and information about the restatements. It turned

out that there are only two companies out of the 310 that restated their financial statements of the

year 2009. This is the reason why I decided to use the F-score of each company to predict the

likelihood that the company will restate the financial statements for the year 2009 in the near

future, as explained in the next section, ‘prediction of restatements'. The second database,


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Compustat, is used to find information about the control variables, namely FSize, FreeC,

FinRaised, No_Quarters_EPSGrowth and LEV. To do this, the same list is uploaded, but now the

GVKEY is used. As a result, there is an excel sheet with data for all variables.

Prediction of restatements

There are only two companies in the sample of 310 companies that restated their financial

statements of the year 2009, which is not enough to come up with conclusions. The restatement

sample has to be increased. To do this, the method of the paper by Dechow et al. (2011) is used.

In this paper an F-score is calculated for each company in the sample, which gives an indication

of the likelihood that the company will restate, in the near future, its financial statements of a

particular year. This method is applied to the sample of this study.

The first step is finding data for the seven variables that are used to compute the F-score,

namely, RSST accruals, change in receivables, change in inventory, % of soft assets, change in

cash sales, change in return on assets and actual issuance. For this, the database Compustat is

used again. The first table in the appendix at the end of this paper (Table A1) gives information

about how the variables are measured and which data items are used in Compustat. The variables

are winsorized at 5% and 95%, except for actual issuance because this is a dummy. This is done

to mitigate the outliers. The 5% and 95% levels are chosen because Dechow et al. (2011)

winsorize at 1% and 99%, but their sample consists of 2190 companies. Table 2 contains the

descriptive statistics of the six winsorized variables and the variable actual issuance. The

variables in panel A cannot be benchmarked with the paper by Dechow et al. (2011), since there

is not a table in the paper that contains these values for their whole sample. Panel B and C,

however, can be benchmarked with the paper by Dechow et al. (2011). At the end of this section

is explained how the sample of 310 companies is split in two groups. Panel B contains the values

of the seven variables that are used to compute the F-score for companies with the highest 20% of

F-scores. The variables RSST accruals, change in receivables and change in inventory are smaller

in this study than in the paper by Dechow et al. (2011). The other four variables, % soft assets,

change in cash sales, change in return on assets and actual issuance are bigger in this study than

in the paper by Dechow et al. (2011). The biggest difference between the mean values in this


- 17 -

Table 2 Descriptive statistics variables to compute F-score

Panel A: Total sample

Panel B: Companies with high F-score, ‘high likelihood restatement’

Panel C: Companies with low F-score, ‘low likelihood restatement’

study and the paper by Dechow et al. (2011) is for the variable % soft assets. % soft assets has a

mean value of 64.70% in the paper by Dechow et al. (2011) and 87.56% in this study. This means

that the companies with high F-scores in this study have more soft assets than the restatement

firms in the paper by Dechow et al. (2011). This might be because the value of property plant and

(n = 62)

Variable Min. Max. Median Mean Std. Dev.

RSST accruals -0.03926 0.14755 0.03390 0.04481 0.04631

Change in receivables -0.06347 0.03644 -0.00106 -0.00250 0.02473

Change in inventory -0.03388 0.01868 0 0.00183 0.00922

% Soft assets 72.59% 93.77% 88,10% 87.56% 5.91%

Change in cash sales 0.55162 1.22019 0.98455 0.96515 0.17188

Change in return on assets -0.06682 0.05900 0.00139 0.00103 0.02089

Actual issuance 1 1 1 1 0.00000

(n = 248)


RSST accruals -0.11321 0.14755 0.01371 0.01212 0.06759

Change in receivables -0.06347 0.03644 -0.00417 -0.00805 0.02468

Change in inventory -0.04289 0.01868 -0.00195 -0.00785 0.01584

% Soft assets 20.01% 93.66% 55.52% 53.87% 19.40%

Change in cash sales 0.55162 1.22019 0.91405 0.89430 0.16345

Change in return on assets -0.10879 0.13126 -0.01131 -0.011850 0.05688

Actual issuance 0 1 1 0.96 0.18700


- 18 -

equipment might be decreased because of the economic circumstances. The sample of Dechow et

al. (2011) contains the years 1982 to 2005, while this study contains only the year 2009. The

economic circumstances in 2009 were worse than the years before, which might be a reason for

lower values of property plant and equipment. Lower values of property plant and equipment lead

to higher percentages of soft assets. Panel C contains the values of the seven variables that are

used to compute the F-score for companies with the lowest 80% of F-scores. The variables RSST

accruals, change in receivables, change in inventory and % soft assets are smaller in this study

than in the paper by Dechow et al. (2011). The other three variables, change in cash sales, change

in return on assets and actual issuance are bigger in this study than in the paper by Dechow et al.

(2011). What is striking is that the variable % soft assets now is smaller in this study than in the

paper by Dechow et al. (2011), while for the firms with high F-scores this variable is bigger in

this study compared to the paper by Dechow et al. (2011). Firms with higher F-scores have a

higher % soft assets than companies with lower F-scores. This is exactly what is expected,

because soft assets are relatively easy to manipulate. For example, inventory is an account where

judgment is needed to value it. Furthermore, it can be concluded from table 2 that the variables

are normally distributed, since the median and the mean are close to each other for each variable.

Dechow et al. (2005) motivates why these seven variables are important to calculate the

F-score. The variables RSST accruals, change in receivables and change in inventory are

included because earnings are primarily misstated via the accrual component of earnings. The

variable % soft assets is included because it can be assumed that companies with more soft assets

have more discretion for management to change assumptions to meet short-term earnings goals.

