ceo characteristics and value of cash holdings annual...the relation among age, tenure, professional...
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CEO Characteristics and Value of Cash Holdings
Seung Hun Han*, Dongwook Seo**, SeongJae Mun***
Korea Advanced Institute of Science and Technology, Korea
Abstract
This study examines the effect of CEO characteristics on the value of excess cash using the
listed firm data in Korea. We find that firms with business major CEO have significantly higher
value of excess cash compared to the other firms, but science and engineering major have no
significant effect. Moreover, firms with MBA CEO and master CEO increase the value of
excess cash. Age and tenure have negative impact on the value of excess cash, but newly
appointment of CEO has positive impact on the value of excess cash. Our study suggests that
the value of excess cash depends on CEO characteristics such as educational backgrounds and
CEO demographics.
JEL Classification: G30, G32, G34 Keywords: Cash holdings, CEO characteristics, Educational backgrounds, CEO
demographics, Agency Theory, Upper Echelons Theory * E-mail: [email protected] ** E-mail: [email protected] *** Corresponding author / E-mail: [email protected] Address: N22 #2122, 335 Gwahangno, Yuseong-gu, Daejeon 34141, S. Korea Tell: +82-42-350-6309 Fax: +82-46-350-6334
1. Introduction
The characteristics of CEOs who have been distinguished in the industry have been highlighted
in both academia and the media. For example, as Harvard Business Review annually nominates
the top 100 best-performing CEOs, it reports the actual business performance of leading CEOs
based on their novel characteristics that have contributed to the economy.1 Moreover, the
media has highlighted news regarding on the most distinctive or leading CEOs.2 Therefore,
this research is to examine the characteristics of leading CEO groups. If the facts that general
characteristics of outperforming CEOs are related to risk management skills are proven right,
those characteristics could be used as an important measure when a company needs to choose
a new CEO.
Recently, CEOs with liberal arts background, especially business management, are the
paramount concern in Korea during the recession. Companies newly appoint CEOs with
business background to overcome the economic recession with a strong expectation that CEOs
with business background can stabilize the crisis and propel the long-term new business. In the
case of business groups such as Samsung and LG in 2015, CEOs with business background are
more appointed increasingly in comparison to the year of 2014 for overcoming constant
recession. 3Based upon this anecdotal evidence, this paper is motivated from the research
question what kinds of specific characteristics of CEOs have an impact on the managerial
1 Harvard Business Review publishes articles about the worldβs best-performing CEOs to assess a CEOβs performance and to understand how CEOs handle their business problems with regard to leadership. These articles, which are published annually, provide information for investors about the operating policies of CEOs by analyzing outperforming CEOsβ operating styles and special characteristics. 2 Newsmax releases its list of 100 most influential business leaders in the US. In addition, Forbes selects the best and worst CEOs and reports their contributions and errors. 3 According to the Maeil Business News Korea (2015.12.06), the percentage of newly appointed CEOs with business background in Samsung business group has been increased from 25% to 57%, from the year of 2014 to 2015. In LG business group, the percentage of newly appointed CEOs with business background has been increased from 33% to 50%, from the year of 2014 to 2015.
performance.
The effect of CEO on corporate strategies and financial policies is widely examined
by the previous literature (Barker III and Mueller (2002); Bertrand and Schoar (2002); CustΓ³dio
and Metzger (2014); Dittmar and Duchin (2016); Galasso and Simcoe (2011); Huang-Meier,
Lambertides and Steeley (2015); Kaplan, Klebanov and Sorensen (2012); Malmendier and Tate
(2005); Manner (2010); Orens and Reheul (2013); Weisbach (1995 )). According to the
previous literature, when selecting a company-level strategy, a CEO chooses the strategy that
maximizes the companyβs performance (Bebchuk and Stole (1993 )). In addition, according to
the upper echelon theory (UET) (Hambrick and Mason (1984 )), strategic choices of a company
are affected by upper echelon characteristics. Furthermore, firm performance is sequentially
influenced from strategic choices triggered by managerial background characteristics.
Therefore, CEO characteristics are one of the major factors affecting future firm performance
successively. Influenced by their characteristics, CEOs may choose strategic actions within
their available free-cash flow, such as acquisitions, divesture, financial choices, and resource
allocation. More specifically, CEOs determine the number of acquisitions, investment ratio,
and cash holdings (Hambrick and Mason (1984 )).
Among the studies of the effect of CEO characteristics and financial policies of firm,
we focused on CEO characteristics and cash holdings. On the previous literature, Jensen (1986 )
finds that self-interest of managers affects the amount of cash reserves for their self-interest
instead of maximization of shareholder wealth. Recently, previous studies find the link between
cash holdings policy and the effect of CEO characteristics. Orens and Reheul (2013 ) find that
age and tenure of CEOs have a positive impact on the amount of cash holdings for the
stabilization. Dittmar and Duchin (2016 ) demonstrate effects of professional experiences of
CEO on corporate debt and cash policies. Huang-Meier, Lambertides and Steeley (2015 ) show
that optimism of CEOs has significantly affected cash holding policies.
In the study of Dittmar and Mahrt-Smith (2007 ), the authors find that Micorosoft
consistently holds excess cash that is significantly larger than the median excess cash of U.S.
firms. After controlling factors that affect operational motivation of cash holdings, firm,
industry, and year effect, unexplained factors which is influenced by the effect of CEO still
occurs. Likewise, this effect is called βBill Gatesβeffect. While previous literature illustrates
the relation among age, tenure, professional experiences and CEO optimism, the link between
characteristics of CEOs and cash holdings is not explained from empirical evidence. We
attempt to fill the research gap between each CEO characteristic and corporate cash policy,
focusing on the value of excess cash. Thus, our aim through this research is to find out what
kinds of special CEO characteristics influence firm value, which is focused especially on value
of excess cash through modified model of Fama and French (1998) by using Korean listed firm
data.
Korea suffers from the excess cash problem by the statement of Bank of Korea (2015)4.
The amount of cash and cash equivalent of top 500 listed firms in Korea hits the highest record
throughout the history, and the amount is 158 trillion Korean won in 2014. From this anecdotal
evidence, we expect that the effect of CEO characteristics on cash holdings of firm is highly
increasing. Moreover, CEOs in Korean market generally have shown the tendency to have both
of high education levels and condensed education backgrounds, specifically such as majored
in business and science and engineering. 5 However, the research regarding on CEO
4 The Currency and Liquidity Report is quarterly provided by the Bank of Korea, and this report contains current conditions about currency such as the currency amount of listed firms in Korea and the cash related policies. 5Throughout the dataset we have acquired, CEOs in Korea have graduated from college and have higher education
levels (28.7% have shown education levels upper than masterβs degrees), while 45.4% majored in business and 37.69% majored in science and engineering.
characteristics in emerging markets, especially in East Asia, has not been properly studied yet.
Therefore, Korean samples are good experimental sets which can complement the lack of
research on CEOs in emerging markets since Korean samples could explain the relation of
CEOβs high education levels and the highly condensed educational fields such as business and
science and engineering in comparison with other countries. In this sense, this study is
appropriate to find out effects or relation of education levels and concentration tendency of
specific majors.
The sample in this study consists of 534 firms from 2005 to 2013 listed on the Korea
Composite Stock Price Index (KOSPI), which is the representative stock market index of all
common stock market divisions in Korea. Financial data is obtained from the Data Guide data
base program. We hand-collected 2,574 firm-year CEO characteristic observations from the
annual reports of each individual firm from the Data Analysis, Retrieval and Transfer System
(DART) provided by the Korean Financial Supervisory Service during 2005-2013.
To test the effect of CEO characteristics on the cash holdings from the perspective of
shareholders, we follow the modified model of Fama and French (1998 ). Pinkowitz, Stulz and
Williamson (2006 ) modified model of Fama and French (1998 ), and we mainly examine the
coefficient of excess cash holdings to find the contribution of excess cash on market value of
the firm in conjunction with CEO characteristics variables. Moreover, to measure the excess
cash holdings, we follow the model of Bates, Kahle and Stulz (2009), and to solve the
endogeneity problem we employ 2 year lagged sales growth as instrument variable in 2SLS
regression from the study of Dittmar and Mahrt-Smith (2007 ).
