




































 

 

 
1 

† Corresponding author 
© 2016 Conscientia Beam. All Rights Reserved. 

 

DOES MANAGERIAL EMOTIONAL BIASES AFFECT DEBT MATURITY 
PREFERENCE? BAYESIAN NETWORK METHOD: EVIDENCE FROM TUNISIA 

 

AZOUZI Mohamed Ali1† --- JARBOUI Anis2 
1Assistant Professor in Finance and Accounting Methods Higher Institute of Business Administration (ISAAS) University of Sfax, 
Tunisia 
2Doctor and HDR financial and accounting Associate Professor of Universities Higher Institute of Business Administration (ISAAS) 
University of Sfax, Tunisia 

 

ABSTRACT 

This study documents that managerial characteristics’ play an important role in determining corporate debt 

maturity. Specifically, we focus on the relationship between the managerial biases and firm debt maturity 

preference. Empirical analysis of the relationship between emotional bias and debt maturity using Bayesian 

Network Method. We distributed a questionnaire among 100 Tunisian managers to measure their 

emotional biases. Our results have revealed that the behavioral analysis of debt maturity preference implies 

leader affected by behavioral biases (optimism, loss aversion, and overconfidence) presence prefer long term 

debt maturity allowing this protect against the takeover operation Russianness.  

Keywords: Emotional biases, Debt maturity, Behavioral corporate finance, Bayesian network, Managerial 

characteristics. 

JEL Classification: G14, G31, G32, D80. 

 

Contribution/ Originality  

The paper pushing organizations managers to choose according to their emotional level 

(applied emotional capacity test up psychometric testing). In addition, it increases the validity of 

inferences from the research. This paper incites governments to establish training programs 

aimed at the development of learning of emotional capacity. 

 

1. INTRODUCTION 

In most cases, the decision is complex because, in addition to the difficulty of choosing an 

alternative from a multitude of alternatives, the decision maker often faces the uncertainty of 

actions whose results are very imperfectly known at the time of choice. Managers must make 

daily choices related to the definition of sectors and business strategy, monitoring market 

investments or equipment, supply management and inventory management of financial risks, 

industrial or environmental, employment, and the launch of new products. 

Financial Risk and Management Reviews 
2016 Vol. 2, No. 1, pp. 1-25 
ISSN(e): 2411-6408 
ISSN(p): 2412-3404 
DOI:  10.18488/journal.89/2016.2.1/89.1.1.25 
© 2016 Conscientia Beam. All Rights Reserved. 

 

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Financial Risk and Management Reviews, 2016, 2(1): 1-25 

 

 
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Moreover, the nature of the objectives pursued by the leaders can be broken between search 

security or durability and a desire for growth and acceptance of risk which cannot remain without 

consequence also their behavior in terms of investment and funding (Julien and Marchesnay, 

1987). 

In recent decades, a significant amount of research in finance, both theoretical and empirical 

focused on the psychology of leadership and its impact on the process of decision making. These 

studies have contributed to the understanding of certain behavioral biases that affect the decisions 

made by leaders namely through loss aversion in the case of financing choices and investment 

(Kahneman et al., 1991; Stulz, 1996; Zhang, 1997; Helliar et al., 2005). 

Many contemporary researches in behavioral corporate finance have stressed the importance 

of the personal equation and objectives of the leaders in explaining corporate finance structure.  

This theories have illuminated how biases like overconfidence and optimism can affect various 

corporate decisions (Bernardo and Welch, 2001; Heaton, 2002; Goel and Thakor, 2008). 

Recent papers there are findings on managerial fixed effects (Bertrand and Schoar, 2003) on 

managerial overconfidence proxies relating to firm behavior (Malmendier and Tate, 2005; 2008) 

and on Chief Executive Officer (CEO) characteristics in private equity firms being related to 

outcome success (Kaplan et al., 2010). 

In this context, research in finance, both theoretical and empirical have focused on the 

psychology of leadership and its impact on the process of decision decisions. These studies have 

contributed to the understanding or even explaining some decisions by behavioral arguments 

(Malmendier and Tate, 2005; Lin et al., 2007; Azouzi and Jarboui, 2012). Malmendier and Tate 

(2005) find that CEOs who are optimistic regarding their firm’s future performance have greater 

sensitivity to investment cash flow leading to distortions in investment. Also, Lin et al. (2007) 

show in more financing constrained firms, optimistic managers exhibit higher investment cash 

flow sensitivity than do non-optimistic managers. 

Bison by these studies, on our article examines the possible influence of three closely related 

emotional biases, which are extensively documented in behavioral research, loss aversion, 

optimism and overconfidence, on a firm’s debt characteristics’ choice. More specifically, it 

examines the links between emotional biases and firm debt maturity preference. 

 

2. HYPOTHESIS DEVELOPMENT 

Research related to CEO financing choices have focused on the debt level of analysis and that 

all debt is homogeneous (Harris and Raviv (1991)). However, as part of a credit report, companies 

are able to set their preferences regarding debt maturity, level and nature. 

It is not yet well understood to what extent firms manage the rollover dates of their bonds by 

spreading out maturities. Fixed cost components of bond issues and secondary market liquidity 

considerations should motivate firms to concentrate their debt in a single or few issues.  



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Static Trade-off Theory ( STT) and Pecking Order Theory (POT) is the body of theory of 

reference that addressed the issue of the firm financial structure choice (Ross, 1977; Jalilvand and 

Harris, 1984; Myers, 1984; Stulz, 1990; Titman and Wessels, 1998; Graham, 2000; Booth et al., 

2001; Azouzi and Jarboui, 2012). These approaches argue that firms tend to choose between 

financial methods based on agency costs (STT) and / or asymmetric information (POT) between 

leader- shareholders and creditors (Myers and Majluf, 1984; Myers, 1984; Fama and French, 

2002; Bushman et al., 2004; Antoniou et al., 2007; Frank and Goyal, 2007; Huang and Ritter, 2009; 

Graham et al., 2013).  