Change in cash sales is included to evaluate whether sales that are not subject to accruals

management are declining. Change in return on assets is included because managers want to

provide increases in earnings, so this could be a sign for misstatements. Actual issuance is

included to investigate whether companies are concerned with a high stock price. A high stock

price reduces the cost of raising new equity.


- 19 -

The second step is calculating the F-score for each company. When the data for the seven

variables is collected and winsorized, a predicted value for each company has to be calculated.

The second table in the appendix (Table A2) gives information about the regression coefficients

of the variables, which are needed to calculate the predicted value for each company. These

regression coefficients are the same as in model 1 of the paper by Dechow et al. (2011). Then, the

probability has to be calculated for each company and this has to be divided by the unconditional

probability. The outcome of this will be the F-score for that particular company. See the paper by

Dechow et al. (2011) for more detailed information about the calculations.

The last step is to determine which companies will get a value ‘1’ for the dependent

variable in the model of this study, namely DUM_FScore. There are several methods possible.

One method is taking the companies with the highest F-scores, for example the companies with

the highest 20% of F-scores. Another method is taking all companies that have an F-score that is

higher than the median F-score. For this study the first method will be used. The higher the F-

score of the company, the higher the likelihood that the company has to restate its financial

statements of the year 2009 in the near future. So taking the highest 20% of F-scores might be

better than the highest 50%, or the median, since in the second group there are values included

that are just above median, which is not a relatively high F-score. The highest 20% of F-scores

will give a large enough sample to come up with conclusions and the F-scores are relatively high,

which is an indication that the financial statements might be restated. So, there are two groups

based on the F-score now. The first group is the ‘low likelihood restatement’, which consists of

248 companies. The second group is the ‘high likelihood restatement’, which consists of 62

companies.

Model

The model that is used in this study is the following: DUM_FScore = a + b*DUM1 +

c*DUM2 + d*DUM3 + e*FSize + f*FreeC + g*FinRaised + h*No_Quarters_EPSGrowth +

i*LEV + j*%Fem + k*DUM_PoorQualCEO + l*%PoorQualBoard + ε,

where (all observations are for the year 2009),


- 20 -

DUM_FScore = dummy variable with a value of 1 if the companies F-score is in the

highest 20% of F-scores, 0 otherwise;

DUM1 = dummy variable with a value of 1 if the facial width-to-height ratio

of the CEO is in the highest 20% of ratios and the average facial

width-to-height ratio of the board is in the lowest 80% of ratios, 0

otherwise;


of the CEO and the average facial width-to-height ratio of the board

are in the highest 20% of ratios, 0 otherwise;


of the CEO is in the lowest 80% of ratios and the average facial

width-to-height ratio of the board is in the highest 20% of ratios, 0

otherwise;

FSize = log of total equity;

FreeC = demand for external financing, measured as the sum of cash from

operations less average capital expenditures divided by lagged total

assets;

FinRaised = external financing (debt and equity) raised by the company,

deflated by total assets;

No_Quarters_EPSGrowth = number of quarters of continuous earnings per share growth in

2008;

LEV = total debt deflated by total assets;

%Fem = percentage of females in the board;

DUM_PoorQualCEO = dummy variable with a value of 1 if the quality of the photograph

of the CEO is ‘poor’, 0 otherwise;

%PoorQualBoard = percentage of ‘poor’ quality photographs of the directors for each

company.


- 21 -

The dummy variable for the F-scores gives information about whether the company has a

higher likelihood than others to restate the financial statements of the year 2009 in the near

future. This is useful, since the sample consists of companies of which it is known who the CEO

and directors were in 2009. In this way, the facial width-to-height ratio of the CEO and directors

can be associated with whether or not the financial statements, on which the CEO and directors

had influence, have a higher likelihood to be restated. So when the announcement of the

restatement has occurred does not matter. The F-score is an indication for restating the financial

statements, however, there are many reasons why the financial statements might have to be

restated. Only one of them is fraud. This is the only one in which the facial width-to-height ratios

are involved. This is a limitation of this study.

It is expected that CEOs with a higher facial width-to-height ratio will manipulate the

financial statements more often than CEOs with a lower facial width-to-height ratio. But it can

also be that the CEOs with a higher width-to-height ratio are more likely to publicly announce the

manipulation instead of manipulating the financial statements. So, there might be two forces at

play in the background, the ‘bad’ force and the ‘good’ force. However, I expect the first situation

to be more common, since the CEO takes the final decisions. When the CEO takes the final

decisions, he is the last one that can change the financial statements before they become public.

The high facial width-to-height ratio is positively related with aggressive behavior,

untrustworthiness, psychological sense of power (unethical behavior) and financial performance.

Recall that the definition of aggressive behavior is “Any form of behavior directed toward the

goal of harming or injuring another living being who is motivated to avoid such treatment”. Since

aggressive behavior, untrustworthiness and unethical behavior all are ‘bad’ characteristic traits, I

expect the ‘bad’ force, to be more common than the good force. This means that I expect CEOs

with a high facial width-to-height ratio to manipulate earnings more often than that these CEOs

will make the manipulation public. The financial performance, however, might have two forces.

The first one is that companies with good financial performance do not need to manipulate the

financial statements. The second one is that good financial performance is easier to reach by

manipulating the financial statements. So, the good financial performance might be a reason for

not manipulating the financial statements, or it might be a consequence of manipulating the


- 22 -

financial statements. For financial performance it can turn out to support both the good and the

bad force.

