CEO characteristics and firm’s performance

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Amsterdam Business School Faculty of Economics and Business Master of Science in Business Economics : Finance Master Thesis

CEO Characteristics and Firm’s Performance

Name: Lorin Dhimitri Student ID: 10826696 Supervisor: Mark Dijkstra Submitted at: 7th July, 2016

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Statutory Declaration I herewith declare that I have completed the present thesis independently, without making use of other than the specified literature and aids. Sentences or parts of sentences quoted literally are marked as quotations; identification of other references with regard to the statement and scope of the work is quoted. The thesis in this form or in any other form has not been submitted to an examination body and has not been published. This thesis has not been used, either in whole or part, for another examination achievement.

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Abstract

This paper examines the relationship between CEO’s characteristics such as age and firm’s performance indicators. As firm’s performance indicators are defined the Sales and Asset Growth, Tobin’s Q ratio, Return on Assets and also the volatility of the daily stock returns. The sample includes 1,233 U.S. firms and 1,737 CEOs during the period between 20082014. Empirical results show that there is a negative significant correlation between age and firm performance. Also it is investigated especially this relationship around the retirement period which is defined around the age of 65. The outcome of this investigation shows that market performance of the company is correlated positively with age around that period

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Introduction

A large number of scholars, whose work is presented in this paper, have examined the degree of influence of CEO’s characteristics and firm performance. Age and previous experience as a CEO have been proved to affect in a negative and positive way respectively the profitability of the firm (Zhang, 2010). More specifically, age and experience are clearly two factors which are positively associated (e.g., Barker & Mueller, 2002; Musteen, Barker, & Baeten, 2006) to firm outcome. Hambrick and Mason (1984) introduce the Upper Echelons theory where they support that the age of the managers additionally to other characteristics might affect company’s results. Murphy (1999) proposes that the correlation between outcometurnover has weakened during the 1990s and that scholars try to explore the connection to other characteristics such as CEO age. Wiersema and Bantel (1992) show that it is more possible for a younger higher management to go through corporate strategic changes and more specifically they support that the firm’s need to remain vital (profitable) within its environment depends on its capability to adjust in this environment by implementing various changes in its corporate strategy. Child (1974) who performs a research between more than eighty British firms and eight hundred senior managers, indicates the correlation of top managers and company’s growth but he finds no evidence that the first is also associated with profitability. This paper is investigating the effect of these CEO’s characteristics such as age and experience on firm’s performance. The sample includes 1,233 different U.S. firms and 1,737 CEOs throughout the fiscal years from 2008 until 2014 . The results show that there is a negative correlation between age and performance as predicted in the existing literature and a positive one among experience and performance.

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Zhang (2010) shows a solid negative performanceage correlation. More precisely he shows a negative relationship between agegrowth and agemarket value. The same negative correlation between ageprofitability is observed when the CEOs are older and work for large companies but it changes its sign to positive when they are younger and the firms they serve are smaller. Moreover, Serfling (2014) finds that the CEO age is negatively correlated to stock return volatility. Specifically he documents that older CEOs tend to spend less in R&D and maintaining the financial leverage of the firm on a lower level. On the other side, McClelland, Barker and Yong OH (2012) examine the influence of experience which is measured as years of tenure on the future performance outcome. They argue that as the CEO tenure increases, their paradigms are becoming more obsolete, leading to a negative effect on future firm performance within dynamic industries.