Thus, the choice of debt maturity is explained by the costs of agency debt in the long term, 

by choosing a reported debt in the short term, the share leader in the capital and the duration its 

place the head of his company. However, even non-financial firms frequently have several bonds 

outstanding, with different times to maturity. However, despite the contributions of these 

approaches in the analysis of financial policy corporate several decisions remain misunderstood. In 

this sense, several authors have updated the old idea that emotions have an adaptive role. 

Emotions are necessary for the operation of many of our faculties, such as memory, reasoning, 

decision making or social adjustment. Only recently has a smaller number of analyses emerged 

focusing the leader cognitive biases themselves and trying to understand how they can affect their 

investment and financing decisions (Hawkins et al., 2001; Baker et al., 2004; Hackbarth, 2009; Ho 

and Chang, 2009; Malmendier et al., 2010; Azouzi and Jarboui, 2012). 

We investigate the influence of managerial bias (loss aversion, optimism and overconfidence) 

about corporate debt maturity choice. 

 

2.1. Optimism and Debt Maturity 

CEO optimism and overconfidence have been shown theoretically and empirically to explain 

important corporate decisions, including investment, financing, and dividends (Bernardo and 

Welch, 2001; Heaton, 2002; Goel and Thakor, 2008; Hackbarth, 2009; Ho and Chang, 2009; 

Malmendier et al., 2010; Azouzi and Jarboui, 2012). 

Hackbarth (2009) argue that managers are optimistic more likely to excel in tournaments and 

can be promoted to senior positions. This leader optimistic with its firm growth opportunities 

uses its decisional leverage to improve its reputation on the labor market. He opts for long-term 

debt indicating the convergence of its interests with those of shareholders. This implies a positive 

correlation between CEO optimism level and firm long-term debt choice. 

Menkhoff and Nikiforow (2009) show that the knowledge of behavioral biases changes the 

way the managers perceive the markets, but not the way they see themselves. Optimistic leader is 

aware that his company is undervalued by the market. It therefore seeks to limit its reliance on 

external financing modes whose debt. However, if insufisance internal financing mode it chose not 

risky debt (short-term) first then risky debt (long-term debt), and finally the capital increase 



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(Heaton, 2002; Malmendier and Tate, 2005). This implies that the optimistic manager seeks to 

reduce the debt maturity. 

Malmendier et al. (2010) also show that leaders optimistic are able to take risky decision, and 

find that overconfident CEOs take on more debt. This optimistic leader underestimates its firm 

total risk. It tends to preferred of long-term debt positively correlated with firm value increase. 

The choice of debt maturity level improves mutual trust between manager and shareholders. The 

establishment of a trust environment increase CEO freedom rate and enhances its reputation on 

the labor market. This implies the presence of a positive correlation between firm maturity debt 

and leader optimism level. 

Hirshleifer (1993) shows that managers invest in short-term projects to generate a positive 

cash flow, increase their income and their managerial reputation on the labor market. This leader 

opts for choice related to the duration of his Monday. Optimistic leader kept his place at the head 

of his company opts for long-term financial policy which long term debts choice. This implies a 

positive correlation between optimism and debt maturity. 

Azouzi and Jarboui (2012) find that the find that the analysis of managerial decisions 

(financing and investment) within the behavioral approach is consistent with the financial 

Organizational theory. In other word, the optimistic leader uses his decisional power to converge 

with the interest of shareholders and ensure its place at the head of the management team. This 

prediction in Tunisian context implies that optimistic leader adjusted its firm maturity according 

to their confidence kept its place at the head of his company. 

H1: Optimistic leader accepts level of maturity debt greater than rational leader 

 

2.2. Loss Aversion and Debt Maturity 

Psychological studies document that loss aversion causes people to overestimate risk, be 

more uncertain about forecasts and opt for making it safer to limit the likelihood of his removal 

(Bertrand and Mullainathan, 2003; Baker et al., 2007; Azouzi and Jarboui, 2012).  

Adams et al. (2005) also show that in firms where CEOs are powerful and dominate most 

major decisions, the risk arising from judgment errors is not well-diversified, resulting in more 

extreme decisions and higher variance of firm performance. This shower head to risk of loss of 

earnings or reputation seeks to limit the potential loss by choosing low-risk decisions including 

short-term debt choice. This implies negative correlation between CEO loss aversion level and 

firm debt maturity choice. 

Baker et al. (2007) shows that loss aversion causes CEO to overestimate its firm total risk. 

This overestimation of the risk of the business leader impulse decision to choose less risky. He 

prefers the short-term debt low risk compared to long term. This implies a positive correlation 

between CEO loss aversion level and firm short-term debt level. 

Nosic and Weber (2008) have shown the importance of perception yields and risk officer in 

explaining these choices. Indeed a leader who undervalued the capacity of its business to generate 



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future revenues is encouraged to undertake choices consistent with shareholder interest. He opted 

for policies and strategies converge with the interests of major shareholders with long term debt. 

This implies a positive correlation between CEO loss aversion level and firm short-term debt 

level. 

Chang et al. (2009) argue that the stock price variation affects the firm capital structure 

composition.  This explains the limited use of the leaders (loss aversion) in equity financing. He 

opts for long term risky debt reporting the performance of its business (able to comply with the 

commitments of long-term debt). This choice of risky financing improves the evaluation of its 

business in the market and increases its reputation on the labor market. 

H2: Loss aversion leader accepts level of maturity debt greater than rational leader 

 

2.3. Overconfidence and Debt Maturity 

The human tendency to be overconfident has been widely documented in psychology and has 

become a central feature in economics and behavioral finance (Heaton, 2002; Malmendier and 

Tate, 2005; Ben-David et al., 2007; Hackbarth, 2009; Landier and Thesmar, 2009; Malmendier et 

al., 2010; Azouzi and Jarboui, 2012). Many facets of overconfidence have been examined through 

the tendency to overestimate our own knowledge (in particular in miscalibration studies), our 

abilities compared to others (better-than-average effect), or the degree to which we control future 

events. 