The dependent variable, DUM_FScore, is a discrete variable with possible outcomes 0

and 1. Because of this, linear regression cannot be used. If linear regression would be applied, the

expected values for DUM_FScore can take a value smaller than 0 or bigger than 1. This may

result in big residues which are not normally distributed. Therefore, logit regression is used in

this study. In logit regression, DUM_FScore is like a chance. So the value will always be

between 0 and 1.

Three dummies are used for the facial width-to-height ratio for the CEO and the directors,

because this is the easiest way to make a distinction between high facial width-to-height ratios

and low facial width-to-height ratios. The value of the dummies is based on the highest 20% or

lowest 80% of values of the width-to-height ratios, as explained in the ‘sample’ section. Whether

a CEO or a director has a high or a low facial width-to-height ratio is interesting to know, since

males with high ratios are more aggressive, more untrustworthy, have a higher psychological

sense of power, which is associated with more unethical behavior, and have better financial

performance. This, in turn, could be associated with more restatements of the financial

statements. Since the CEO and the average of the board can have high or low facial width-to-

height ratios, there are four situations possible. In the first situation, the CEO and the average of

the board have a low facial width-to-height ratio. In this case the directors may be equal to the

CEO, but the CEO is likely not to manipulate the financial statements. In the second situation,

which is called DUM1, the CEO has a high facial width-to-height ratio and the average of the

board has a low facial width-to-height ratio. In this case the directors may not be equal to the

CEO and the CEO is likely to manipulate the financial statements, which will result in a

restatement of the financial statements when discovered. In this situation the directors have very

little influence. This is the most interesting situation. There is a board needed, that on average is

equal to the CEO when the CEO himself has a high facial width-to-height ratio. In the third

situation, which is called DUM2, both the CEO and the average of the board have a high facial

width-to-height ratio. In this case the directors may be equal to the CEO, and the CEO is likely to

manipulate earnings, which might result in a restatement of the financial statements. In the last


- 23 -

situation, which is called DUM3, the CEO has a low facial width-to-height ratio and the average

of the board has a high facial width-to-height ratio. In this case the directors may be equal to the

CEO, but the CEO is likely not to manipulate the financial statements since the CEO takes the

final decisions and the directors only give advice. So, if DUM1 has a value of 1, this could be a

sign that there is higher likelihood that the company has to restate the financial statements of the

year 2009 in the near future. In this case, there is a board needed with a high facial width-to-

height ratio.

On the other hand, there might also be circumstances in which the board might collude

with the CEO. When this is the case, the CEO and the directors together manipulate the financial

statements, which will result in a restatement of the financial statements when discovered.

However, I think this will not happen very often, because in this situation every single director

has to participate in the manipulation. Otherwise, some directors might make the manipulation

public, or threaten with it, which will reduce the chance that the board will collude with the CEO

when the CEO wants to manipulate the financial statements.

The variables FreeC, FinRaised and LEV come from the paper by Aier et al. (2005). This

paper investigates whether the characteristics of CFOs are associated with accounting errors. Aier

et al. used prior research to come up with some control variables to improve their model. For

example, the paper by Dechow et al. (1996) concluded that the demand for external financing is

an important determinant of earnings management. The paper by Richardson et al. (2002) finds

evidence that leverage is related to earnings management. Since prior research concluded that

these variables are related with earnings management, they are used in this study as control

variables. The papers by Richardson et al. (2002) and DeFond and Jiambalvo (1991) take

earnings growth as a determinant of earnings management. The paper by Aier et al. (2005) uses a

dummy variable that has the value 1 when the company had four quarters of EPS growth prior to

the restatement. Since in the sample of 310 companies there is only one company that would have

a value 1 for this dummy, this dummy is not used in this study. Instead, the variable

No_Quarters_EPSGrowth is included in the model. No_Quarters_EPSGrowth may be a good

control variable, since it also measures the EPS growth of the previous quarters, but it gives more

information since it is not a dummy variable.


- 24 -

The variable %Fem is included in the model to control for the percentage of females in

the board. The idea behind the facial width-to-height ratios only holds for males, so a board with

only males would give the most precise conclusions since then there is no influence of females

who are not taken into account by using the width-to-height ratio. The higher the percentage of

females in the board, the less precise the conclusions will be. The variable DUM_PoorQualCEO

is included in the model to find out whether the quality of the photograph of the CEO influences

the likelihood that the financial statements will be restated in the near future. When the quality of

the photograph is ‘poor’, the conclusions will be less precise than when the quality of the

photograph is good, since the ratios will be measured less precise. The variable %PoorQualBoard

is included in the model to control for the average quality of the photographs of the directors. The

better the quality of the photographs, the better the facial width-to-height ratios will be measured,

the more precise the conclusions will be.

The next step is analyzing the data. For this purpose, the program SPSS is used. Some

descriptive statistics and logit regressions are run and the results will be benchmarked with the

results of the paper by Aier et al. (2005). The results will be discussed in the ‘findings’ section of

this study.


- 25 -

Findings

Table 3 contains the descriptive statistics of the F-score. The mean F-score in Dechow et

al. (2011) is around 1.9 for misstating firms during misstating years. So this F-score and the F-

score in this study cannot be benchmarked with each other, since the F-score in Dechow et al.

(2011) only captures misstating companies while the F-score in this study captures all companies.