` Nevertheless CEOs who majored in science and engineering did not significantly
affect the value of cash, we find that CEOs with business background or MBA degree manage
cash well to improve the value of excess cash. Considering education levels, CEOs with master
degree show better managerial ability to increase value of excess cash. However, in the case of
CEOs with excessively higher education levels, we could not find any significant association
between their doctoral degree and the value of excess cash. In addition, we confirm that as the
tenure of CEOs lasts longer and the age of CEOs gets older, the value of excess cash becomes
decreased. On the other hand, newly appointed CEOs have tendency to increase the value of
excess cash. In crisis period, we find that only three groups of newly appointed CEOs, CEOs
with business background or MBA degree seem to be well evaluated by stock investors based
on our empirical results showing significantly positive effect on the value of excess cash.
To the best of our knowledge, this study is the first to analyze how efficiently CEOS
with different characteristics manage cash well. In addition, by using recent data and applying
a more detailed classification of CEO characteristics, we contribute to CEO-related research
by adding empirical evidence that CEO characteristics affect the value of excess cash as
determinants of performance of corporate cash policy.
The rest of this paper is as follows. Section 2 summarizes the development of our
studyβs hypotheses based on a literature review. Section 3 provides explanation of the data,
variables and the empirical models used to estimate the level of normal cash and excess cash,
and to find the effect of CEO characteristics. Section 4 explains empirical results. Section 5
describes discussion and limitations of our study. Finally, Section 6 concludes our study.
2. Hypothesis Development
2.1. CEO and corporate strategy
According to Agency Theory, shareholders appoint CEOs who are capable of aligning
owners' wealth and maximizing corporate goals, and those assigned CEOs make a decision
which maximizes shareholder's wealth through a measure like Compensation policy (Jensen
(1990); Jensen and Meckling (1976 )). Therefore, CEOs make decisions of corporate strategies,
and they have a strong incentive to maximize firm performance to secure their position for a
long time (Bebchuk and Stole (1993 )).
In addition to secure their position, a firmβs strategic choices are affected by upper
echelon characteristics, such as functional track, former education background, and other
career related experiences (Hambrick and Mason (1984 )). By the UET (Hambrick and Mason
(1984 )), performance of firm is influenced by strategic choices followed by managerial
background characteristics. Furthermore, firm performance is influenced by strategic choices
followed by managerial background characteristics. Therefore, depending on their
characteristics, CEOs may decide the) number of acquisitions, investment ratio, investment
ratio, and cash holding ratio (Hambrick and Mason (1984); Zacharias, Six, Schiereck and Stock
(2015 )).
According to prior research based on international survey, CEO's personal
characteristics accounts for a great part of factors which influence corporate financial policy
like cash policy (Lins, Servaes and Tufano (2010 )). In addition, Previous literature show that
various characteristics of CEOs influence financial policy (Barker III and Mueller (2002);
Bertrand and Schoar (2002); Coles, Daniel and Naveen (2006); CustΓ³dio and Metzger (2014);
Gottesman and Morey (2010); Lins, Servaes and Tufano (2010); Malmendier and Tate (2005);
Manner (2010); Orens and Reheul (2013); Xie (2015); Zacharias, Six, Schiereck and Stock
(2015 )). Among studies regarding the relation between cash holding and CEOs' characteristics,
most of the previous studies observe how much cash is accumulated as a perspective of quantity
instead of value of cash as a perspective of quality (Bertrand and Schoar (2002); CustΓ³dio and
Metzger (2014); Orens and Reheul (2013 )).
2.2 CEO and cash holding
Cash is one of the most important assets when CEO decides corporate strategy and
financial policy. The benefits and costs of cash holdings are widely explained by previous
literature. By the study of Myers and Majluf (1984), cash holdings can be a buffer to invest, so
firms can reduce the cost of external financing. Moreover, Opler et al. (1999) show that firms
with cash holdings is able to prepare unexpected events. In spite of those benefits of holding
cash, there exist some costs. Cash holdings without any investment arises opportunity costs.
Another cost of cash holdings is agency cost by Jensen (1986). CEO and managers may use
cash holdings in firms to their private benefits, not for the maximization of shareholderβs wealth.
Therefore, excess cash holdings are easily target of agency costs by managers, and invested in
non-profitable projects. Therefore, shareholders have a great interest in excess cash holdings
in the firms.
Recently, lots of studies find the link between cash holdings policy and the effect of
manager. Literature on corporate governance and value of excess cash find that corporate
governance has a positive impact on the value of excess cash (Dittmar and Mahrt-Smith (2007);
Lee and Lee (2009 )). Dittmar and Duchin (2016) find that the effects of professional
experiences of CEO on the corporate debt and cash policies. Huang-Meier et al. (2015) show
examine the optimism of CEO have significantly affect cash holding policies. The amount of
excess cash influenced from CEO characteristics could be invested on other usage positively
by shareholders, but it brings opportunity cost to retain cash. After all, this causes a problem to
depreciate the shareholder value (Orens and Reheul (2013 )).
While previous literature shows the relation between corporate governance,
professional experiences, CEO optimism, age, and tenure. Some previous studies find the link
between CEO characteristics and the amount of cash holdings (Orens and Reheul (2013 )),
2013), but our study find the relation between CEO characteristics and the value of excess cash
from the view of shareholders in depth. Previous literature measure the value of excess cash
holdings by using modified model of (Fama and French (1998 )). The value of excess cash is
the contribution of excess cash holdings on the firm value in the view of stock market investors.
Therefore, in this study, we examine the value of excess cash in connection with CEO
characteristics.
2.3.1 CEO education background
Lots of previous studies examine the relationship between CEO educational
background and financial decisions. In previous studies, the impact of CEO education
background on financial choices, such as major and education level, is somewhat obscure and
remains controversial.
Malmendier and Tate (2005)) empirically show that corporate financial policy is
influenced by CEOsβ major such as Finance, Science and Engineering. More specifically,
Manner (2010) insists that CEOs with humanities background tend to proactively invest in
Corporate Social Performance (CSP) rather than CEOs with economics. CEOs with science
and engineering major tend to invest in and place more weight on R&D investment
opportunities than CEOs with other major backgrounds (Tyler and Steensma (1998 )). On the
other hand, CEOs with business major or MBA degree tend to risk-averse when they operate
their companies conservatively for the stable operation (Barker III and Mueller (2002 )). On
the contrary to this, Gottesman and Morey (2010) argues that acquisition of MBA degree have
not influence on the firm financial performance.
CEOs with higher education levels are capable of dealing with technological
innovation and they are more willing to adopt innovation (Bantel and Jackson (1989 )). Barker
III and Mueller (2002) affirm that highly educated CEOs are less risk averse, and tend to accept
new ideas, innovative changes, and investment opportunities. In addition, Orens and Reheul
(2013) show that the education level of a CEO is reflected in the firmβs strategic choices. Jalbert,
Rao and Jalbert (2002) insist that there is positive association between CEO education level
and firm performance.
In this study, we examine how CEOβs major s and education levels affect value of cash,
in the view of managerial ability in cash management, in the Korean stock market. Following
the Malmendier and Tate (2005)βs classification of major, we separate major into three
categories (Business, Science and Engineering, and others). Also, we divide education level
into three degree (Bachelorβs, Masterβs, and Doctorβs) and propose hypotheses to check the
effect of each characteristic. In particular, we hypothesize as follows.
Hypothesis 1: There is a positive relationship between CEOs with business background
and the value of excess cash
Hypothesis 2: There is a negative relationship between CEOs with Science and
Engineering background and the value of excess cash.
Hypothesis 3: There is a positive relationship between CEOs with high education level and
the value of excess cash.
Hypothesis 4: There is a positive relationship between CEOs with MBA degree and the
value of excess cash.
2.3.2 CEO age and tenure
Previous studies suggest that in terms of career concerns, there are two controversial
views about the impact of CEO age and whether the CEO is newly appointed on financial
decisions. On the one hand, from the perspective of short-term career concerns (Prendergast
and Stole (1996); Xie (2015 )), younger or newly appointed CEOs tend to invest more
aggressively because they want to be recognized by showing their ability and performance in
the short term. Therefore, they are willing to develop new products and take risks in bold new
investment. On the other hand, from the perspective of long-term career concerns, younger or
newly appointed CEOs behave more cautiously because they strive hard to keep their positions
and built their reputations for the future (Xie (2015 )).