Landier and Thesmar (2009) explored the impacts of overconfidence and/or optimistic 

entrepreneur on financial contracting and corporate performance and found that optimistic 

entrepreneur tend to make decisions under-reacting the negative information. So, CEO 

overconfidence debt maturity choice is a reaction to undervaluation. It uses long-term debt 

reported to the performance of its business and corrected firm market valuation. 

Malmendier et al. (2010) argue that overconfident managers perceive their firms to be 

undervalued. To correct this undervaluation leader opts decisions by indicating the performance 

of its business, the choice of long term debt. This choice of risky financing improves the 

evaluation of its business in the market and increases its reputation on the labor market. This 

implies positive correlation between CEO overconfidence level and firm debt maturity choice. 

Goel and Thakor (2008) find that excessively overconfident CEOs invest less in information 

reception and jeopardize the stakeholders. This implies that the CEO overconfident of their 

personal abilities opts for diverging choice with shareholders’ interests. The diminution of his loss 

aversion level he impulse to choose risky decisions and negatively correlated with creation value 

objective (expensive), the choice of risky debt in the long term. This implies positive correlation 

between CEO overconfidence level and firm debt maturity choice. 

Ben-David et al. (2010) suggest that overconfident managers will either tend to 

underestimate the volatility of their firms' future cash flows or overweight their private signals 

relative to public information. This firm risk probability underestimation impulse CEO 



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overconfident to undertake risky policy, the choice of high debt maturity. This implies positive 

correlation between CEO overconfidence level and firm debt maturity choice. 

H3: overconfidence leader accepts level of maturity debt greater than rational leader. 

 

3. RESEARCH METHOD 

3.1. Data 

To note, the empirical tests are based on 100 non-financial Tunisian firms during the 2010 

fiscal year (28 are listed companies and 82 are non-listed companies, see Table 1). All financial 

firms (including banks) outing to the fact that this business sector is regulated and likely to have 

fundamentally different cash flows and characteristics. Firms with insufficient data regarding 

about emotional characteristics and the board of director’s composition are also excluded. The 

board’s compositions, as well as financial characteristics data, are gathered from the BVMT 

annual report. 

Emotional and psychological characteristics are collected by means of an administered 

questionnaire. Actually, the selected choice deals with some homogeneous individuals 

representing some Tunisian CEO representatives of 100 firms (60 males, 35 females, 5 

unreported), ranging in age from 25 to 58 (Table 2).  

 

Table-1.Visited Companies 

Initial BVMT sample for 2010 50 

Financial firms (22) 

Other non financial  firms 120 

Insufficient data to emotional intelligence (40) 

Insufficient data to board of directors compositions (8) 

Final sample 100 

    Source:  BVMT 

 

Most questionnaires have been distributed by the method of door to door to ensure they are 

personally delivered to the person concerned; few among them have been mailed, for businesses 

located outside the Greater Tunis area. 

It is worth noting, however, a broader sample that even if it had been envisaged to be studied 

and that more than 100 questionnaires had been distributed for this purpose, we would have 

received far fewer responses than expected (return rate 44.84 per cent: although the number of 

distributed questionnaires reached 223, the responses received did not exceeded 100 CEO). 

 

 

 

 

 



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Table-2.CEOs’ characteristics 

 n  Percentages 

Age 
25-30 years 
31-40 years 
40-49 years 
Over 50 years 

 
5 
20 
35 
40 

 
5% 
20% 
35% 
40% 

Gender/sex 
Males 
females 
Unreported 

 
60 
35 
5 

 
60% 
35% 
5% 

Degree  
Baccalaureate 
Bac + 2 
Bac + 4 
DAS/HDSS 

 
15 
20 
30 
35 

 
15% 
20% 
30% 
35% 

 

Indeed, many of the adduced have refused to respond to our questions on the ground of 

several reasons, namely, that: 

 They are too busy and have no time to devote to research; 

 they generally do not pay any interest to the questionnaires submitted by students and 

would return them to their assistants or other staff for a response (this has been the case 

of our officer-centered research); and 

 They perceive that the questionnaire is a sort of ‘‘control’’ damage to their private lives 

and that it is out of the question to answer. 

Other encountered difficulties are mainly due to the administrative procedures and 

hierarchical procedures which linger questionnaires to the recoveries. Fortunately, the leaders 

who had been so kind as to cooperate and help us formulate and set up our sample eventually 

composed of 100 private company leaders, belonging mostly to the industrial sector. 

 

3.2. Variables’ Measurement 

The objective of this section is to determine the variables’ measurement. 

 

3.2.1. Debt Maturity 

The deadline is the measure most famous of the life of the debt that is the time between the 

date of possession of the borrowing and repayment. Debts are classified according to the three 

following periods: one year at most, one year and five years and more than five years. For our 

part, we consider bank debt long-term (or more logically medium and long term), any debt 

granted to a credit institution having a period of payment over a year. We propose to use in our 

study the ratio of long-term debt term total debt as a measure of the dependent variable. The 

amount of debt reported long-term to total debt and total assets not to separate the decision of 

the debt maturity and the decision of leverage. 



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DMAT= Long Term Debet/Total Debet 

This measured is used by Scherr and Hulbert (2001); Barclay and Smith (1995) and Gueney 

and Paudyal (2003) 

 

 3.2.2. Emotional Bias 

The questionnaire focuses on evaluating and scoring of the three emotional biases (risk 

aversion, optimism and overconfidence). The questions have been inspired from the 

questionnaires formulated by the Fern Hill and Industrial Alliance companies (Table 3).  

The emotional bias takes 2 follows:  

• 1 if the individual has a high level for each bias.   

• 0 if not. 

 

3.2.3. Investment Decision 

The purpose of this article is to show the impact of emotions on the firms’ investment 

decision 

(Investment nature, level and horizon). The appropriate measures in the literature to evaluate 

investment decision are: 

 

 3.2.3.1. Assets Specificity 

In our study, we will use the degree of assets intangibility as a proxy of the specific 

investments. The degree of assets intangibility can be appreciated on many levels. The France 

Bank and Ministry of 

Industry in studies devoted to the development of intangible investments in France has used 

the ratio often intangible / tangible assets. In Tunisia, as in France, the intangible asset 

accounting record comes from the capitalization of such expenses. However, the unavailability of 

information legitimizes the use of the amount of intangible assets is presented in the balance sheet 

although this amount is usually surrounded by doubt as the result of discretionary choices 

performed by the leaders. Akin to the French context, the measurement of intangible capital in 

the Tunisian context has the same problems, which leads us to adopt accounting. Based on that 

discussion and the availability of data of Tunisian companies we offer the following indicator of 

the degree of activation of intangible expenses: 

Asset Specificity Rate (ASR) = Intangible Assets / Asset Accounting. 