Dechow et al. (2011) also report a mean F-score when all other variables are held at their mean

values. The mean F-score in that case is 0.728, which is close to the mean F-score in this study,

which is 0.672. The variables in the paper by Dechow et al. (2011) are winsorized at 1% and

99%. In this study the variables are winsorized at 5% and 95%. The difference is because of the

sample size. The sample size in Dechow et al. (2011) is 2190 companies while in this study it is

310 companies. The median F-score cannot be benchmarked with the paper by Dechow et al.

(2011), because in that paper the median F-score is not reported. The mean and median F-score in

this study are very close to each other, which means that the F-score is normally distributed.

Table 3 Descriptive statistics F-score

Based on the F-score, there are two groups now as explained in the ‘prediction of

restatements’ section. The first group is called ‘high likelihood restatement’ and contains 62

companies. The second group is called ‘low likelihood restatement’ and contains 248 companies.

To be able to compare the two groups, a table is produced with some descriptive statistics of both

groups. Table 4 contains descriptive statistics of the companies in both groups. The footnotes

indicate how the independent variables are computed. The mean of DUM1 is higher for high

likelihood restatement companies than for low likelihood restatement companies. This means that

there are more combinations of high facial width-to-height ratios for CEOs and low facial width-

to-height ratios for the average of the board in the high likelihood restatement group than in the

low likelihood restatement group. This is what is predicted, because this combination of facial

width-to-height ratios is expected to restate the financial statements more often than other

(n = 310)


F-Score 0.14482 1.37048 0.64679 0.67159 0.29272


- 26 -

Table 4 Descriptive Statistics Independent Variables

combinations of facial width-to-height ratios. The mean of DUM2 is higher for low likelihood

restatement companies than for high likelihood restatement companies. In this situation both the

facial width-to-height ratio of the CEO and the average of the board are high. The means of

DUM3 do not differ much. The mean of FSize is a bit higher for high likelihood restatement

companies than for low likelihood restatement companies. This means that the companies that are

more likely to restate their financial statements of the year 2009 in the near future are slightly

bigger than the companies that are less likely to restate their financial statements for the year

High likelihood restatement Low likelihood restatement

(n = 62) (n = 248)

Variable Mean Std. Dev. Mean Std. Dev.

DUM1a

0.21 0.41 0.16 0.37

DUM2b

0.00 0.00 0.04 0.20

DUM3c

0.16 0.37 0.17 0.38

Fsized

7.70523 0.48503 7.59139 0.52803

FreeCe

0.05217 0.03285 0.07596 0.05967

FinRaisedf

0.08049 0.08066 0.09779 0.07922

# of quarters of EPS growthg

1.69 0.74 1.48 0.76

LEVh

0.67675 0.18722 0.59448 0.18781

% Femi

18.37 8.87 17.43 9.38

DUM_PoorQualCEOj

0.32 0.47 0.32 0.47

%PoorQualBoardk

24.02 16.51 28.77 16.41

a. Dummy with value 1 if CEO width-to-height ratio is in 20% highest and average board width-to-height ratio

is in 80% lowest, 0 otherwise

b. Dummy with value 1 if CEO and average board width-to-height ratio are in 20% highest, 0 otherwise

c. Dummy with value 1 if CEO width-to-height ratio is in 80% lowest and average board width-to-height ratio

is in 20% highest, 0 otherwise

d. Log of total equity

e. (Net cash flow from operating activities - capital expenditures) / Total assets

f. (Issuance of long term debt + Issuance of common shares) / Total assets

g. Number of quarters of EPS growth in 2008

h. (Total assets - Total common equity) / Total assets

i. Percentage of females in the board

j. Dummy with value 1 if the quality of the photograph is the CEO is 'poor', 0 otherwise

k. Percentage of 'poor' quality photographs of directors in the board


- 27 -

2009 in the near future. The means of FreeC and FinRaised are higher for the low likelihood

companies than for the high likelihood companies. This means that low likelihood restatement

companies have more money available compared to total assets than high likelihood restatement

companies. The means of number of quarters of EPS growth do not differ much. The mean of

LEV is slightly higher for high likelihood restatement companies than for low likelihood

restatement companies. This means that high likelihood restatement companies have more debt

compared to total assets than low likelihood restatement companies. The mean of percentage of

females in the board is slightly higher for high likelihood restatement companies than for low

likelihood restatement companies. This means that high likelihood restatement companies have

on average more females in the board than low likelihood restatement companies. The means of

the dummy that indicates the quality of the photograph of the CEO are the same for both groups.

The mean of percentage of poor quality photographs of directors in the board is higher for low

likelihood restatement companies than for high likelihood restatement companies. The main

conclusion based on table 4 is that the companies in the high likelihood restatement group are

slightly bigger, have less cash available and have more debt compared to total assets than the

companies in the low likelihood restatement group.