In previous studies, the impact of CEO age and tenure differs by country and is
ambiguous. We are concerned with which CEO career concern (long-term or short-term) is
appropriate for financial decisions in the Korean stock market. Thus, we propose the following
hypotheses.
Hypothesis 5: There is a positive relationship between younger CEOs and the value of
excess cash.
Hypothesis 6: There is a positive relationship between newly appointed CEOs and the
value of excess cash
Meanwhile, CEO tenure plays a significant role in decision making, especially in
financial choices. There are opposing views in the debate about the impact of CEO tenure.
Based on the UET, CEOs with longer tenure become more confident in their tasks and take
financial decisions that are more challenging (Orens and Reheul (2013 )).
On the other hand, Coles et al. (2006) insist that CEOs with longer tenure have a
tendency to pay more dividends than to invest in R&D projects because of their risk aversion,
with the aim of retaining their status. Thus, CEOs with longer tenure pursue stability rather
than R&D. We wonder which opinion is more reasonable in the South Korean stock market.
Hence, we set the following hypothesis.
Hypothesis 7: There is a negative relationship between CEOs with longer tenure and the
value of excess cash
3. Data and Empirical Settings
3.1 Data.
Financial data for empirical analysis is collected from Dataguide and TS2000 database.
Dataguide and TS2000 are electronic database system that provide financial data of Korean
listed firms. CEO characteristics data is collected by hand from Data Analysis, Retrieval and
Transfer System (DART). The sample period is from 2005 to 2013. We exclude the financial
institutions and utility firms because the cash holding policies of these industries are
significantly affected by regulation and law. Moreover, we exclude missing values in financial
variables. In our sample, the number of observation is 2,574, and the number of firms is 534.
Table 1 reports the descriptive statistics of the variables used in our analysis. It shows
that a high proportion of CEOs of Korean firms majored in business (39.90%) or the sciences
and engineering (33.08%). By focusing on this phenomenon of the existence of two major
academic fields, CEOsβ majors were segmented into (1) business, (2) science, and engineering,
and (3) other. We assigned each major dummy variable a value of 1 if the CEO majored in
business or sciences and engineering, and 0 otherwise. Likewise, the ratio of CEOs with
masterβs degrees was only 25.70% and that of CEOs with doctoral degrees was 10.73%. Lastly,
we assigned each education-level dummy variable a value of 1 if CEOs have masterβs or
doctoral degrees, and 0 otherwise.
To measure CEO characteristics related to careers, the age of a CEO was calculated as
the value of the difference between the birth year of the CEO and the current fiscal year. Newly
appointed CEOs was a dummy variable with a value of 1 if the CEO was appointed in that
fiscal year. The CEOβs tenure was calculated using the value of the difference between the first
year that the CEO was appointed and the current fiscal year. We employ 1-year lagged values
for CEO characteristics in order to consider the time lag between the effect of his/her influence
and value of excess cash
[Insert Table 1 here]
[Insert Table 2 here]
3.2 Methodology
3.2.1 Measuring the amount of excess cash
In all the regression models in this paper, we follow the variable of excess cash
retrieved from the normal cash regression of (Bates, Kahle and Stulz (2009 )). In the first stage,
we calculate the normal level of cash holdings for a firm by the normal cash regression by
using 2SLS model. In the second stage, the excess cash is defined as the amount of cash
holdings that exceeds the normal level. The normal cash regression model used in our analysis
is shown in the following equation (1).
πΆπΆπΆπΆπΆπΆβππ,π‘π‘ππππππ,π‘π‘
= π½π½0 + π½π½1πΏπΏπΏπΏοΏ½ππππππ,π‘π‘οΏ½ + π½π½2πΆπΆπΆπΆππ,π‘π‘ππππππ,π‘π‘
+ π½π½3πππππΆπΆππ,π‘π‘ππππππ,π‘π‘
+ π½π½4πΆπΆπΆπΆπΆπΆππ,π‘π‘ππππππ,π‘π‘
+ π½π½5πΏπΏπΏπΏπΏπΏππ,π‘π‘
+ π½π½6πΌπΌπΏπΏπΌπΌπΌπΌπΆπΆπΌπΌπΌπΌπΌπΌ πΌπΌπππΆπΆππππ,π‘π‘ + π½π½7π·π·πππΏπΏ πΌπΌπΌπΌπππππΌπΌππ,π‘π‘ + π½π½8π π &π·π·ππ,π‘π‘ππππππ,π‘π‘
+ π½π½9πππππππ€π€,π‘π‘οΏ½ + πππΆπΆππ + πΆπΆπΆπΆππ + ππit
(1)
where Cash is cash and cash equivalent, TA is total assets, CF is operating cash flow to the
firm, NWC is net working capital that is the current assets minus current liabilities, CPX is
capital expenditure that is the difference of property, plant, and equipment from year t-1 to year
t, Lev is leverage that is total liabilities divided by total equity, Industry risk is the median
industry standard deviation of cash flows during the past ten years. Div dummy is one if a firm
paid dividends in year t, otherwise 0. R&D is research and development expense of a firm in
year t. YFE is year fixed effect and FEF is firm fixed effect.
To solve the endogeneity issue emerging from the relation between market to book
ratio and cash holdings, we use the two stage (2SLS) regressions. In the first stage, we predict
MTB by using 2 year lagged sales growth rate as an instrument variable, which is used in
Dittmar and Mahrt-Smith (2007). Moreover, when calculating excess cash level, we consider
firm specific effect as a part of excess cash holdings, by following the idea of Dittmar and
Mahrt-Smith (2007) and Schauten et al. (2013). After that, the excess cash level is estimated
by πΆπΆπΆπΆππ + ππit from the equation (1). The result of the normal cash regression is explained in
Appendix B.
3.2.2 Value regression model
We examine the effect of excess cash on the firm value to test our hypothesis by using
the value regression model specified in equation (2) below. Value regression model is
developed by Fama and French (1998), and we mainly follow the modified model of Pinkowitz
et al. (2006).
ππππππ,π‘π‘ππππππ,π‘π‘
= π½π½0 + π½π½1πΆπΆπππΆπΆπΆπΆβππ,π‘π‘ππππππ,π‘π‘
+ π½π½2ππππ,π‘π‘ππππππ,π‘π‘
+ π½π½3πΌπΌππππ,π‘π‘ππππππ,π‘π‘
+ π½π½4πΌπΌππππ,π‘π‘+2ππππππ,π‘π‘
+ π½π½5πΌπΌππππππ,π‘π‘ππππππ,π‘π‘
+ π½π½6πΌπΌππππππ,π‘π‘+2ππππππ,π‘π‘
+ π½π½7π π π·π·ππ,π‘π‘ππππππ,π‘π‘
+ π½π½8πΌπΌπ π π·π·ππ,π‘π‘ππππππ,π‘π‘
+ π½π½9πΌπΌπ π π·π·ππ,π‘π‘+2ππππππ,π‘π‘
+ π½π½10πΌπΌππ,π‘π‘ππππππ,π‘π‘
+ π½π½11πΌπΌπΌπΌππ,π‘π‘ππππππ,π‘π‘
+ π½π½12πΌπΌπΌπΌππ,π‘π‘+2ππππππ,π‘π‘
+ π½π½13π·π·πΌπΌππππ,π‘π‘ππππππ,π‘π‘
+ π½π½14πΌπΌπ·π·πΌπΌππππ,π‘π‘ππππππ,π‘π‘
+ π½π½15πΌπΌπ·π·πΌπΌππππ,π‘π‘+2ππππππ,π‘π‘
+ π½π½16πΌπΌππππππ,π‘π‘+2ππππππ,π‘π‘
+ π½π½17πΆπΆπΆπΆπππΌπΌπΉπΉπΌπΌππ,π‘π‘ ΓπΆπΆπππΆπΆπΆπΆβππ,π‘π‘ππππππ,π‘π‘
+ πππΆπΆππ
+ πΌπΌπΆπΆππ + πππππ‘π‘
(2)
where Xcash is excess cash which is retrieved from the normal cash regression model in the
subsection 4.1. MV is the market capitalization plus book value of liabilities. E is earnings
before interest and tax (EBIT). NA is net asset, which is total asset minus cash and cash
equivalent. RD is R&D expenditure and I is interest expense. DIV is common cash dividend
payout. πΌπΌπΆπΆππ,π‘π‘ is compact notation of 2 year change, time t and t-2 πΌπΌπΆπΆππ,π‘π‘_2 is compact notation
for 2 year change, time t+2 and t. πΆπΆπΆπΆπππΌπΌπΉπΉπΌπΌππ,π‘π‘ is our main variable such as chaebol, control right,
disparity, and corporate governance. YFE is year fixed effect, and IFE is industry fixed effect.