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Literature Review

Empirical researchers which are listed in this paper also have attempted to track the relationship among management age, strategic choice and firm performance in the past decades. Murphy (1999), using a data set which includes S&P 500, S&P 400 FOR midcap firms and for small cap firm the S&P 600 across the period 1992 through 1995 shows that smaller companies have a higher turnover to performance sensitivity and that CEO age becomes more explanatory (the coefficient of age becomes larger when the size of the company increases) about the turnover when the firms become larger. Thus, the age associated variation of a manager’s personal needs leads to different working stances and attitudes. Richard and Shelor (2002) show that the diversification of age in top management is correlated negatively to return on asset of the firm and positively to sales growth. Bertrand and Schoar (2002) made an empirical study, which included 600 American companies and 500 managers, and they concluded by measuring the level of capital expenditures that manager’s age is associated in a positive way with lower risk business practices. Hart and Mellors (1970) investigate the correlation of the chairman age and the growth of the assets. They support that firms led by a younger manager grow at a faster but more volatile rate. Moreover, some other scholars support that the volatility of the stock return is a measure of overall riskiness of the firm (Guay (1999); Cassell, Huang, Sanchez, and Stuart (2012); Kini and Williams (2012)) Scharfstein and Stein (1990) , Hirshleifer and Thakor (1992), Holmstrom (1999) also build models incorporating career concerns which predict that older CEOs are less risk averse than the younger ones and that is due to the low reputation of the second as high quality managers

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because of their age. More precisely, they support that the older CEOs have already proven their capability and skills of managing and based on that a potential negative outcome of an investment will not affect their future career in the same degree it would in case of a young CEO. Thus, the younger CEOs’ career is affected in a larger degree in case of a poor performance result and their future career potentials could be reduced significantly, a fact that drives them to follow more conservative investment policies. In addition to that, Zwiebel (1995) shows that the older executives will invest in more innovative ideas unlike the younger ones who will implement investment practices which are standard and common across the industry and thus easier to be evaluated. Fama (1980) supports that older CEOs have substantially less wealth concerns compared younger CEOs since the second have considerable future compensation. Fama (1980) is among the first to support that the manager’s career concerns can diminish the agency problem1_{, a fact that leads to the improvement of financial outcome of the firm. As the years} pass for the younger CEOs the total value of the forthcoming compensation will diminish, leading to larger agency costs2_{and lower financial firm performance. Furthermore, it is} observed that CEOs who serve for firms with greater agency problems receive a higher compensation and also that companies with higher agency problem perform worse (Core, Holthausen, and Larcker (1999)). According to existing literature the ending point of CEO’s career horizon is placed around the age of 65. Barro and Barro (1990), Murphy, (1999) and Brickley, (2003) find that the usual 1_{Agency problem is defined as the conflict of interest between between two parties when the first party is expected to act in} second party’s interest. The problem occurs when the first party (the agent) instead of taking the most beneficial decision for the second party( company, shareholders etc.) decides differently, stimulated by selfinterest. 2 _{Agency costs are internal costs which are charged to the firm and paid to the agent (manager, CEO) in order the second to} act on behalf of the first.

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contributor to this decision than performance. Zhang(2010) using a sample of 1,390 U.S. industrial companies and 1,940 CEOs for the period of 1992 through 2006, shows that CEOs who own a larger number of stocks tend to remain in their position after the age of 65, while CEOs who have higher nonincentive compensations or longer firm tenure usually do not continue serving after the regular department time. Also another interesting finding is that CEOs of larger companies retire around the age of 65, in contrast with those of smaller and fast growing firms who hold their hold the position even after the age of 65. Yermack (2006) shows that when the CEOs retire, can receive exceptional separation benefits on top of their annual compensation, and these rewards can be related to the preretirement company’s financial outcome. The fact that the CEO knows when he is approaching the age of 65 creates the so called horizon problem which is observed when the CEOs are close to the retirement age. This makes them having no future career concerns since in a short period they will no longer belong to the active workforce. Therefore, the absence of these concerns might escalate the agency problem and stimulate these preretirement CEOs to affect company’s performance in order to promote their own interest at the cost of stakeholders. Moreover, Gibbons and Murphy (1992) support that companies need to use more stock based incentives in order to scale down the agency problem. Dechow and Sloan (1991) using a sample from 1979 to 1989 of CEOs successions document evidence that CEOs tend to cut R&D expenses when close to retire in order to enforce the shortterm earnings performance and in this way to receive a higher compensation which is usually based on the results of earnings. Thus, these actions engender the cost of the company’s longtime outcome. In addition to that, Smith and Watts (1982) and Bizjak et al. (1993) both propose the idea that firms can postpone the incentive payments for the post