An independent samples test is needed to conclude on whether or not the means of the

independent variables of the two groups in table 4 differ significantly. The results of this test are

reported in table 5. The Levene’s test for equality of variances is used to decide whether the

variances are assumed to be equal or not assumed to be equal. For the independent variables

DUM1, DUM3, FSize, FinRaised, # of quarters of EPS growth, LEV, % Fem,

DUM_PoorQualCEO and %PoorQualBoard the variances are assumed to be equal, since the

values of significance of the Levene’s test for equality of variances are higher than 0.05. For the

independent variables DUM2 and FreeC the variances are assumed not to be equal, since the

values of significance of the Levene’s test for equality of variances are lower than 0.05. This is

used to conclude on the equality of means. The independent variables DUM2, FreeC, LEV and

%PoorQualBoard are significant, which means that the means of these variables are significantly

different between high likelihood restatement companies and low likelihood restatement

companies. For DUM2 this means that the combination of a CEO with a high facial width-to-


- 28 -

Table 5

Independent Samples Test

height ratio and an average board with a high facial width-to-height ratio is more likely not to

restate the financial statements for the year 2009 in the near future than to restate the financial

statements. The variable FreeC is significantly lower for the high likelihood restatement group

Equal variances assumed 3,559 ,060 ,986 308 ,325 ,052 ,053

Equal variances not

assumed

,919 86,641 ,361 ,052 ,057

Equal variances assumed 11,281 ,001 -1,609 308 ,109 -,040 ,025

Equal variances not

assumed

-3,222 247,000 ,001 -,040 ,013

Equal variances assumed ,093 ,760 -,152 308 ,880 -,008 ,053

Equal variances not

assumed

-,153 94,811 ,879 -,008 ,053

Equal variances assumed ,987 ,321 1,542 308 ,124 0,11383 0,07381

Equal variances not

assumed

1,623 100,329 ,108 0,11383 0,07013

Equal variances assumed 25,909 ,000 -3,025 308 ,003 -0,02379 0,00787

Equal variances not

assumed

-4,222 173,911 ,000 -0,02379 0,00564

Equal variances assumed ,283 ,595 -1,533 308 ,126 -0,01730 0,01129

Equal variances not

assumed

-1,516 92,635 ,133 -0,01730 0,01141

Equal variances assumed ,045 ,833 -1,350 308 ,178 -,145 ,108

Equal variances not

assumed

-1,377 96,247 ,172 -,145 ,105

Equal variances assumed ,037 ,847 3,087 308 ,002 0,08226 0,02665

Equal variances not

assumed

3,092 94,081 ,003 0,08226 0,02660

Equal variances assumed ,106 ,745 ,713 308 ,476 0,93810 1,31580

Equal variances not

assumed

,741 98,634 ,460 0,93810 1,26602

Equal variances assumed ,000 1,000 0,000 308 1,000 0,000 ,067

Equal variances not

assumed

0,000 93,443 1,000 0,000 ,067

Equal variances assumed ,009 ,926 -2,035 308 ,043 -4,73903 2,32866

Equal variances not

assumed

-2,026 93,365 ,046 -4,73903 2,33885

% Fem

DUM_Poor

QualCEO

%PoorQual

Board

Mean Difference Std. Error Difference

DUM1

DUM2

DUM3

Levene's Test

for Equality of

Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed)

FSize

FreeC

FinRaised

# of

quarters of

EPS growth

LEV


- 29 -

than for the low likelihood restatement group, which means that companies with a lower FreeC

are more likely to restate their financial statements for the year 2009 in the near future than

companies with a higher FreeC. Companies with a lower FreeC have a lower net cash flow from

operating activities, have invested more or have more total assets. A reason that these companies

are more likely to restate the financial statements might be that they want their net cash flow from

operations to look better, because that might influence decisions of investors. The variable LEV

is significantly higher for high likelihood restatement companies than for low likelihood

restatement companies. This means that companies with a higher LEV are more likely to restate

their financial statements for the year 2009 in the near future than companies with a lower LEV.

A high leverage does not give a positive sign to outsiders, so this might be the reason that

companies with relatively high leverage are more likely to restate the financial statements. The

last significant variable is %PoorQualBoard. This variable is lower for high likelihood

restatement companies than for low likelihood restatement companies, which means that the

percentage of ‘poor’ quality photographs in the board is lower for companies that are likely to

restate their financial statements of the year 2009 in the near future than for companies that are

less likely to restate their financial statements. This means that the facial width-to-height ratios of

directors of high likelihood restatement companies are measured more precise than the facial

width-to-height ratios of directors of low likelihood restatement companies.

The control variables FreeC, FinRaised and LEV can be benchmarked with the same

variables as in the paper by Aier et al. (2005). However, the sample of that paper consists of

companies of all sizes, while the sample of this study consists of the top largest companies.

Furthermore, the variables in the paper by Aier et al. (2005) are not winsorized, while the

variables in this study are winsorized at 5% and 95%. This will influence the mean values of the

variables. These two differences between the paper by Aier et al. (2005) and this study might be a

reasons for differences in the mean values. First, the high likelihood restatement group of this

study is benchmarked with the restatement sample in the paper by Aier et al. (2005). For the

restatement sample in the paper by Aier et al. (2005), the values of these three control variables

are 0.015, 0.189 and 0.304 respectively. This means that average FreeC and LEV are bigger in

the high likelihood restatement group of this study and average FinRaised is smaller in the high

likelihood restatement group of this study than in the restatement sample in the paper by Aier et


- 30 -

al. (2005). The variable FreeC is more than three times as big in this study than in the paper by

Aier et al. (2005). This might be because of a higher net cash flow from operating activities, less

capital expenditures or less total assets. Especially the last two explanations are likely, because

the economic conditions were worse in 2009 than in the period 1997-2002. This might result in

less investing activities and a lower value of assets. The variable FinRaised is smaller in this

study than in the paper by Aier et al. (2005). The most likely explanation for this is that there is

less debt and equity issued by the companies in the high likelihood restatement group in this

study than by the companies in the restatement sample in the paper by Aier et al. (2005). In bad

economic circumstances it is harder to issue debt and equity, so this might be an explanation. The

variable LEV is more than twice as big in this study than in the paper by Aier et al. (2005). This

means that total debt is larger or total assets is smaller than in the paper by Aier et al. (2005).