This measured is used by Cazavan-Jeny (2004); Moussu and Thibierge (1997); Thibierge 

(2001) etc. 

 

 

 

 



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Table-3. Items used in the emotional biases scale (14 items) 

Items Factor 1: 
loss 
aversion 
50.710 % of 
total 
variance 

Factor 2: 
optimism 
29.450 % of 
total 
variance 

Factor 3: 
overconfiden
ce 
10.275 % of 
total 
variance 

Factor 4: 
cognitive 
flexibility 
5.385 
% of total 
variance 

1. What is your propensity to take 
financial risks with respect to 
others? 

0.802    

2. With a great financial decision, 
what do you care about more: 
possible losses or possible gains? 

0.742    

3. Insurance can protect us against 
a wide variety of risks: theft, fire, 
accidents, illness and death... How 
many insurance subscriptions have 
you subscribed to? 

0.713    

4. When you think of the word 
‘‘risk’’ in a financial context, what 
term in the following list first 
comes to mind? 

0.686    

5. When I’m faced with a 
challenge, I give up because I’m 
afraid of failure. 

0.600    

6. What emotional effect do 
important decisions have on you 
once they are taken? 

 0.857   

7. I am motivated by imagining 
the successful decisions positive 
results of entrepreneurial tasks 

 0.851   

8. Do you consider that degree of 
uncertainty is the business 
environment is 

 0.842   

9. I know how to most control my 
emotions. 

  0.774  

10. For how long do you reckon to 
keep your position in your firm? 

  0.715  

11. How confident are you in your 
ability to take good financial 
decisions? 

  0.641  

12. How easily do you adapt 
yourself to deterioration of your 
financial situation? 

   0.862 

13. Your reaction regarding 
changes in your firm environment 
is: 

   0.862 

14. In a job search would you 
rather seek: 

   0.789 

 

 

 



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3.2.3.2. Investment Level 

In this study, we will use the presence of free cash flow and growth opportunities as two 

indicators of over-investment (low Future investment opportunities and free cash flow) or 

underinvestment (low free cash flow and Future investment opportunities). The literature differs 

on how to measure the free cash flow as conceptualized by Jensen (1986). In general, however, it 

is defined as operating income before depreciation interest expense and taxes, as well as dividends 

paid 

(Lehn and Poulsen, 1989; Gul and Tsui, 1998; Jaggi and Gul, 1999) divided by book value of 

total assets to account for effects related to size (Lang et al., 1991). 

Free Cash Flow Rate (FCFR) = Operating Profit / Total Assets. 

Future investment opportunities are measured by Tobin's Q (Skinner, 1993). Tobin's Q is 

defined as the ratio of market value of a firm to the replacement value of its assets (Griliches, 

1981; Lindenberg and Ross, 1981; Cockburn and Griliches, 1988; Megna and Klock, 1993; 

Skinner, 1993). A Tobin's Q greater than one then the company has signed a profitable 

investment opportunities and vice versa. In our study, we will retain an approximation of Tobin's 

Q, calculated as follows (Chung and Pruitt, 1994): 

it it

it

it

MVS  D  
Q

A




 

MVS – market value of common and preferred shares; D – book value of debt, defined as 

current liabilities plus long-term debt plus inventories minus current assets; A – total assets. 

 

 3.2.3.3. Investment Horizon 

Referring to the theory of agency leaders has an obligation of result on short horizons. Their 

wealth is tied to the performance of the firm during the duration of their mission is the period 

during which they run the firm. These leaders prefer investment projects in the short term to 

quickly reveal the performance of these investments and reduce uncertainty about their own value 

on the labor market. 

In our study we will use the rate of investment operations (industrial and commercial assets) 

as an indicator of the investment horizon. 

Capital Expenditure Rate (CER) = operating assets / Total assets 

This measured is used by Cliche (2000); Gervais et al. (2002); Malmendier and Tate (2005); 

Chang et al. (2009); Draief (2010) etc. 

The investment decision takes 9 follows: 

• 1 if the manager chooses investment specific: positive variation in the rate of assets specificity. 

• 2 if the manager chooses overinvestment: low future investment opportunities and free cash flow 

• 3 if the manager chooses underinvestment: low free cash flow and future investment 

opportunities. 



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• 4 if the manager chooses long-term investment: negative variation in the rate of capital 

expenditure 

• 5 if the manager chooses short-term investments: positive variation in the rate of capital 

expenditure. 

• 6 if the manager chooses (overinvestment+ long-term investment): negative variation in the 

rate of capital expenditure, low future investment opportunities and free cash flow. 

• 7 if the manager chooses (underinvestment+ short-term investments): positive variation in the 

rate of capital expenditure, low free cash flow and future investment opportunities. 

• 8 if the manager chooses (specific investment+ overinvestment + long-term investment): 

positive variation in the rate of assets specificity, negative variation in the rate of capital 

expenditure, low future investment opportunities and free cash flow. 

• 9 if the manager chooses specific investment+ underinvestment + long-term investment): 

positive variation in the rate of assets specificity, low free cash flow, future investment 

opportunities and negative variation in the rate of capital expenditure. 

 

 3.2.4. Dividend Policy  

The variable used to measure dividends level  is  the distribution rate (Rozeff, 1982; Agrawal 

and Jayaraman, 1994). The advantage of the distribution rate is the information that is in terms of 

retention of earnings and, therefore, whether the flow (the retention rate is equal to 

100 in the payout ratio). 