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retirement period. The reasoning behind this tactic is that these incentive payments will be linked with the future firm’s performance. Whereas the performancebased incentive plan has been deployed to induce the close to retirement CEOs to pursue some optimal investment opportunities, this specific incentive category stimulates the CEOs to boost the shortterm earnings in order to receive higher personal income. On the other side, Brickley et al. (1999) find an overperformance of company’s return on asset (ROA) prior to the anticipated retirement but they state that the possibility for the retired CEO to serve for the internal or external board after the scheduled retirement is correlated in a positive way to the priorretirement share and accounting performance. Hence, the chance of directorship after the retirement grant preretirement CEO with new concerns about his career which will neutralize the possible horizon problem. Gillan, Hartzell, and Parrino (2009) show that in order to induce younger managers to overcome the fact of having more to lose in the case of a negative financial outcome, companies are proving them a special employment agreement in which the CEOs are covered in case of a bad investment choice Serfling (2014), using a sample of 2,356 companies and 4,493 CEOs through the period 19922010 shows that companies chaired by younger CEOs will have higher stock return volatility. Barker and Mueller (2002) using a sample of 172 firms from 1989 to 1990 propose that age is correlated negatively with R&D expenditure. Lastly, Custodio and Metzger (2013) prove that CEOs of nonfinancial companies with a previous experience in the financial field are implementing specific financial policies which benefit the firm such as adapting the company’s leverage under market conditions1_{, a fact that could be overall beneficial to the} shareholders. 1_{An example of financial policy is to bring the financial leverage of the firm at the market average level by increasing or} decreasing it

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Methodology

Model 1 is deployed:

Performance = β_o,i + β_1,i Age_i + β_2,i Firm_Size_i + β_3,i CAPEX_i + β_4,i Leverage_i + β_5,i Salary_i + β_6,i Retirement_Period_i + β_7,i Fixed_Effects_i+ ε_i

To investigate the correlation between firm performance and CEO’s characteristics OLS regressions were employed. Firm performance which is the dependent variable is expressed by 4 indicators, Sales Growth, Asset Growth, Tobin’s Q, Return on Asset (ROA) and Volatility which shows us the riskiness of the firm and which according to Peng (2015) is correlated to performance . The first 4 categories consists of information about firm performance and include the change of revenues, the change of total assets, the Tobin’s Q ratio and return on assets. As Sales Growth is determined the percentage change in annual base of revenues.Asset Growth is set the annual percentage change of a firm’s total assets. Tobin’s Q ratio is according to Lewellen and Badrinath (1997) the market to book ratio of total assets. The ratio of Net Income to total assets represents the Return on Assets (ROA). The fifth category that is used as a dependent variable is the volatility of the equity and more specifically the annual standard deviation of the daily stock returns. Age is the chronological age of the CEO. According to Barker and Mueller (2002) there is a number of variables that control firm characteristics. As firm’s characteristics the variables that are used are the size of the company, the financial leverage and the capital expenditure. As firm’s size is defined the natural logarithm of total assets. Financial leverage is the ratio of the value of total debt to the value of total assets. CAPEX is the ratio of capital expenditure to sales. The variable Salary

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refers to the total annual salary the CEO receives. CEO Experience is defined as the years served as CEO but due to high correlation with CEO’s Age, the pure effect on performance is examined separately. Moreover, in order to investigate the abnormalities around the retirement age which are predicted due to the horizon problem (Hambrick & Mason (1984)), Retirement_Period is deployed. Retirement_Period is a dummy variable that equals 1 if the CEO age is above 60 and below 67, otherwise is 0. The CEO’s retirement period 60 to 67 is similar to the one which is used by Zhang(2010) (63 to 67).

Robustness Check

Fixed effects are also included in order to avoid any potential bias due to dissimilarities across different industries. For each different industry a dummy which equals to 1 if the observation belongs to this industry, 0 otherwise, is introduced. Also the pure effect of age and experience on performance indicators is examined.