Both cases are possible, so this might explain the difference between the two results.

The same three control variables can also be benchmarked with the paper by Aier et al.

(2005) for the low likelihood restatement group. For the control sample in the paper by Aier et al.

(2005) the values of the three control variables are 0.020, 0.164 and 0.268 respectively. So, for

the low likelihood restatement group the same holds as for the high likelihood restatement group,

namely, the average FreeC and LEV are bigger in this study and average FinRaised is smaller in

this study than in the paper by Aier et al. (2005).

Before the binary logistic regression for the model will be discussed, the correlation

between the variables is tested. In table A3 in the appendix at the end of this paper the correlation

matrix is published. There are many significant correlations, namely between DUM_FScore and

FreeC, DUM_FScore and LEV, DUM_FScore and %PoorQualBoard, DUM1 and DUM3, DUM1

and No_Quarters_EPSGrowth, DUM1 and DUM_PoorQualCEO,DUM2 and FreeC, DUM2 and

%PoorQualBoard, DUM3 and FSize, DUM3 and %PoorQualBoard, FSize and FreeC, FSize and

FinRaised, FreeC and LEV, No_Quarters_EPSGrowth and LEV, %Fem and %PoorQualBoard.

The values of the correlation coefficients that are significant all are between -0.270 and 0.158.

These values are not extreme correlations, since they are relatively close to 0, especially the

positive correlation coefficient. The correlation coefficient -0.270 is the value of the correlation

between FreeC and LEV. This means that when the FreeC of the companies increases, the


- 31 -

companies have less leverage. The intuition behind this is that companies with a relatively high

FreeC have less debt, compared to total assets, because they have more cash available.

Next, the binary logistic regression for the model will be discussed. The model is:

DUM_FScore = a + b*DUM1 + c*DUM2 + d*DUM3 + e*FSize + f*FreeC + g*FinRaised +

h*No_Quarters_EPSGrowth + i*LEV + j*%Fem + k*DUM_PoorQualCEO +

l*%PoorQualBoard + ε. Table 6 contains the results of the binary logistic regression.

Table 6 Binary logistic regression

Panel A: Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 26,400 11 ,006

Block 26,400 11 ,006

Model 26,400 11 ,006

Panel B: Model Summary

Step -2 Log

likelihood

Cox & Snell R

Square

Nagelkerke R

Square

1 281,142a ,083 ,131

a. Estimation terminated at iteration number 20 because

maximum iterations has been reached. Final solution cannot

be found.

Panel C: Classification Tablea

Observed

Predicted

DUM_Fscore Percentage

Correct 0 1

Step 1 DUM_Fscore

0 241 1 99,6

1 60 2 3,2

Overall Percentage 79,9

a. The cut value is ,500


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Panel D: Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a

DUM1(1) -,379 ,396 ,914 1 ,339 ,685

DUM2(1) 19,232 13098,608 ,000 1 ,999 225194127,175

DUM3(1) -,146 ,426 ,117 1 ,732 ,864

FSize -,022 ,263 ,007 1 ,934 ,979

FreeC -4,751 2,872 2,737 1 ,098 ,009

FinRaised -1,824 1,695 1,157 1 ,282 ,161

NoOfQuartersOfEPSGrowth -,192 ,203 ,895 1 ,344 ,825

LEV 2,082 ,861 5,850 1 ,016 8,020

Fem ,002 ,017 ,011 1 ,915 1,002

DUM_PoorQualCEO(1) -,122 ,328 ,139 1 ,709 ,885

PoorQualBoard -,019 ,010 3,272 1 ,070 ,981

Constant -19,927 13098,609 ,000 1 ,999 ,000

a. Variable(s) entered on step 1: DUM1, DUM2, DUM3, FSize, FreeC, FinRaised, NoOfQuartersOfEPSGrowth, LEV, Fem,

DUM_PoorQualCEO, PoorQualBoard.

From table 6 panel A can be concluded that the model explains at least something, since

the Chi-square value is significant. That means that the model is significantly different from the

model before the parameters are estimated. Panel B contains the model summary. This is based

on the maximum likelihood estimation instead of the ordinary least-squares method, since that

does not work for dummy variables as explained in the ‘research method’ section. Roughly

speaking, the value of the Nagelkerke R Square says something about the variability in the

dependent variable that is explained by the independent variables. In this model about 13.1% of

the variability in the dependent variable can be explained by the independent variables, which is

really low. Panel C contains the classification table. The predictive capacity of the model is

79.9%. Panel D contains the variables in the equation. The variables DUM2, LEV and %Fem are

positive and the variables DUM1, DUM3, FSize, FreeC, FinRaised, No_Quarters_EPSGrowth,

DUM_PoorQualCEO and %PoorQualBoard are negative. A positive value means that when the

independent variable increases, the likelihood of a restatement of the financial statements of the

year 2009 also increases. When LEV, for example, increases, the likelihood of a restatement of

the financial statements of the year 2009 also increases. The same holds for negative values, but

vice versa. The only variable that is significant is LEV, although FreeC and %PoorQualBoard are