Payout ratio = Dividend per share / earnings per share 

 

3.2.5. Control Variables   

Static trade-off theory (STT) and pecking order theory (POT) is the body of theory of 

reference that addressed the issue of the financial structure of the firm. The factors that explain 

the financial structure are mainly at the cost, size, level of risk, growth opportunities, the 

structure of assets and business (Rajan and Zingales, 1995; Booth et al., 2001; Dufour and Molay, 

2010).  

We include in our model three control variables that explain the effectiveness of choice of 

financial structure of the company. These variables are proxies for profitability, firm size and 

growth opportunities.   

 

3.2.5.1. Profitability  

More profitable firms have, ceteris paribus, more internally generated resources to fund new 

investments. If their managers follow a pecking order, they will be less likely to seek external 

financing (Fama and French, 2002). Thus, on average, these firms’ leverage ratios will be lower. 

In trade-off models, on the other hand, this relationship is inverted. More profitable firms are less 

subject to bankruptcy risks, ceteris paribus. Hence, their expected bankruptcy costs are reduced 



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and they can make more use of the tax shields provided by debt, thus choosing a position of 

greater leverage. We will keep the ratio of return on assets ROA to measure this variable:  

ROA= Earnings before interest, tax, depreciation divided by total assets, lagged one year 

period. 

 

3.2.5.2. Firm Size  

Studies suggest that the probability of bankruptcy is lower in larger firms and that, therefore, 

their debt capacity is higher than that of smaller ones, all else equal. On the other hand, fixed 

transaction costs can make new stock issues unattractive to small corporations, stimulating them 

to issue debt (Rajan and Zingales, 1995; Hovakimian et al., 2004; Dufour and Molay, 2010). 

Indeed, most studies have applied total assets or turnover as a measure for firm size (Bujadi and 

Richardson, 1997). In this paper, it is measured through the log of the firm’s total assets 

(LNSIZE).  

MVS – market value of common and preferred shares; D – book value of debt, defined as current  

liabilities plus long-term debt plus inventories minus current assets; A – total assets.  

 

3.2.5.3. Board of Directors  

To note, theories regarding the board of directors, along with prior empirical researches and 

various recommendations have suggested that some board characteristics have an influence on 

the quality of the financial report and on firms’ performance. Board characteristics are examined 

here:, independence (Fama and Jensen, 1983). 

The board’s independence 

The different  characteristics pertaining to the board’s independence  are  measured by  the 

following variable: BIND is  defined as the  percentage of  the  board members  who are 

simultaneously independent  and  non-executives which is equal to the number of outside 

directors  divided by the total board members (Forker, 1992; Wright, 1996; Haniffa and Cooke, 

2000; Chtourou et al., 2001). 

BIND = number of outside directors /total board members. 

Table 4 presents the characteristics of boards of directors of the 100 Tunisian companies 

included in our study. Tunisian companies are run by independent boards, medium (seven 

directors) and not dominated by CEOs. 

 

Table-4. Board of Directors’ Characteristics 

Variables Mean Std Min Max N 

Entire Board 7.60 2.56 4 12 100 

Outside Directors 2.62 1.11 1 4 100 

Affiliated Directors 1.98 0.80 1 3 100 

Inside Directors 3.360 1.34 1 5 100 

CEO Duality  0.26 0.44 0 1 100 



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For simplification purposes, the summary of each variable extent range in the model, its name as 

well as its expected impact on the firm assets specificity choice are depicted in Table 3. 

 

Table-5. Operational definitions of variables 

Class : Phenomena : Mesure : Variables : Predictions : 

Endogens variables  : 
Debet maturity Debt deadline DMAT= Long 

Term Debet/Total 
Debet 

DM 

Exogenous variables : 
 Short 

Ter
m 

Medium  Long-
Term 

Optimism 
 
 

Directors 
overestimate 

capacity of their  
firms 

The questionnaire 
obtained score 

OP - + + 

Lost aversion Loss rumination 
and reputation 

The questionnaire 
obtained score 

LA + + - 

overconfidence Directors 
overestimate their  

personal 
competences 

The questionnaire 
obtained score 

OVER + + + 

Investment 
decision 

Assets specificity 
 

Asset Specificity 
Rate (ASR) 

= intangible 
assets / asset accoun

ting. 

AS - + + 

Investment level Free Cash Flow 
Rate (FCFR) 
= Operating profit / 

total assets. 
and 

it it

it

it

MVS  D  
Q

A




 

INL - + + 

Investment horizon 
 

Capital Expenditure 
Rate 

(CER) = operating a
ssets / Total assets 

INH + + + 

Dividend Policy The presence of a 
dividend policy 

Payout 
ratio = Dividend per 
share / earnings per 

share 
If the payout 

ratio <0 
or> 0: yes: presence 
of a policy 

of dividend 
distribution. 

If the payout 
ratio = 0: no, absenc
e of a 

policy of dividend 
distribution 

DV + - - 

Controls variables: 



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Profitability 
 

Reports on 
the company's 

ability to meet its 
commitments 

ROA= Earnings 
before interest, tax, 
depreciation divided 
by total assets, 
lagged one year 

period 

PF - + + 

Firm size Firms signaled 
performance 

Ln (total assets) LNSIZE + + + 

Board Of 
Directors 

The presence of 
independent 

members in the 
board 

Number of outside 
directors /total 

board 
Members. 

BIND 
 

+ 
 

- 
 

- 
 

 

3.3. Bayesian Network Method 

The definition of a Bayesian network can be found in many versions, but the basic form 

(Pearl, 1986) is stated as follows: a Bayesian network is a directed probability graph, connecting 

the relative variables with arcs, and this kind of connection expresses the conditional dependence 

between the variables. The formal definition follows.  

A Bayesian network is defined as the set of (D, S, and P), where:  

(1) D is a set of variables (or nodes): in our case it consists of investment cash flow sensitivity, 

optimism, loss aversion, overconfidence, profitability, firm size and future investment 

opportunities.  

(2) S is a set of conditional probability distributions (CPD). S = {p (D /Parents (D) / D ∈ D), 

Parents (D) ⊂ D stands for all the parent nodes for D, p (D/Parents (D) is the conditional 

distribution of variable D. 