We add CEO characteristics variables as πΆπΆπΆπΆπππΌπΌπΉπΉπΌπΌππ,π‘π‘, and we mainly focus on the coefficient of
interaction term between πΆπΆπΆπΆπππΌπΌπΉπΉπΌπΌππ,π‘π‘ and πΆπΆπππΆπΆπΆπΆβππ,π‘π‘ (π½π½17).
4. Empirical Results
We empirically examined the relationship between CEO characteristics and the value
of excess cash of the company using recent samples in Korea. In Table 3, we examined the
effect of CEOβs majors on the value of excess cash. Model (1) of Table 3 only contains sample
with Business majored CEO. In Model (1), we found that the coefficient of excess cash
(Xcash/NA) is 4.415 at 1% significance level. Coefficient of excess cash means the
contribution of excess cash on the firm value, and it means that investors in the stock market
evaluate the value of excess cash as 4.415 in case of business majored CEO. In Model (2) of
Table 3, we only examined samples with non-business majored CEO. The coefficient of
Xcash/NA is 3.293 that is lower than the coefficient of business majored CEO sample. In Model
(3) of Table 3, we added interaction term of Xcash/NA and Business major, and the coefficient
is 0.858 at 1% significance level. This result means the value of excess cash is high when the
CEO has a business major. This result supports our hypothesis 1. This result supports the
anecdotal evidence and previous studies of Barker III and Mueller (2002). CEOs with business
background tend to operate company more stably based on their risk-averse attitude.
Model (4) only contains sample with science and engineering majored CEO. The
coefficient of Xcash/NA is 3.472 at 1% significance level. Model (5) only contains samples
with non-science and engineering majored CEO. The coefficient of Xcash/NA is 3.815 at 1%
significance level. To find out the effect of science and engineering majored CEO, we added
interaction term between Xcash/NA and SCI_ENG dummy in Model (6) of Table 3. The
coefficient of interaction term is -0.012, and not significant. This result means that science and
engineering majored CEOs have no effect on the value of excess cash. This result could not
support our hypothesis 2.
[Insert Table 3 here]
In Table 4, we examined the effect of educational background of CEO. We added MBA
dummy, Master dummy, Doctor dummy in our regression model. Model (1) only contains
samples with MBA CEO, and the coefficient of Xcash/NA is 4.684 at 1% significance level.
Model (2) only contains none MBA CEO, the coefficient of Xcash/NA is 3.571 at 1%
significance level. The coefficient of Xcash/NA is significantly higher in CEOs with MBA
degree. To find out the effect of MBA educational background of CEO, we added interaction
term between Xcash/NA and MBA dummy. The coefficient of interaction term is 1.008 at 5%
significance level in Model (3). This result supports our hypothesis 3 that investors in the
market think that MBA CEOs have a better ability to manage excess cash.
We also investigated the effect of master degree achieved CEOs. In Model (4), we only
examined the value of excess cash in master degree achieved CEO samples. The coefficient of
Xcash/NA is 4.897 at 1% significance level. Model (5) only contains non-master degree CEO
samples. The coefficient of Xcash/NA is 3.411 at 1% significance level. In Model (6), we added
interaction term between Xcash/NA and Master dummy, and coefficient is 1.117 at 1%
significance level. The coefficient of Xcash/NA is higher in master degree achieved CEO
samples, and this result also means that master degree achieved CEOs have a superior ability
to manage excess cash in the view of stock investors. These results are consistent with the
previous research of Jalbert, Rao and Jalbert (2002), and it appears that CEOs with higher
education levels tend to appropriately deal with good investment opportunities.
Model (7) contains CEOs with doctoral degree, and coefficient of Xcash/NA is 3.130
at 1% significance level. Model (8) only contains non-doctoral degree CEOs, and coefficient
of Xcash/NA is 3.756 at 1% significance level. The coefficients of doctoral degree achieved
CEO samples and non-doctoral degree CEO samples have no significant difference. In model
(9), we added interaction term between Xcash/NA and Doctor dummy, but coefficient is not
significant (-0.174). We found that when CEOs have excessively higher education levels, the
effect of the increase in value of cash disappears. This result means that stock investors think
that CEOs with doctoral degree have no better ability to manage excess cash than other CEOs
without doctoral degree.
[Insert Table 4 here]
We also examined the effect of CEO age, tenure, and newly appointment. In Model (1)
of Table 5, we added interaction term between Xcash/NA and CEO age. The coefficient is -
0.0406 at 5% significance level. This result means CEOsβ age has a negative impact on the
value of excess cash in the view of stock investors. From a perspective of short-term career
concerns (Prendergast and Stole (1996); Xie (2015 )), younger CEOs who invest aggressively
appear to be managing excess cash well to stock investors.
In Model (2) of Table 5, we find that tenure has a negative impact on the value of
excess cash because the coefficient of interaction term between Xcash/NA and CEO tenure is
-0.0496 at 1% significance level. As CEOsβ tenure lasts longer, the value of excess cash
accumulated within the company itself gets worse. CEOs who are older and have longer tenure
periods retain much cash for running companies stably based on the studies from Orens and
Reheul (2013), Bertrand and Schoar (2002), and this result can be interpreted that stock
investors may think those CEOs do not manage cash well.
Moreover, we investigate the effect of newly appointed CEOs. In Model (3) of Table
5, we find that the coefficient of Xcash/NA is 4.295 at 1% significance level in the sample of
newly appointed CEOs. In Model (4) of Table 5, we found that the coefficient of Xcash/NA is
3.570 at 1% significance level. We added interaction term between Xcash/NA and Newly
appointment dummy, and coefficient is 0.592 at 10% significance level. Likewise, newly
appointed CEOs seems to be well evaluated by stock investors for the same reasons as younger
CEOs based on short-term career concerns (Prendergast and Stole (1996); Xie (2015 )).
[Insert Table 5 here]
Anecdotal evidence shows that the performance of business majored CEOs are better
than non-business majored CEOs during the financial crisis in 2008 because they have specialty
in risk management and how to cope with the crisis by their managerial skills. This result
supports the anecdotal evidence which explains why companies prefer CEOs having business
background during crisis period. Therefore, we separated our additional sample within crisis
period, then tested the effect of CEO characteristics on the value of excess cash. We only
examined samples from 2008 to 2009 to find out the effect of CEO characteristics on the value
of excess cash during the financial crisis. In Model (1) of Table 6, we found that business
majored CEOs manage excess cash well, so the value of excess cash is higher than that of non-
business majored CEO samples. The coefficient of interaction term between Xcash/NA and
business dummy is 1.369 at 1% significance level. In contrast, science and engineering majored
CEOs have a negative impact on the value of excess cash during the crisis, but the coefficient
is not significant. Moreover, MBA achieved CEOs have also a positive impact on the value of
excess cash. In Model (3), the coefficient of interaction term between Xcash/NA and MBA
CEO is 2.458 at 1% significance level. This result means that the ability of management skills
of MBA achieved CEOs is better than other CEOs without MBA during the crisis. Master
degree achieved CEOs and Doctoral degree achieved CEOs have no significant impact on the
value of excess cash during the crisis in the result of Model (4) and Model (5). In addition, age
and tenure have no significant impact on the value of excess cash during the crisis in Model (6)
and Model (7). However, newly appointed CEOs have a positive impact on the value of excess
cash during the crisis. The coefficient of interaction term between Xcash/NA and Newly
appointment dummy is 1.142 at 5% significance level. Newly appointed CEOs manage excess
cash well during the crisis, and stock investors positively evaluate the newly appointed CEOs
during the crisis.
5. Discussion and Limitations
This study has some limitations, especially in terms of robustness. We used only value
of excess cash as a measure of managerial ability. To improve robustness, we need to add other
proxies for managerial ability. Although this study considered a 1-year time lag effect between
CEO characteristics and the value of excess cash, other time lag effects should be considered
in further studies to reflect various revelation of CEO characteristics on corporate strategy.
This paper adds empirical evidence to the literature that examine link between cash
holdings and CEO characteristics. As mentioned in the study of Dittmar and Mahrt-Smith
(2007), there is unexplained part of cash holdings in spite of controlling year and industry effect.