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Data and Descriptive Statistics

In order to be performed this research is used a sample which spans from 2008 to 2014 and contains 4,953 fiscalyears observations. This data consist of 1,233 U.S. firms and 1,737

CEOs, and the source for both of them is Compustat and ExecuComp respectively.

Panel A. Summary Statistics

N Mean Std. Dev. Min Max

Age 4,953 56.07 7.53 28 95 CEO Experience 4,953 8.09 7.83 0 60 Performance Variable Sales Growth 4,953 8.3 36.8 98 965 Asset Growth 4,953 9.5 29.1 78.3 581 Tobin's Q 4,953 3.91 20.03 0.05 1,002 ROA 4,953 3.3 12.1 176 84 Volatility 4,953 0.04 0.20 0.004 10.65 Firm and CEO characteristics Firm Size 4,953 15,088 110,626 10,012 2,573,126 Leverage 4,953 20 17.8 0 95.3 CAPEX 4,953 9 30.2 0 1,082.3 Salary 4,953 753 378 0 3,867 Sales Growth, Asset Growth, ROA, Leverage and CAPEX are expressed in percentages. Tobin’s Q is the ratio of firm’s market to book value. Firm Size is expressed in $ millions, Salary is expressed in thousands $.

As shown in Panel A the average CEO age is 56.07 years and the average CEO experience is 8.09 years.

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Panel B. Performance of CEOs who are close to Retirement versus the ones who are not Close to Retirement Not Close to Retirement All Sample

Mean Mean Mean

Std.

Dev. Min Max

Sales Growth 6.6 8.3 8.3 36.8 98 965 Asset Growth 9.2 9.5 9.5 29.1 78.3 581 Tobin's Q 4.2 3.9 3.91 20.03 0.05 1002 ROA 1.9 3.3 3.3 12.1 176 84 Sales Growth, Asset Growth and ROA are expressed in percentages. Tobin’s Q is the ratio of firm’s market to book value. Panel B shows a comparison in terms of firm’s performance of CEOs who are close to retirement against those who are not. It can be noticed that the only category in which the CEOs close to retirement have a higher average performance is that of Tobin’s Q.

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Empirical Results and Discussion

Table 1. OLS Regression General Analysis

Dependent Variable

Sales Growth Asset Growth Tobin's Q

Independent Variable Age 0.001** 0.001** 0.001*** 0.001*** 0.018*** 0.013** (2.15) (2.15) (3.42) (3.11) (2.95) (2.12) Firm Size 0.009** 0.007* 0.009*** 0.007*** 0.375*** 0.180*** (2.37) (1.81) ( 1.94) (2.68) (10.86) (4.80) CAPEX 0.090*** 0.082*** 0.111*** 0.125*** 0.654*** 0.085 (5.04) (4.20) (8.20) (8.11 ) (4.24) (0.50) Leverage 0.018 0.013 0.014 0.029 4.034*** 4.506*** (0.61) (0.42) (0.61) (1.18) (14.52) (16.22) Salary 0.00 0.00 0.00** 0.00*** 0.00*** 0.00*** (0.46) (0.50) (2.41) (2.57) (5.64) (3.45) Intercept 0.237*** 0.167 0.168*** 0.027 5.548*** 2.995*** (5.36) (1.54) (4.82) (0.33) (14.04) (3.17)

Fixed Effects No Yes No Yes No Yes

Number of observations 4,953 4,953 4,953 4,953 4,953 4,953 Fvalue 29.70 3.75 18.62 8.06 59.49 38.42 Rsquared 0.0291 0.0105 0.0185 0.0223 0.0567 0.0982 Adj Rsquared 0.0282 0.0077 0.0175 0.0196 0.0558 0.0957 Model 1 is fitted, with the exception of variable ‘Retirement Period’, using as a total sample of 4,953 observations. In parenthesis are stated the tvalues. *, **and *** refer to the 90%, 95% and 99% confidence levels, respectively_.