(3) P is a set of marginal probability distributions. P = {p (D) / D ∈ D} stands for the 

probability distribution of variable D.  

In the Bayesian network, variables are used to express the events or objects. The problem 

could be modeled with the behavior of these variables. In general, we first calculate (or determine 

from expert experience) the probability distribution of each variable and the conditional 

probability distribution between them. Then from these distributions we can obtain the joint 

distributions of these variables.  

Finally, some deductions can be developed for some variables of interest using some other 

known variables. 

 

3.3.1. Define Network Variables and Values 

The first step in building a Bayesian network expert is to list the variables recursively, 

starting from the target variable to the causes. In this order we present the variables in the table 

below: 

 

 

 



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Table-6. The network variables and their values 

 Variables Type 

Debt maturity Discret [1 ; 2 ; 3] 

Investment decision Discret [1 ; 2 ;3 ;4 ;5 ;6 ;7 ;8 ;9] 

Dividend Policy Discret : YES/NO 

Optimism Discret : YES/NO 

Loss aversion Discret : YES/NO 

overconfidence Discret : YES/NO 

Profitability 
 

Discret : YES/NO 

Firm size Discret [1 ; 2 ; 3] 

Future investment opportunities Discret : YES/NO 

Board  Independence Discret : YES/NO 

 

3.3.2. Graphical Model 

The second step of Bayesian network the construction is to express the relationships between 

variables. The BayesiaLab learning of Bayesian network by taking the database as a discrete entry 

process without sampling data. The Bayesian network constructed is the result for the total 

database. According to the data that we have received through the questionnaire, we have 

established relationships following graph (Fig. 1). 

 

 
Fig-1. Firm Debt Maturity Choice: Bayesian Network 

 

The graphical model if it (Figure 1) explains the debt maturity choice of Tunisian firms. This 

decision is affected by the CEO emotional bias (optimism, loss aversion, and overconfidence). 

These emotional biases originate the firms’ financial position (capital structure choice, dividend 

policy, size, profitability and board independence). 

In what follows, we describe in detail the various correlations between these variables and 

their effect on the target variable (debt maturity: DM). 

 



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4. EMPIRICAL RESULTS  

4.1. The Relationships Discovered Analysis 

The relationships between the variables in the database are directed at the parent node child 

node. Each relationship is composed of three different measures: the Kullback-Leibler, the relative 

weight and the Pearson correlation (direction of relation). Indeed, the Kullback-Leibler and the 

relative weight are two measures indicating the strength of relationships and the level correlation 

between variables, in that while the correlation measure of personal meaning and relationship 

significance. The relative weight scale of 0 to 1. Thus, the table (Table 5) below shows the 

relationships analysis results between variables across the network Pearson correlation. Table 7 

examines the relationship (independence and correlation) between networks variables. 

 

Table-7. The relationships analysis 

Note: a. Kullback-Leibler close to 1: important correlation between the variables 

b. Relative weight close to 1: important correlation between the variables. 

c. Pearson correlation:*, **, ***, respectively at 10%, 5%, 1%. 

 

Parents  
Nodes 

Childs  
Nodes 

Kullback-Leibler 
Divergence 

Relative 
Weight 

Pearson  
Correlation 

CSC DI 0,9159 1,0000 0,1911* 

BIND OP 0,8846 0,9659 0,9724 

OP CSC 0,6031 0,6585 -0,0395** 

FSIZE DM 0,5750 0,6278 0,5422 

OVER DI 0,4591 0,5012 0,1448* 

LA DI 0,4122 0,4500 -0,0117*** 

DI DM 0,3374 0,3684 -0,0694** 

LA CSC 0,3240 0,3538 -0,0612** 

CSC DM 0,2999 0,3274 -0,0636** 

DI DV 0,2939 0,3209 -0,0857* 

OP DV 0,2703 0,2951 0,5527 

OP DI 0,2484 0,2713 -0,1453* 

OVER CSC 0,1946 0,2124 0,2642 

LA DV 0,1727 0,1886 -0,0555** 

FSIZE LA 0,1416 0,1546 -0,3055 

OVER DV 0,1006 0,1098 -0,1486 

DV DM 0,0936 0,1022 0,0956* 

PF LA 0,0745 0,0813 -0,1532* 

BIND LA 0,0708 0,0773 -0,0117*** 

FSIZE OP 0,0491 0,0536 -0,0694** 

PF OVER 0,0352 0,0384 -0,0612** 

FSIZE OVER 0,0256 0,0280 -0,0636** 

PF OP 0,0149 0,0163 -0,0857* 

BIND OVER 0,0032 0,0034 0,5527 

OP OVER 0,0000 0,0000 -0,1453* 

LA OP 0,0000 0,0000 0,2642 



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Table 7 examines the relationship (strength and correlation type) between networks 

variables. 

The analysis of the relations shows the presence of a strong relationship (Kullback- Leibler=0, 

9159/ relative weight =1) and positive (β = 0, 1911) between capital structure choice and firm 

investment decision. This confirms the predictions of organizational financial theory 

independence between the two decisions. The results also show the presence of a strong and non-

significant relationship between board of director’s independence and investment decision 

(Kullback-Leibler=0, 8846/relative weight = 0, 9659/β=0, 9724). 

In addition, the firm capital structure choice is negatively correlated with his level of 

optimism (β=-0, 0395) and loss aversion (β=-0, 0612), positively correlated with CEO 

overconfidence rate (β= 0, 2642). The investment decision is positively correlated with the CEO 

overconfidence level (β=0, 1448) and negatively correlated with the loss aversion level β= (-0, 

0117) and optimism (β=-0, 1453). Behavioral investment level analysis has enriched the 

predictions of theories based on asymmetric information (signals theory and rooting theory) and 

agency theory in choice of underinvestment or overinvestment.   Finally, firm dividend policy is 

positively correlated with CEO optimism level (β=0, 5527) and negatively correlated with the 

loss aversion level (β= -0, 0555) and overconfidence (β=-0, 1486). CEO overconfidence/optimistic 

positively affects firm dividend policy. A confident leader agrees on the establishment of a 

generous dividend policy. It overestimates the future results of its business and its ability to meet 

its commitments on dividends distribution 

Network debt maturity analysis show the presence of strong relationship (Kullback- Leibler 

0, 3374=/relative weight= 0, 3684) and negative (β=-0, 0694) between investment decisions and 

firm debt maturity choice. Also, there is a strong relationship (Kullback- Leibler=0, 0936/0, 

1022=relative weight) and positive (β=0, 0956) between firm debt maturity and dividend 

payments. This confirms the predictions of financial theory for organizational independence 

between financing decisions (dividend payment and choice of financing method) and investment. 