From the line of literature review on CEO characteristics, we suggest that CEO characteristics
have a relation with cash holdings, specifically the value of excess cash. Therefore, our study
is the first to fill the research gap between the performance of cash policy and CEO
characteristics. Also, we used a more detailed classification of education background, such as
masterβs degree and business major, as this classification could largely explain CEOsβ
education backgrounds in Korea. In addition, this study try to include data of all companies
listed on the KOSPI. Furthermore, through this data, we attempted to reveal the relationship
between each CEO characteristic and the value of excess cash.
6. Conclusion
Orens and Reheul (2013) investigate the relationship between the amount of cash
holdings and CEO characteristics. They suggest implication to shareholders that CEOsβ
characteristics can be interpreted as another investment factor since the characteristics of CEOs
have influence on the degree of corporate cash holdings. Because, we examined the impact of
CEO characteristics on the value of cash by focusing on the managerial ability rather than the
amount of cash holding, we could provide another implication of qualitative association
between CEO characteristics and the value of cash to shareholders.
We looked at the relationship between various CEO characteristics and the value of
excess cash that stock investors evaluate from a perspective of shareholder value. In summary,
firms with CEOs majored in business or with MBA degree have a higher value of excess with
stable operations and better understanding of company management. In addition, as the value
of excess cash of firms with a master degree CEOs is higher than that of firms with other degree
CEOs, we confirm that there is a significant association between education level and the value
of excess cash.
In CEO career related variables, younger, shorter tenured, and newly appointed CEOs
have positively influence on the value of excess cash. Younger or newly appointed CEOs seem
to appeal to stock investors as they are considered to be using cash more effectively through
aggressive investment and challenging management policies (Prendergast and Stole (1996);
Xie (2015)), compared to older CEOs who are willing to retain cash for stability (Orens and
Reheul (2013)).
References
Bantel, Karen A, and Susan E Jackson, 1989, Top management and innovations in banking: Does the composition
of the top team make a difference?, Strategic Management Journal 10, 107-124.
Barker III, Vincent L, and George C Mueller, 2002, Ceo characteristics and firm r&d spending, Management
Science 48, 782-801.
Bates, Thomas W, Kathleen M Kahle, and RenΓ© M Stulz, 2009, Why do us firms hold so much more cash than
they used to?, The journal of finance 64, 1985-2021.
Bebchuk, Lucian Arye, and Lars A Stole, 1993, Do shortβterm objectives lead to underβor overinvestment in longβ
term projects?, The Journal of Finance 48, 719-729.
Bertrand, Marianne, and Antoinette Schoar, 2002, Managing with style: The effect of managers on firm policies.
Coles, Jeffrey L, Naveen D Daniel, and Lalitha Naveen, 2006, Managerial incentives and risk-taking, Journal of
financial Economics 79, 431-468.
CustΓ³dio, ClΓ‘udia, and Daniel Metzger, 2014, Financial expert ceos: CeoΧ³ s work experience and firmΧ³ s financial
policies, Journal of Financial Economics 114, 125-154.
Dittmar, Amy, and Ran Duchin, 2016, Looking in the rearview mirror: The effect of managers' professional
experience on corporate financial policy, Review of Financial Studies 29, 565-602.
Dittmar, Amy, and Jan Mahrt-Smith, 2007, Corporate governance and the value of cash holdings, Journal of
financial economics 83, 599-634.
Fama, Eugene F, and Kenneth R French, 1998, Taxes, financing decisions, and firm value, The Journal of Finance
53, 819-843.
Fama, Eugene F, and Kenneth R French, 1998, Value versus growth: The international evidence, The journal of
finance 53, 1975-1999.
FrΓ©sard, Laurent, and Carolina Salva, 2010, The value of excess cash and corporate governance: Evidence from
us cross-listings, Journal of Financial Economics 98, 359-384.
Galasso, Alberto, and Timothy S Simcoe, 2011, Ceo overconfidence and innovation, Management Science 57,
1469-1484.
Gottesman, Aron A, and Matthew R Morey, 2010, Ceo educational background and firm financial performance,
Journal of Applied Finance (Formerly Financial Practice and Education) 20.
Hambrick, Donald C, and Phyllis A Mason, 1984, Upper echelons: The organization as a reflection of its top
managers, Academy of management review 9, 193-206.
Huang-Meier, Winifred, Neophytos Lambertides, and James M Steeley, 2015, Motives for corporate cash holdings:
The ceo optimism effect, Review of Quantitative Finance and Accounting 1-34.
Jalbert, Terrance, Ramesh P Rao, and Mercedes Jalbert, 2002, Does school matter? An empirical analysis of ceo
education, compensation, and firm performance, International Business and Economics Research
Journal 1, 83-98.
Jensen, Kevin B, 1990, Cytology and taxonomy of elymus kengii, e. Grandiglumis, e. Alatavicus, and e. Batalinii
(poaceae: Triticeae), Genome 33, 668-673.
Jensen, Michael C, 1986, Agency cost of free cash flow, corporate finance, and takeovers, Corporate Finance,
and Takeovers. American Economic Review 76.
Jensen, Michael C, and William H Meckling, 1976, Theory of the firm: Managerial behavior, agency costs and
ownership structure, Journal of financial economics 3, 305-360.
Kaplan, Steven N, Mark M Klebanov, and Morten Sorensen, 2012, Which ceo characteristics and abilities matter?,
The Journal of Finance 67, 973-1007.
Lee, Kin-Wai, and Cheng-Few Lee, 2009, Cash holdings, corporate governance structure and firm valuation,
Review of Pacific Basin Financial Markets and Policies 12, 475-508.
Lins, Karl V, Henri Servaes, and Peter Tufano, 2010, What drives corporate liquidity? An international survey of
cash holdings and lines of credit, Journal of financial economics 98, 160-176.
Malmendier, Ulrike, and Geoffrey Tate, 2005, Ceo overconfidence and corporate investment, The journal of
finance 60, 2661-2700.
Manner, Mikko H, 2010, The impact of ceo characteristics on corporate social performance, Journal of business
ethics 93, 53-72.
Myers, Stewart C, and Nicholas S Majluf, 1984, Corporate financing and investment decisions when firms have
information that investors do not have, Journal of financial economics 13, 187-221.
Opler, Tim, Lee Pinkowitz, RenΓ© Stulz, and Rohan Williamson, 1999, The determinants and implications of
corporate cash holdings, Journal of financial economics 52, 3-46.
Orens, Raf, and Anne-Mie Reheul, 2013, Do ceo demographics explain cash holdings in smes?, European
Management Journal 31, 549-563.
Pinkowitz, Lee, RenΓ© Stulz, and Rohan Williamson, 2006, Does the contribution of corporate cash holdings and
dividends to firm value depend on governance? A crossβcountry analysis, The Journal of Finance 61,
2725-2751.
Prendergast, Canice, and Lars Stole, 1996, Impetuous youngsters and jaded old-timers: Acquiring a reputation for
learning, Journal of political Economy 1105-1134.
Schauten, Marc BJ, Dick Van Dijk, and JanβPaul van der Waal, 2013, Corporate governance and the value of
excess cash holdings of large european firms, European Financial Management 19, 991-1016.
Tyler, Beverly B, and H Kevin Steensma, 1998, The effects of executivesβ experiences and perceptions on their
assessment of potential technological alliances, Strategic Management Journal 19, 939-965.
Weisbach, Michael S, 1995, Ceo turnover and the firm's investment decisions, Journal of Financial Economics
37, 159-188.
Xie, Jun, 2015, Ceo career concerns and investment efficiency: Evidence from china, Emerging Markets Review
24, 149-159.
Zacharias, Nicolas A, Bjoern Six, Dirk Schiereck, and Ruth Maria Stock, 2015, Ceo influences on firms' strategic
actions: A comparison of ceo-, firm-, and industry-level effects, Journal of Business Research 68, 2338-
2346.