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The main purpose of this research is to examine what is the relation between the CEO’s characteristics such as age and firm’s performance. This performance is expressed by the dependent variables of Table 1. Sales Growth and Asset Growth are the first two dependent variables for which the correlation with age is examined. The Age coefficients referring on Sales Growth and Asset Growth are both negative and significant at 5% and 1% level respectively. This means that the company’s growth is diminishing along with the executive’s aging process. Furthermore this negative correlation between age and growth is consistent with previous studies ( Fama (1980), Child (1974) and Hambrick and Mason (1984)). Similarly, the association between Tobin’s Q and age is negative and significant at 1%, showing that there is a negative connection between firm’s market value and the CEO’s aging process. On the other side the relationship between age and ROA is minor and not significant, so we can infer that the executive’s age does not affect the profitability of the company. These last two conclusions are in line with the finding of Zhang (2010) who showed that age is negatively correlated with the market value of the firm and having no effect on profitability. Also no association is documented between the age and the firm’s riskiness which is expressed by the volatility of the stock returns, something that is not corresponding with Chen and Zheng (2014) who show a minor but significant at 1% level of negative correlation between age and volatility. In addition, the correlation between salary and performance is minor, a fact that does not confirm the argument that nonincentive compensation may lead to higher agency cost and lower firm performance (Mehran, 1995; Core et al., 1999).

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Table 1. OLS Regression General Analysis (Continued) _{Dependent Variable} ROA Volatility Independent Variable Age 0.00 0.00 0.00 0.00 (1.18) (1.06) (0.13) (0.18) Firm Size 0.008*** 0.012*** 0.008*** 0.009*** (7.76) (10.38) (12.06) (12.88) CAPEX 0.020*** 0.012** 0.00 0.003 (4.30) (2.32) (0.03) (0.87) Leverage 0.099*** 0.096*** 0.017*** 0.013** (11.61) (11.07) (3.12) (2.34) Salary 0.00*** 0.00** 0.00*** 0.00*** (4.14) (2.35) (5.69) (6.46) Intercept 0.008 0.140*** 0.082*** 0.143*** (0.71) (4.77) (10.36) (7.39)

Fixed Effects No Yes No Yes

Number of observations 4,953 4,953 4,953 4,953 Fvalue 51.84 28.15 29.70 14.10 Rsquared 0.0498 0.0739 0.0291 0.0384 Adj Rsquared 0.0488 0.0713 0.0282 0.0357 Model 1 is fitted, with the exception of variable ‘Retirement Period’, using as a total sample of 4,953 observations. In parenthesis are stated the tvalues. *, **and *** refer to the 90%, 95% and 99% confidence levels, respectively

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As a robustness check the same regressions have been employed with the addition of Fixed Effects as dummies representing each industry. The relationship between the Age and Asset Growth, ROA and Volatility remains the same while for Sales Growth and Tobin’s Q the association with Age changes only in terms of significance level which decreases from 1% for the model excluding Fixed Effects to 5% for the one which includes Fixed Effects. Table 2. Pure effect of Age on Performance Indicators Depended Variable

Sales Growth Asset Growth Tobin's Q ROA Volatility

Age 0.001*** 0.001*** 0.018*** 0.00 0.00 (2.67) (3.61) (2.91) (0.41) (0.29) Intercept 0.17*** 0.170*** 4.122*** 0.030*** 0.038*** (4.78) (7.37) (11.49) (2.75) (5.43) Number of observations 4,953 4,953 4,953 4,953 4,953 F(6, 4513) 7.14 13.01 8.44 0.17 0.08 Rsquared 0.0014 0.0026 0.0017 0.0000 0.0000 Adj Rsquared 0.0012 0.0024 0.0015 0.0002 0.0002 The pure effect of the age on firm’s performance indicators is also examined on Table 2. The results are similar to the ones of Table 1 where Sales Growth, Asset Growth and Tobin’s Q are in a high significance level negatively correlated. Moreover the pure effect of CEO’s experience on firm’s performance is investigated on Table 3. Sales Growth, Asset Growth and Tobin’s Q do not appear to be correlated with experience due to minor and insignificant coefficients. On the other side, profitability of the