- 33 -

almost significant. So one of the conclusions of panel D will be that when the leverage of the

company increases, the likelihood of a restatement of the financial statements of the year 2009

also increases. A possible explanation is that the companies want to give debtors more confidence

in the company by manipulating the financial statements. However, restatements not only occur

because of manipulation of the financial statements. So there might be other reasons that explain

this significant relation. Based on table 6, the two hypotheses can be answered. The first

hypothesis is: facial width-to-height ratios of CEOs are positively associated with restatements of

the financial statements of their companies. DUM1 and DUM2 are the two dummies for which

the CEO has a high facial width-to-height ratio. DUM3 is the dummy for which the CEO has a

low facial width-to-height ratio. Since the three dummies in panel D are not significant, it can be

concluded that the facial width-to-height ratios of CEOs are not associated with restatements of

the financial statements of their companies. The second hypothesis is: high facial width-to-height

ratios of CEOs are positively associated with restatements of the financial statements of their

companies, when facial width-to-height ratios of directors are low. DUM1 is the dummy for

which the CEOs have a high facial width-to-height ratio and the directors have a low facial

width-to-height ratio. Again, this dummy is not significant. This means that the facial width-to-

height ratios of directors are not associated with restatements of the financial statements of their

companies.

The research question of this study is: ‘Are facial width-to-height ratios of CEOs and

directors associated with restatements in the financial statements of their company?’ Since the

results of the three dummies that measure the facial width-to-height ratios are not significant, it

can be concluded that facial width-to-height ratios of CEOs and directors are not associated with

restatements in the financial statements of their company.

To see whether the outliers in the independent variables have an effect on the outcome of

the model, the model will be run again but some independent variables are winsorized. Dummy

variables are used in the model for the variables DUM1, DUM2 and DUM3. The distinction

between a value 1 and a value 0 is based on the values of the facial width-to-height ratios of the

CEO and the average of the board. This is explained in the ‘sample’ section. When the outliers

on the left side and the right side of the data are winsorized, they still will get a value 0 and a


- 34 -

value 1 respectively. So there is no need to winsorize these variables. Also the independent

variables No_Quarters_EPSGrowth, %Fem, DUM_PoorQualCEO and %PoorQualBoard are not

winsorized. This is because the information to calculate these variables are ‘facts’. To calculate

the percentage of females in the board, for example, the number of males and the number of

females in the board is a fact. And whether the quality of the photograph is ‘good’ or ‘bad’ is also

decided. So it is useless to winsorize these variables. The only interesting independent variables

to winsorize are FSize, FreeC, FinRaised and LEV, since there can be significant errors in the

values of the underlying information to calculate these variables. There are 310 companies in the

sample. The lowest 5% and highest 5% will be winsorized. This means that the values of the

independent variables of the lowest and highest sixteen companies, when sorted on each of the

four independent variables, are changed to the values of the 17th

and 294th

company respectively.

Table A4 in the appendix contains the correlation matrix for the winsorized model. Table A5 in

the appendix contains the outcomes of the binary logistic regression of the winsorized model.

The correlation matrix of the winsorized model is almost the same as the correlation

matrix of the unwinsorized model. The same correlations are significant, but for the winsorized

variables there is 1 more correlation significant, namely between FSize and

DUM_PoorQualCEO. The correlation coefficient of this correlation is -0.112. This means that

the correlation is not very strong, since the value is relatively close to 0. Furthermore, the

correlation is negative, which means that the photograph of the CEO is more often of ‘poor’

quality for smaller companies than for bigger companies. The quality of the photograph is useful

for measuring the facial width-to-height ratio carefully. So the facial width-to-height ratio of the

CEO of smaller companies might be less precise than the facial width-to-height ratio of the CEO

of bigger companies. All values of the correlation coefficients that are significant are between

-0.335 and 0.173. These values are not extreme correlations, since they are relatively close to 0,

especially the positive correlation coefficient. The correlation coefficient -0.335 is the value of

the correlation between FreeC and LEV. So the most extreme correlation coefficient that is

significant is the one of the same two independent variables as in the correlation matrix of the

unwinsorized variables.


- 35 -

Table A5 in the appendix contains the outcomes of the binary logistic regression of the

winsorized model. The Chi-square value in panel A is significant, just like for the unwinsorized

model. This means that the model is useful. Panel B contains the model summary. In this

winsorized model about 12.5% of the variability in the dependent variable can be explained by

the independent variables. This is slightly less than for the same model without winsorized

variables. Panel C contains the classification table. The predictive capacity of the model is

80.3%. This is almost the same as in the same model without winsorized variables where it was

79.9%. Panel D contains the variables in the equation. The variables DUM2, FSize, LEV and

%Fem are positive and the variables DUM1, DUM3, FreeC, FinRaised,

No_Quarters_EPSGrowth, DUM_PoorQualCEO and %PoorQualBoard are negative. This is the

same as in the model without winsorized variables, except for FSize. The winsorizing of FSize

changed the coefficient from -0.022 to 0.188. Since the value of the coefficient was very close to

0 before it was winsorized, this result is not surprising. Still, the only variable that is significant is

LEV. %PoorQualBoard and FreeC still are almost significant. They even became slightly more

significant. The winsorizing of the four variables in the model does not change the answer on the

two hypotheses and the research question. DUM1, DUM2 and DUM3 still are not significant,

which means that the facial width-to-height ratios of CEOs and directors are not associated with

restatements of the financial statements of their companies.


- 36 -

Conclusion

For this study, the facial width-to-height ratio of the CEO and directors of 310 companies

are used to find an association between the facial width-to-height ratio and whether or not the

company did restate the financial statements of the year 2009. Since there are only two

companies in the sample that restated their financial statements of the year 2009, a prediction is

made of which companies are more likely to have to restate their financial statements of 2009 in

the near future. To see whether the outliers of the independent variables have a significant

influence on the outcomes of the model, four control variables are winsorized at the end of the

paper.