Table 1 Description of CEO characteristic variables
Variables Mean Median Std Max Min Business 0.3990 0.0000 0.4897 1.0000 0.0000 SCI_ENG 0.3308 0.0000 0.4705 1.0000 0.0000 Masterβs 0.2570 0.0000 0.4370 1.0000 0.0000 Doctorβs 0.1073 0.0000 0.3095 1.0000 0.0000 AGE 56.7372 0.0000 8.1623 91.0000 30.0000 Newly_app 0.1721 0.0000 0.3775 1.0000 0.0000 Tenure 6.4946 4.0000 7.7396 45.0000 0.0000
Notes: This table shows description of CEO characteristic variables for the data used in our analysis. The data set is comprised of 686 firms and 3,253 firm-year observation covering 2005 to 2013in Korea listed firms. Business and SCI_ENG are dummy variables with values of 1 if the CEO majored in business or science and engineering, and 0 otherwise. Masterβs and Doctorβs are dummy variables with values of 1 if the CEO had only a masterβs degree or a doctoral degree, and 0 otherwise. Age is calculated as the value of the difference between the birth year of the CEO and the current fiscal year. Newly_App dummy has a value of 1 if the CEO was appointed in the same fiscal year, and 0 otherwise. Tenure is calculated as the value of the difference between the year when the CEO was appointed and the current fiscal year.
Table 2 Description of variables in Value regression
Variables Mean Median Std Max Min MVππ,π‘π‘/ππππππ,π‘π‘ 1.0587 0.9116 0.6367 13.0754 0.03005
Xcashππ,π‘π‘/ππππππ,π‘π‘ 0.0449 0.0345 0.0556 0.2960 β0.0780 ππππ,π‘π‘/ππππππ,π‘π‘ 0.0565 0.0527 0.0679 0.4817 β0.4868 πΌπΌππππ,π‘π‘/ππππππ,π‘π‘ β0.0048 β0.0047 0.0612 0.3614 β0.4831 πΌπΌππππ,π‘π‘+2/ππππππ,π‘π‘ β0.0056 β0.0056 0.0623 0.6426 β0.5042 πΌπΌππππππ,π‘π‘/ππππππ,π‘π‘ β0.0023 β0.0009 0.0585 0.5601 β0.5049 πΌπΌππππππ,π‘π‘+2/ππππππ,π‘π‘ 0.0007 0.0007 0.0540 0.4389 β0.3915 π π π·π·ππ,π‘π‘/ππππππ,π‘π‘ 0.0069 0.0016 0.0134 0.1513 β0.0017 πΌπΌπ π π·π·ππ ,π‘π‘/ππππππ,π‘π‘ 0.0004 0.0000 0.0068 0.0720 β0.0738 πΌπΌπ π π·π·ππ ,π‘π‘+2/ππππππ,π‘π‘ 0.0002 0.0000 0.0064 0.0671 β0.0738 πΌπΌππ,π‘π‘/ππππππ,π‘π‘ 0.0142 0.0121 0.0123 0.1403 0.0000 πΌπΌπΌπΌππ,π‘π‘/ππππππ,π‘π‘ β0.0007 β0.0001 0.0104 0.1086 β0.1286 πΌπΌπΌπΌππ,π‘π‘+2/ππππππ,π‘π‘ 0.0000 β0.0001 0.0096 0.1185 β0.1286 π·π·πΌπΌππππ ,π‘π‘/ππππππ,π‘π‘ 0.0080 0.0051 0.0107 0.1317 0.0000 πΌπΌπ·π·πΌπΌππππ,π‘π‘/ππππππ,π‘π‘ β0.0004 0.0000 0.0082 0.1003 β0.1150 πΌπΌπ·π·πΌπΌππππ,π‘π‘+2/ππππππ,π‘π‘ β0.0009 β0.0001 0.0076 0.0915 β0.1150 πΌπΌππππππ,π‘π‘+2/ππππππ,π‘π‘ β0.0289 β0.0188 0.4491 4.6845 β 7.0017
Notes: This table shows description of variables for the data used in Value regression. The data set is comprised of 686 firms and 2,986 firm-year observation covering 2005 to 2013in Korea listed firms. The definitions of each variables are explained in Appendix A. Xcash is excess cash that was driven through the normal cash regression model. CGSCORE is corporate governance score. Disparity is the ratio of cash flow right over control right. Control right is direct ownership of controlling shareholders. πΌπΌπΆπΆππ,π‘π‘ is compact notation of 2 year change, time t and t-2 πΌπΌπΆπΆππ,π‘π‘+2 is compact notation for 2 year change, time t+2 and t.
Table 3. OLS Regression Results for Value regression of Excess cash and CEO major
This table examines the effect of the CEO major on the value of excess cash. Variables related to the CEOβs major are 1-year lagged. Bussiness is a dummy variable if the major of CEO is business, otherwise 0.SCI_ENG a dummy variable if the major of CEO is science or engineering, otherwise 0.Other control variables are same as in Table 3. Details about variables are explained in Appendix A. All models include observations only if Xcash is positive. Year effects and industry effects are fixed. VIFs of all variables are less than 10. The ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels respectively.
Variable (MV) (1)Bussiness (2)None Bussiness
(3)Pooled (4)SCI_ENG (5) None SCI_ENG
(6) Pooled
Xcash/NA 4.415*** (20.36)
3.293*** (15.93)
3.377*** (18.31)
3.472*** (13.87)
3.815*** (20.25)
3.717*** (21.24)
Bussiness -0.022 (-1.20)
Xcash/NA * Bussiness
0.858*** (3.28)
SCI_ENG -0.007 (-0.34)
Xcash/NA * SCI_ENG
-0.012 (-0.04)
ππππ,π‘π‘/NA 1.841*** (7.03)
3.110*** (13.24)
2.595*** (14.67)
1.802*** (6.03)
2.963*** (13.40)
2.619*** (14.73)
πΌπΌππππ,π‘π‘/NA 0.127 (0.56)
-0.263 (-1.38)
-0.095 (-0.64)
0.363 (1.43)
-0.336* (-1.84)
-0.117 (-0.79)
πΌπΌππππ,π‘π‘+2/NA 1.087*** (4.56)
1.042*** (5.22)
1.026*** (6.64)
0.712*** (2.80)
1.181*** (6.07)
1.022*** (6.58)
πΌπΌππππππ,π‘π‘/NA 1.721*** (7.75)
1.014*** (5.45)
1.289*** (8.95)
1.501*** (6.00)
1.174*** (6.65)
1.263*** (8.76)
πΌπΌππππππ,π‘π‘+2/NA -1.332*** (-5.55)
-1.222*** (-5.73)
-1.274*** (-7.87)
-0.627** (-2.11)
-1.501*** (-7.76)
-1.297*** (-8.01)
π π π·π·ππ,π‘π‘/NA 5.597*** (4.25)
5.900*** (6.34)
6.215*** (8.21)
10.238*** (9.71)
3.241*** (2.95)
6.192*** (8.08)
πΌπΌπ π π·π·ππ ,π‘π‘/NA 4.646** (2.23)
-5.481*** (-3.33)
-2.690** (-2.07)
-8.909*** (-4.92)
1.428 (0.79)
-2.823** (-2.17)
πΌπΌπ π π·π·ππ ,π‘π‘+2/NA 5.091** (2.38)
2.296 (1.43)
3.391*** (2.63)
5.202*** (3.03)
1.748 (0.93)
3.506*** (2.71)
πΌπΌππ,π‘π‘/NA 7.938*** (7.24)
10.269*** (10.70)
9.077*** (12.43)
9.939*** (7.84)
8.638*** (9.63)
9.093*** (12.42)
πΌπΌπΌπΌππ,π‘π‘/NA -1.376 (-1.17)
-2.192** (-2.09)
-1.590** (-2.01)
-3.522*** (-2.71)
-0.766 (-0.76)
-1.653** (-2.08)
πΌπΌπΌπΌππ,π‘π‘+2/NA 6.253*** (4.38)
5.659*** (4.87)
6.013*** (6.63)
4.701*** (2.98)
6.196*** (5.57)
6.055*** (6.65)
π·π·πΌπΌππππ ,π‘π‘/NA 9.627*** (5.50)
4.910*** (3.59)
5.969*** (5.54)
8.480*** (5.35)
4.050*** (2.77)
5.829*** (5.39)
πΌπΌπ·π·πΌπΌππππ,π‘π‘/NA 0.758 (0.38)
0.268 (0.20)
0.719 (0.66)
-1.541 (-0.95)
1.656 (1.13)
0.809 (0.74)
πΌπΌπ·π·πΌπΌππππ,π‘π‘+2/NA 7.603*** (3.97)
1.749 (1.09)
3.329*** (2.69)
3.576** (2.21)
2.354 (1.29)
3.295*** (2.65)
πΌπΌππππππ,π‘π‘+2/NA -0.582*** (-18.45)
-0.366*** (-15.45)
-0.434*** (-23.04)
-0.373*** (-12.34)
-0.468*** (-19.36)
-0.434*** (-22.96)
Year Fixed Fixed Fixed Fixed Fixed Fixed Industry Fixed Fixed Fixed Fixed Fixed Fixed Number of observations
1048 1526 2574 827 1747 2574
Adjusted π π 2 0.5998 0.4723 0.5061 0.5349 0.5020 0.5038
Table 4. OLS Regression Results for Value regression of Excess cash and Educational Background
The dependent variable is market value of the firm divided by net assets. Xcash is excess cash which was driven through the normal cash regression model. Variables related to the CEOβs education level are 1-year lagged. MBA is a dummy variable if CEO graduate MBA, otherwise 0. Master is a dummy variable if CEO has masterβs degree, otherwise 0. Doctor is a dummy variable if CEO has doctorβs degree. Details about variables are explained in Appendix A. Other control variables are same as in Table 3. All models include observations only if Xcash is positive. Year effects and industry effects are fixed. VIFs of all variables are less than 10. The ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels respectively.