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firm expressed by ROA and the firm’s riskiness seem to associate with CEO Experience in a minor but significant in 1% and 10% level respectively. These results are not in line with Hamori and Koyuncu (2015) who find that experience in the CEO position is negatively related to firm performance. Table 3. Pure effect of previous CEO experience on Performance Indicators Depended Variable

Sales Growth Asset Growth Tobin's Q ROA Volatility

CEO Experience 0.00 0.00 0.016 0.0004*** 0.0002* (2.63) (1.50) (2.68) (2.58) (1.90) Intercept 0.077*** 0.082*** 3.221*** 0.030*** 0.034*** (10.30) (18.71) (46.85) (14.62) (25.52) Number of observations 4,953 4,953 4,953 4,953 4,953 F(6, 4513) 1.25 2.24 7.19 6.66 3.59 Rsquared 0.0003 0.0005 0.0015 0.0013 0.0007 Adj Rsquared 0.0001 0.0002 0.0012 0.0011 0.0005 The horizon problem is an issue mentioned multiple times in the existing literature. I am investigating the correlation between the CEO’s age and firm’s performance around the regularly expected end of the career horizon. The ending point has been placed by the existing literature (Brickley et al., (1999)) around the age of 65.

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Table 4. OLS Regression Retirement Period

_{Dependent Variable}

Sales Growth Asset Growth Tobin's Q

Independent Variable Age 0.001** 0.001** 0.001*** 0.001** 0.020*** 0.015** (2.11) (2.10) (2.84) (2.55) (3.28) (2.42) Firm Size 0.009** 0.007* 0.007*** 0.009*** 0.381*** 0.188*** (2.36) (1.79) (3.13) (3.70) (11.01) (4.97) CAPEX 0.087*** 0.082*** 0.082*** 0.093*** 0.656*** 0.086 (5.04) (4.20) (8.11) (8.16) (4.25) (0.50) Leverage 0.018 0.013 0.001 0.008 4.018*** 4.494*** (0.61) (0.42) (0.09) (0.44) (14.47) (16.17) Salary 0.00 0.00 0.00** 0.00*** 0.00*** 0.00*** (0.32) (0.51) (3.46) (3.52) (5.85) (2.78) Retirement Period 0.001 0.004 0.033 0.033 0.754** 0.651** (0.05) (0.11) (1.54) (1.55) (2.30) (2.02) Intercept 0.237*** 0.166 0.120*** 0.022 5.683*** 3.058*** (5.29) (1.54) (4.58) (0.36) (14.23) (3.23)

Fixed Effects No Yes No Yes No Yes

Number of observations 4,953 4,953 4,953 4,953 4,953 4,953 Fvalue 7.50 3.50 16.31 7.81 50.50 36.15 Rsquared 0.0090 0.0105 0.0194 0.0232 0.0577 0.0990 Adj Rsquared 0.0078 0.0075 0.0182 0.0202 0.0566 0.0962 Model 1 is fitted using as a total sample of 4,953 observations. As retirement period is defined the CEO age period between 60 to 67. In parenthesis are stated the tvalues. *, **and *** refer to the 90%, 95% and 99% confidence levels, respectively

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Table 4. OLS Regression Retirement Period (Continued) _{Dependent Variable} ROA Volatility Independent Variable Age 0.00 0.00 0.00 0.00 (0.97) (0.80) (0.15) (0.49) Firm Size 0.008*** 0.012*** 0.008*** 0.010*** (7.83) (10.48) (12.17) (13.01) CAPEX 0.020*** .012** 0.00 0.003 (4.30) (2.32) (0.04) (0.88) Leverage 0.099*** 0.095*** 0.017*** 0.013** (11.58) (11.03) (3.08) (2.30) Salary 0.00*** 0.00** 0.00*** 0.00*** (3.99) (2.16) (5.85) (6.64) Retirement Period 0.012 0.015 0.011* 0.012* (1.23) (1.50) (1.78) (1.90) Intercept 0.010 0.142*** 0.084*** 0.144*** (0.89) (4.81) (10.51) (7.45)