Since the variables that measure the facial width-to-height ratio of the CEO and the

average of the board are not significant, it can be concluded that there is no association between

the facial width-to-height ratios of CEOs and directors on the one hand and restatements in the

financial statements of their company on the other hand. The only significant variable in the

models is LEV. The coefficient of this model is negative, which means that an increase of the

leverage of the company decreases the likelihood that the company has to restate its financial

statements.

There are several limitations to this study. One limitation is that almost all companies in

the sample have a board with females in it. The facial width-to-height ratio only holds for males,

so companies with only males in the board would have been very useful for this study. However,

this was not possible for this study, since there were less than 10 companies in the sample that

satisfied this condition. Another possible limitation is that the results of this study are based on

only one year. This might not be representative, since in 2009 the worldwide economic

circumstances were worse than in the years before. This might have influenced the financial

variables in the model. Another limitation is that restatements not only occur as a consequence of

fraud. This study, however, could not make a distinction between restatements as a consequence

of fraud and restatements as a consequence of other factors.


- 37 -

Future research could use a bigger sample. This probably would overcome the fact that

there are too few observations for companies that actually did restate their financial statements.

Also, using a bigger sample might give the opportunity to use a sample of companies of which

the boards consist only of males. This should give more precise conclusions. Furthermore, for

future research it would be nice to use data of several years. This might influence the financial

variables.

The only significant variable is leverage. The higher the leverage, the more likely the

company is to restate the financial statements. This might be useful for everyone who makes

decisions based on the financial statements of a particular company. Be cautious when the

leverage of the company is high. This might be a sign that there is a higher likelihood that the

financial statements will be restated.


- 38 -

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Variable Calculation Data itemsRSST accruals (∆WC + ∆NCO + ∆FIN) / average total assets

WC = (current assets - cash and short term investments) - current assets ACT

(current liabilities - debt in current liabilities) cash and short term investments CHE

current liabilities LCT

debt in current liabilities DLC

NCO = (total assets - current assets - investments and advances) - total assets AT

(total liabilities - current liabilities - long term debt) current assets ACT

investments and advances IVAO

total liabilities LT

current liabilities LCT

long term debt DLTT

FIN = (short term investments + long term investments) - short term investments IVST

(long term debt + debt in current liabilities + preferred stock) long term investments IVAO

long term debt DLTT

debt in current liabilities DLC

preferred stock UPSTK

Change in receivables ∆accounts receivable / average total assets accounts receivable RECT

total assets AT

Change in inventory ∆inventory / average total assets inventory INVT

total assets AT

% Soft assets (total assets - PPE - cash and cash equivalents) / total assets total assets AT

PPE PPENT

cash and cash equivalents CHE

Change in cash sales sales - ∆accounts receivable sales SALE

accounts receivable RECT

Change in return on assets (earnings t / average total assets t) - (earnings t-1 / average total assets t-1) earnings IB

total assets AT

Actual issuance value 1 if sale of common and preferred stock > 0 or long term debt issuance >0 sale of common and preferred stock SSTK

value 0 otherwise long term debt issuance DLTIS

Appendices

Table A1 Variables to calculate the F-score


- 42 -

Table A2 Regression coefficients of variables to calculate predicted values as by Dechow et al. (2011)

Variable Coefficient

RSST accruals 0.790

Change in receivables 2.518

Change in inventory 1.191

% Soft assets 1.979

Change in cash sales 0.171

Change in return on assets -0.932

Actual issuance 1.029

Constant -7.893


- 43 -

Table A3 Correlations of variables


- 44 -

Table A4 Correlations of winsorized variables


- 45 -

Table A5 Binary logistic regression for the winsorized model

Panel A: Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 25,571 11 ,008

Block 25,571 11 ,008

Model 25,571 11 ,008

Panel B: Model Summary

Step -2 Log

likelihood

Cox & Snell R

Square

Nagelkerke R

Square

1 284,678a ,079 ,125

a. Estimation terminated at iteration number 20 because

maximum iterations has been reached. Final solution cannot

be found.

Panel C: Classification Tablea

Observed

Predicted

DUM_Fscore Percentage

Correct 0 1

Step 1 DUM_Fscore

0 247 1 99,6

1 60 2 3,2

Overall Percentage 80,3

a. The cut value is ,500


- 46 -

Panel D: Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a

DUM1(1) -,363 ,393 ,853 1 ,356 ,695

DUM2(1) 19,086 12482,351 ,000 1 ,999 194479595,456

DUM3(1) -,196 ,424 ,214 1 ,644 ,822

FSize ,188 ,305 ,380 1 ,538 1,206

FreeC -5,555 3,083 3,247 1 ,072 ,004

FinRaised -2,045 2,100 ,949 1 ,330 ,129

NoOfQuartersOfEPSGrowth -,194 ,204 ,908 1 ,341 ,824

LEV 1,764 ,864 4,170 1 ,041 5,838

Fem ,001 ,017 ,005 1 ,943 1,001

DUM_PoorQualCEO(1) -,071 ,326 ,048 1 ,827 ,931

PoorQualBoard -,019 ,011 3,321 1 ,068 ,981

Constant -21,128 12482,352 ,000 1 ,999 ,000

a. Variable(s) entered on step 1: DUM1, DUM2, DUM3, FSize, FreeC, FinRaised, NoOfQuartersOfEPSGrowth, LEV, Fem,

DUM_PoorQualCEO, PoorQualBoard.

facial width-to-height ratios and restatements

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