Variable (MV)
(1)MBA (2)None MBA
(3) Pooled (4)Master (5) None Master
(6) Pooled (7) Doctor (8) None Doctor
(9) Pooled
Xcash/NA 4.684*** (13.16)
3.571*** (22.09)
3.630*** (23.11)
4.897*** (16.98)
3.411*** (20.19)
3.418*** (20.06)
3.130*** (6.445)
3.756*** (23.70)
3.724*** (23.99)
MBA -0.015 (-0.45)
Xcash/NA* MBA 1.008** (2.08)
Master -0.0108 (-0.527)
Xcash/NA * Master 1.117*** (3.865)
Doctor -0.00872 (-0.255)
Xcash/NA * Doctor -0.174 (-0.341)
ππππ,π‘π‘/NA 0.319 (0.67)
2.779*** (14.83)
2.634*** (14.86)
2.685*** (6.793)
2.205*** (11.60)
2.595*** (14.69)
-0.00463 (-0.00972)
2.899*** (15.35)
2.613*** (14.73)
πΌπΌππππ,π‘π‘/NA 0.524 (1.43)
-0.179 (-1.15)
-0.128 (-0.87)
-0.162 (-0.545)
0.0631 (0.392)
-0.0951 (-0.647)
0.385 (0.899)
-0.237 (-1.524)
-0.119 (-0.803)
πΌπΌππππ,π‘π‘+2/NA 0.393 (1.04)
1.046*** (6.34)
1.021*** (6.60)
0.912*** (2.812)
1.042*** (6.249)
1.034*** (6.696)
0.110 (0.229)
1.094*** (6.723)
1.018*** (6.561)
πΌπΌππππππ,π‘π‘/NA 1.729*** (4.53)
1.179*** (7.76)
1.255*** (8.71)
2.550*** (8.230)
0.881*** (5.712)
1.272*** (8.856)
0.901*** (2.786)
1.283*** (8.239)
1.266*** (8.782)
πΌπΌππππππ,π‘π‘+2/NA -2.118*** (-4.73)
-1.236*** (-7.25)
-1.280*** (-7.90)
-0.688** (-2.103)
-1.422*** (-8.078)
-1.271*** (-7.869)
-0.942** (-1.992)
-1.356*** (-7.996)
-1.292*** (-7.977)
π π π·π·ππ,π‘π‘/NA 6.699*** (3.42)
5.727*** (7.16)
6.077*** (8.03)
10.40*** (7.027)
4.751*** (5.680)
6.199*** (8.217)
3.246** (2.179)
6.753*** (7.899)
6.230*** (8.120)
πΌπΌπ π π·π·ππ ,π‘π‘/NA 4.766 (1.63)
-3.429** (-2.47)
-2.817** (-2.17)
-9.733*** (-3.952)
1.142 (0.788)
-2.803** (-2.166)
7.293** (2.538)
-4.083*** (-2.896)
-2.859** (-2.199)
πΌπΌπ π π·π·ππ ,π‘π‘+2/NA 3.109 (0.92)
3.398** (2.49)
3.381*** (2.62)
-0.292 (-0.119)
4.657*** (3.242)
3.408*** (2.647)
5.416** (2.181)
2.984** (2.079)
3.515*** (2.716)
πΌπΌππ,π‘π‘/NA 8.537*** (4.75)
9.216*** (11.81)
9.189*** (12.54)
3.792*** (2.620)
10.11*** (12.59)
9.009*** (12.34)
7.212*** (3.588)
9.330*** (12.09)
9.111*** (12.43)
πΌπΌπΌπΌππ,π‘π‘/NA -0.618 (-0.28)
-1.567* (-1.87)
-1.613** (-2.03)
2.128 (1.252)
-2.574*** (-2.997)
-1.694** (-2.143)
-2.112 (-1.226)
-1.705* (-1.957)
-1.685** (-2.123)
πΌπΌπΌπΌππ,π‘π‘+2/NA 1.236 (0.56)
6.185*** (6.43)
6.082*** (6.69)
0.807 (0.436)
7.252*** (7.357)
5.938*** (6.554)
4.999** (2.081)
5.463*** (5.661)
6.045*** (6.649)
π·π·πΌπΌππππ ,π‘π‘/NA 3.398 (0.83)
5.966*** (5.29)
5.937*** (5.50)
2.860 (1.383)
6.998*** (5.744)
5.554*** (5.164)
9.605** (2.005)
5.827*** (5.194)
5.778*** (5.353)
πΌπΌπ·π·πΌπΌππππ,π‘π‘/NA -3.516 (-1.08)
1.154 (1.00)
0.777 (0.71)
0.893 (0.486)
0.409 (0.315)
0.839 (0.767)
-8.858* (-1.913)
1.512 (1.338)
0.856 (0.778)
πΌπΌπ·π·πΌπΌππππ,π‘π‘+2/NA 8.284*** (3.89)
2.766** (2.01)
3.276*** (2.64)
-0.345 (-0.149)
4.284*** (3.097)
3.201*** (2.592)
5.022 (1.039)
3.042** (2.328)
3.293*** (2.656)
πΌπΌππππππ,π‘π‘+2/NA -0.691*** (-13.02)
-0.419*** (-21.12)
-0.433*** (-22.95)
-0.185*** (-5.081)
-0.575*** (-26.91)
-0.433*** (-23.00)
-0.717*** (-12.81)
-0.404*** (-20.42)
-0.434*** (-22.97)
Year Fixed Fixed Fixed Fixed Fixed Fixed Fixed Fixed Fixed Industry Fixed Fixed Fixed Fixed Fixed Fixed Fixed Fixed Fixed Number of observations
214 2360 2574 650 1924 2574 250 2324 2574
Adjusted π π 2 0.7920 0.4894 0.5048 0.6151 0.5261 0.5077 0.6352 0.5102 0.5039
Table 5. OLS Regression Results for Value regression of Excess cash and Age, Tenure, Newly Appointment
The dependent variable is market value of the firm divided by net assets. Xcash is excess cash which was driven through the normal cash regression model. Variables related to the CEOβs career are 1-year lagged. Age is calculated as the value of the difference between the birth year of the CEO and the current fiscal year. The Newly_App dummy has a value of 1 if the CEO was newly appointed in the current fiscal year, and 0 otherwise. Tenure is calculated as the value of the difference between the year when the CEO was appointed and the current fiscal year. Details about variables are explained in Appendix A. Other control variables are same as in Table 3. All models include observations only if Xcash is positive. Year effects and industry effects are fixed. VIFs of all variables are less than 10. The ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels respectively.