Number of observations 4,953 4,953 4,953 4,953 Fvalue 43.45 26.43 25.29 13.41 Rsquared 0.0501 0.0743 0.0298 0.0392 Adj Rsquared 0.0489 0.0715 0.0286 0.0362

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Table 4 results suggest that the correlation between the growth of the firm and the CEOs who retire around the regular retirement period is negative but not significant. On the contrary, Age around the retirement period and market value are strongly correlated in a positive and significant at 1% level. Profitability does not appear to be affected by Age even around the retirement period, while firm’s riskiness is positively correlated at 10% significance level with Age around the retirement period. Fixed Effects when added to the model do not change the results. Table 5. Pure effect of Age on selected firm's performance indicators during retirement period Dependent Variable Sales Growth Tobin's Q Age 0.001*** 0.001** 0.020*** 0.014** (2.63) (2.53) (3.13) (2.27) Retirement Period 0.002 0.004 0.578* 0.616** (0.06) (0.13) (1.72) (1.88) Intercept 0.187*** 0.997 4.024*** 3.733*** (4.73) (0.95) (11.61) (3.95)

Number of observations 4,953 4,953 4,953 4,953 F(6, 4513) 3.57 2.13 5.70 22.67 Rsquared 0.0014 0.0047 0.0023 0.0480 Adj Rsquared 0.0010 0.0025 0.0019 0.0459

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Table 5 shows that Age’s pure effect of company’s growth is not significant but is strongly, positively and significantly at 10% correlated to firm’s market to book ratio. All the above suggest that the agency problem that has been predicted for CEOs around retirement period may still exists for a specific part of company’s performance such as market value. On the other side no correlation was documented between firm’s growth and profitability and CEO Age around retirement period. Also another important finding is that the coefficient of age on firm’s risk (risk may affect positively the firm’s performance Peng (2015) is positive and significant at 1% level around retirement period.

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Conclusion

In the existing literature has been investigated how the CEO’s characteristics may affect the firms they serve. Characteristics such as age and experience are supported to affect firm performance negatively across the CEO’s aging process. Richard and Shelor (2002) show that the diversification of age in top management is correlated negatively to return on asset of the firm and positively to sales growth. Bertrand and Schoar (2002) concluded that manager’s age is associated in a positive way with lower risk business practices. Zhang (2010) shows that CEOs’ age is negatively correlated to firm performance and that CEOs who have higher nonincentive compensations or longer firm tenure usually do not continue serving after the regular department time around the age of 65. In this paper it is examined the effect of CEO’s characteristics and especially age on firm’s performance indicators such as Sales Growth, Asset Growth, the ratio of market to book value, Return on Assets (ROA) and also firm’s risk which is expressed by the volatility of stock returns. This relationship between age and performance especially investigated also around the retirement period The sample consists of U.S. firms with 4,953 fiscal years observations throughout the period 20082014. Empirical evidence shows that age and performance indicators such as firm’s growth and market value are negatively correlated. Also around the retirement age it is observed that age is positively correlated to market value. Overall some further contribution could be an investigation of the effect the CEO’s characteristics may have on other firm’s indicators such as Return on Investment (ROI) or Return on Equity (ROE).

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Appendix

Variable Definition CEO Age CEO's chronological age CEO Experience Years a person served as CEO Sales Growth Annual percentage change of a firm's revenues Asset Growth Annual percentage change of a firm's total assets Tobin's Q A firm's market value to book value ratio ROA Return on Asset (Net income to total assets) Volatility Standard deviation of a firm's daily stock return Firm Size Natural logarithm of a firm's total assets CAPEX Capital expenditure to total sales ratio Leverage Total debt to total assets ratio Salary Annual salary a CEO receives Retirement Period The CEO's age period between 60 to 67 years

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CEO characteristics and firm’s performance