Thus, overconfidence leads the manager to underestimate the company bankruptcy probability 

and, therefore, a higher debt. This financial decision (external preference method) impulses leader 

to follow a generous policy dividends distributing to offset the losses associated with its choice.  

The relationship analysis find absence of relation between debt maturity choice and 

managerial biases (optimism, overconfidence and loss aversion). This result is explained by the 

effect of board independency to moderate CEO emotional bias presence: result finds presence of 

negative correlation between firm board of director’s independence and CEO loss aversion (β= -0, 

0117). This confirmed contractual governance theory prediction: the presence of external 

improves control exercised by the board and the executive disciplined. Thus, CEO overconfidence 

overestimates his skills to reduce risk. This led him to choose high projects risk which is in the 

interest of shareholders and increases firm’s value.  To finance its investment choices, this 



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overconfidence leader considers his company undervalued by the market limit its emissions 

securities risky. 

Firm capital structure nature affect negatively debt maturity choice (Kullback- Leibler 0, 

3374=/relative weight= 0, 3684/ β= -0, 0636). This result confirmed organizational financial 

theory and shows that the CEO debt nature choice is related to the company's ability to generate 

internally cash flows. CEO recognizes firms’ operational risk level and loss aversion seeks to 

reduce its firm’s total risk by using low of external funding including debt. CEO of high 

operational firms risk tries to control the total risk by limiting the financial risk introduced by 

debt and the issuance of new shares. He prefers to finance its investment projects through 

internal funds. 

The relation analysis test shows that firm size affects their debt maturity choice (Kullback- 

Leibler 0, 5750=/relative weight= 0, 6278/ β= 0, 5422). In addition, firm size is negatively 

correlated with CEO optimism level (β= -0, 0694), loss aversion rate (β= -0, 0555) and 

overconfidence (β= -0, 0636). 

Finally, the results also show the presence of a negative correlation between managerial 

overconfidence and his optimism level (β=-0, 1453). 

 

4.2. Target Variable Analysis: Debt Maturity (Dm) 

To analyze the CEO assets specificity choice, we must choose the variable Debt maturity as 

a target variable in the Bayesian network. Then we can use the function that generates the 

analysis report of the target firm debt maturity level. In this report, the relationship between debt 

maturity level and the other variables are measured by binary mutual information and the binary 

relative importance. The mutual information of two random variables is a score measuring 

the statistical dependence of these variables. It is measured in bits. 

The result find that CEO not loss aversion at 50, 8906%, optimism at 58, 3082%, 

overconfident at 60,785%, preferred equity at 24, 5584%, choice dividend distribution at 

72,4981%, opted for overinvestment position at 14,4476%, operated in firm big size at 86,7491%, 

58,9050% of independence board presence. This result implies that CEO behavioral 

characteristics’ affect his decision. Firm long term Debt choice is justified by leader emotional 

profile (optimism, loss aversion and overconfidence). This leader optimistic with its firm future 

investment opportunities increases debt maturity level to limit the likelihood of its replacement 

(loss aversion). Loss aversion leaders seek to avoid the worst-case scenarios.  They not only use 

the tools of risk management to reduce the variance of cash flows but  rather to avoid the worst 

scenarios that influence the risk of bankruptcy or preventing the  company to take advantage of 

profitable investment. Thus, a downpour in the loss leader seeks the minimization of the 

probability of loss for him and a firm. He refuses to debt financing (to avoid the risk of 

bankruptcy) and prefer self-financing 



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CEO debt maturity average ratios of the order of 26, 4416% is explained by its optimist level 

at 56, 2120%, its loss aversion at 78,7610%, its overconfidence at 64,4348%, its preference for 

AS+LT+UNDIN at 16,0929%, equity preference at 28,4494%,dividend distribution at 64,5319%, 

firm directors board independence at 57,5172%,firm low profitability at 55,5406% and average 

size at 71,5105%.  Optimistic leader is aware that his company is undervalued by the market. It 

therefore seeks to limit its reliance on external financing modes whose debt. However, if 

insufficiency internal financing mode it chose not risky debt (short-term) first then risky debt 

(long-term debt), and finally the capital increase (Heaton, 2002; Malmendier and Tate, 2005). 

This implies that the optimistic manager seeks to reduce the debt maturity. 

 

Table-8. Target variable analysis 

c. Modal value: The average values of the explanatory variable for each the target value. 

The target variables analysis shows that 57, 8323% of Tunisian is opting to long term debt maturity, 26, 4416% choice average term debt 

maturity rate and 15.7261% prefer short term debt maturity. 

 

DM = LONG TERM (57,8323%) 
Nodes Binary mutual information Binary relative importanc Modal value 
FSIZE 0,3157 1,0000 BIG 86,7491% 

LA 0,0601 0,1903 NO 50,8906% 
CSC 0,0262 0,0831 EQ 24,5584% 

DI 0,0220 0,0695 OVERINV 14,4476% 
DV 0,0067 0,0211 YES 72,4981% 
OP 0,0029 0,0092 YES 58,3082% 

BIND 0,0015 0,0046 YES 58,9050% 
OVER 0,0007 0,0023 YES 60,7857% 

PF 0,0000 0,0000 NO 55,9322% 
DM = AVERAGE TERM (26,4416%) 

Nodes Binary mutual information Binary relative importance Modal value 
FSIZE 0,2305 1,0000 AVERAGE 71,5105% 

LA 0,0367 0,1593 YES 78,7610% 
DI 0,0215 0,0934 AS+LT+UNDIN 16,0929% 

CSC 0,0124 0,0537 EQ 28,4494% 
DV 0,0021 0,0089 YES 64,5319% 

OVER 0,0006 0,0026 YES 64,4348% 
OP 0,0000 0,0002 YES 56,2120% 

BIND 0,0000 0,0001 YES 57,5172% 
PF 0,0000 0,0001 NO 55,5406% 

DM = SHORT TERM (15,7261%) 
Nodes Binary mutuel information Binary relative importance Modal value 
FSIZE 0,0792 1,0000 SMALL 36,6580% 

CSC 0,0110 0,1386 EQ 33,1499% 
LA 0,0108 0,1368 YES 74,4477% 
OP 0,0064 0,0809 NO 55,2571% 
DI 0,0049 0,0618 AS+LT+UNDIN 16,9809% 

BIND 0,0034 0,0426 NO 50,8751% 
DV 0,0030 0,0381 YES 61,6434% 
PF 0,0001 0,0007 NO 57,0217% 

OVER 0,0000 0,0006 YES 63,0190% 



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Finally, target variable analysis show that CEO short term debt choice at 15, 7261% is 

positevelly correlated with its loss aversion at 74,4477%, its pessimism level at 55,2571%, its 

overconfidence at 63,0190%, its preference for AS+LT+UNDIN at 16,9809%,  firm equity choice 

at 33,1499%, firm small size at 36,6580%, dividend distribution choice at 61,6434%, firm low 

profitability at 57,0217% and firm depended board at  50,8751%. CEOs are powerful and 

dominate most major decisions, the risk arising from judgment errors is not well-diversified, 

resulting in more extreme decisions and higher variance of firm performance. This shower head 

to risk of loss of earnings or reputation seeks to limit the potential loss by choosing low-risk 

decisions including short-term debt choice. 

 

4.3. Average Target Maximizing Analysis 

Thus, the target dynamic profile capability software (BayesiaLab) to query about an a 

posteriori maximization of the target average. This test shows the case to maximize the target 

variable value. Table 7 presents the dynamic profile of the debt maturity (DM). 

 

Table-9. Target maximizing analysis. 

DM = SHORT TERM 

Nodes Optimal modality Probability Joint Probability  

priori 
 

15,7261% 100,0000% 

FSIZE SMALL 52,4081% 11,0000% 

DI LT+OVERINV 78,3799% 0,9174% 

CSC EQ 100,0000% 0,4528% 
DM = AVERAGE TERM 
Nodes Optimal modality Probability Joint Probability  

A priori 
 

26,4416% 100,0000% 

FSIZE AVERAGE 65,2017% 29,0000% 

DI ST+UNDERINV 93,4636% 3,0924% 

OP YES 100,0000% 2,1177% 
DM = LONG TERM 
Nodes Optimal modality Probability Joint Probability  

A priori 
 

57,8323% 100,0000% 

FSIZE BIG 83,6150% 60,0000% 
DI OVERINV 100,0000% 7,5841% 

Note: a. Optimal modality: modality is maximizing the traget value. 

b. Probability: the prior probability of each variable. 

c. Joint probability: the probability that the target variable takes the value n given that the explanatory variable takes the value p. For 

example, the probability of choosing big level of long term debt by firm big size is 60.000%. 

 

The target maximizing analysis show that 11.00% firm size decrease, 0, 4258% equity choice 

and 0,9174% overinvestment preference in long term project is positively correlated with CEO 

preference of short term debt at 15,2761%. The 29,000 % in firm size average rate, 3, 0924% 

increase CEO preference of underinvestment position in short term project  and 2,117 % CEO 



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optimism level increase is positively correlated with 26,4416 increase in its preference for debt  

average term maturity. This result implies the impact of CEO optimism in its firm policy. This 

Optimistic leader kept his place at the head of his company opts for long-term financial policy 

which long term debts choice. A leader optimistic with the growth opportunities of his business 

has an interest to limit the risk of hostile takeover. It seeks a debt threshold limiting the risk of 

failure, the risk of hostile takeover and indicating the health of the business. Finally, target 

dynamic profile analysis show that CEO increased preferences for long term debt maturity of the 

order of 57,832% its correlated with its firm size increased at 60,00% and its preference for 

overinvestment position at 7,5841%. 

 

5. CONCLUSION   

This research examines the determinants of firms’ debt maturity choice introducing a 

behavioral perspective. Theoretical analysis presented implications of managerial characteristics 

(Emotional biases) to explained his preference for debt maturity position. Thus, the optimism of 

the leader over the problem of managerial opportunism described by the agency theory in debt 

choice. Leader optimistic interest in bringing to the maximization of shareholder wealth and to 

know in order to optimize the flow of funds. Debt nature analysis by introducing behavioral 

dimension enriched organizational financial theory: leader affected by behavioral biases presence 

prefer long term debt maturity allowing this protect against the takeover operation Russianness. 

Empirical analysis presenting survey CEO large private companies in Tunisia. Data analyses 

revealed CEO emotional biases importance in explaining his debt nature choice. Indeed, empirical 

relationship analysis between optimism and firm debt maturity shows behavioral dimension role 

in the explanation. CEO optimism level is positively correlated with a preference for long 

term/and or average term debt. Optimistic CEO with its firm growth opportunities uses its 

decisional leverage to improve its reputation on the labor market. He opts for long-term debt 

indicating the convergence of its interests with those of shareholders.  We also note that CEO 

loss aversion level is positively correlated with firm debt maturity choice. Loss aversion causes 

CEO to overestimate its firm total risk. This overestimation of the risk of the business leader 

impulse decision to choose less risky. He prefers the short-term debt low risk compared to long 

term. Finally, the CEO debt maturity  behavioral analysis is consistent with the corporate 

financial theory, the leader affected by behavioral biases adjusts its debt maturity based on their 

ability to assess alternatives (optimism and overconfidence) and risk perception (loss aversion) to 

create of shareholder value and ensure its place at the head of the management team. 

 

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