Variable (MV)
(1) Pooled (2) Pooled (3) Newly Appointment
(4) Not Newly Appointment
(5) Pooled
Xcash/NA 5.985*** (6.589)
3.954*** (21.38)
4.295*** (10.30)
3.570*** (22.28)
3.605*** (22.45)
Age 0.000601 (0.545)
Xcash/NA* Age -0.0406** (-2.549)
Tenure -0.000805 (-0.729)
Xcash/NA * Tenure -0.0496*** (-2.938)
Newly Appointment 0.00966 (0.377)
Xcash/NA * Newly Appointment
0.592* (1.731)
ππππ,π‘π‘/NA 2.637*** (14.89)
2.628*** (14.88)
2.518*** (5.878)
2.493*** (12.94)
2.634*** (14.87)
πΌπΌππππ,π‘π‘/NA -0.125 (-0.849)
-0.120 (-0.815)
0.305 (0.780)
-0.147 (-0.942)
-0.123 (-0.836)
πΌπΌππππ,π‘π‘+2/NA 1.023*** (6.610)
1.037*** (6.710)
1.913*** (5.183)
0.710*** (4.222)
1.018*** (6.579)
πΌπΌππππππ,π‘π‘/NA 1.266*** (8.792)
1.238*** (8.602)
0.637* (1.762)
1.330*** (8.581)
1.244*** (8.631)
πΌπΌππππππ,π‘π‘+2/NA -1.287*** (-7.962)
-1.279*** (-7.914)
-2.405*** (-5.668)
-1.116*** (-6.459)
-1.303*** (-8.056)
π π π·π·ππ,π‘π‘/NA 6.054*** (8.002)
6.228*** (8.245)
5.389*** (2.693)
6.736*** (8.339)
6.167*** (8.154)
πΌπΌπ π π·π·ππ ,π‘π‘/NA -2.866** (-2.210)
-2.822** (-2.180)
-4.211 (-0.931)
-2.543* (-1.914)
-2.812** (-2.168)
πΌπΌπ π π·π·ππ ,π‘π‘+2/NA 3.341*** (2.587)
3.647*** (2.828)
3.040 (0.971)
3.602*** (2.583)
3.381*** (2.619)
πΌπΌππ,π‘π‘/NA 9.050*** (12.36)
9.054*** (12.36)
8.747*** (4.566)
9.300*** (11.89)
9.036*** (12.34)
πΌπΌπΌπΌππ,π‘π‘/NA -1.769** (-2.232)
-1.628** (-2.058)
-3.041* (-1.650)
-1.878** (-2.155)
-1.613** (-2.036)
πΌπΌπΌπΌππ,π‘π‘+2/NA 5.948*** (6.548)
6.059*** (6.686)
3.993* (1.837)
6.535*** (6.575)
6.061*** (6.674)
π·π·πΌπΌππππ ,π‘π‘/NA 5.949*** (5.521)
5.906*** (5.495)
9.335*** (3.663)
5.860*** (4.957)
5.677*** (5.263)
πΌπΌπ·π·πΌπΌππππ,π‘π‘/NA 0.715 (0.652)
0.769 (0.703)
1.334 (0.454)
0.623 (0.535)
0.894 (0.815)
πΌπΌπ·π·πΌπΌππππ,π‘π‘+2/NA 3.367*** (2.718)
3.379*** (2.734)
9.773*** (2.737)
2.896** (2.237)
3.325*** (2.686)
πΌπΌππππππ,π‘π‘+2/NA -0.433*** (-22.97)
-0.428*** (-22.64)
-0.834*** (-16.55)
-0.350*** (-17.23)
-0.433*** (-22.93)
Year Fixed Fixed Fixed Fixed Fixed Industry Fixed Fixed Fixed Fixed Fixed Number of observations
2574 2574 419 2155 2574
Adjusted π π 2 0.5054 0.5070 0.6486 0.4824 0.5052
Table 6. OLS Regression Results for Value regression of Excess cash CEO characteristics during crisis
The dependent variable is market value of the firm divided by net assets. Xcash is excess cash which was driven through the normal cash regression model. Variables related to the CEO characteristics are 1-year lagged. Bussiness is a dummy variable if the major of CEO is business, otherwise 0. SCI_ENG is a dummy variable if the major of CEO is science or engineering, and otherwise 0. MBA is a dummy variable if CEO graduate MBA, otherwise 0. Master is a dummy variable if CEO has masterβs degree, otherwise 0. Doctor is a dummy variable if CEO has doctorβs degree. Age is calculated as the value of the difference between the birth year of the CEO and the current fiscal year. The Newly_App dummy has a value of 1 if the CEO was newly appointed in the current fiscal year, and 0 otherwise. Tenure is calculated as the value of the difference between the year when the CEO was appointed and the current fiscal year. Details about variables are explained in Appendix A. Other control variables are same as in Table 3. All models include observations only if Xcash is positive. Year effects and industry effects are fixed. VIFs of all variables are less than 10. The ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels respectively.
Variable (MV)
(1) Pooled (2) Pooled (3) Pooled (4) Pooled (5) Pooled (6) Pooled (7) Pooled (8) Pooled
Xcash/NA 2.110*** (6.643)
2.870*** (9.637)
2.460*** (9.258)
2.641*** (8.920)
2.657*** (9.859)
2.628*** (7.389)
2.878*** (8.760)
2.419*** (8.659)
Business -0.0227 (-0.715)
Xcash/NA *Business 1.369*** (2.966)
SCI_ENG 0.0154 (0.437)
Xcash/NA * SCI_ENG -0.790 (-1.536)
MBA -0.0150 (-0.261)
Xcash/NA * MBA 2.458*** (2.736)
Master -0.0140 (-0.375)
Xcash/NA*Master -0.00104 (-0.00190)
Doctor 0.0437 (0.714)
Xcash/NA * Doctor
-0.302 (-0.376)
Age 0.0207 (0.649)
Xcash/NA * Age 0.0320 (0.0695)
Tenure -0.00132 (-0.697)
Xcash/NA * Tenure -0.0459 (-1.530)
Newly Appointment -0.0163 (-0.370)
Xcash/NA * Newly Appointment
1.142** (1.967)
ππππ,π‘π‘/NA 1.269*** (3.978)
1.259*** (3.892)
1.309*** (4.103)
1.260*** (3.869)
1.262*** (3.888)
1.285*** (3.967)
1.338*** (4.134)
1.413*** (4.317)
πΌπΌππππ,π‘π‘/NA 0.276 (1.130)
0.266 (1.077)
0.230 (0.947)
0.224 (0.908)
0.234 (0.948)
0.239 (0.967)
0.158 (0.639)
0.131 (0.527)
πΌπΌππππ,π‘π‘+2/NA 0.910*** (3.112)
0.908*** (3.064)
0.866*** (2.964)
0.856*** (2.859)
0.849*** (2.844)
0.878*** (2.949)
0.912*** (3.097)
0.893*** (3.014)
πΌπΌππππππ,π‘π‘/NA 1.034*** (4.756)
0.999*** (4.540)
0.976*** (4.502)
0.974*** (4.423)
0.973*** (4.412)
0.986*** (4.462)
0.967*** (4.423)
0.994*** (4.548)
πΌπΌππππππ,π‘π‘+2/NA -0.963*** (-3.510)
-1.036*** (-3.727)
-0.940*** (-3.425)
-1.008*** (-3.606)
-0.996*** (-3.550)
-1.012*** (-3.634)
-0.961*** (-3.474)
-1.069*** (-3.845)
π π π·π·ππ,π‘π‘/NA 5.277*** (3.703)
5.281*** (3.634)
4.957*** (3.477)
5.067*** (3.504)
4.883*** (3.297)
5.118*** (3.536)
4.861*** (3.381)
4.816*** (3.349)
πΌπΌπ π π·π·ππ ,π‘π‘/NA -2.104 (-0.793)
-2.446 (-0.912)
-1.951 (-0.731)
-2.303 (-0.855)
-2.190 (-0.808)
-2.207 (-0.819)
-2.063 (-0.772)
-2.164 (-0.809)
πΌπΌπ π π·π·ππ ,π‘π‘+2/NA 2.796 (1.428)
3.306* (1.667)
2.776 (1.417)
3.214 (1.612)
3.107 (1.560)
3.193 (1.605)
3.196 (1.622)
2.618 (1.313)
πΌπΌππ,π‘π‘/NA 8.795*** (6.878)
8.785*** (6.788)
8.759*** (6.855)
8.716*** (6.666)
8.670*** (6.661)
8.845*** (6.796)
8.770*** (6.717)
8.834*** (6.849)
πΌπΌπΌπΌππ,π‘π‘/NA 0.869 (0.454)
0.807 (0.420)
0.653 (0.345)
0.795 (0.413)
0.885 (0.460)
0.777 (0.402)
0.836 (0.439)
1.051 (0.550)
πΌπΌπΌπΌππ,π‘π‘+2/NA 3.537*** (2.619)
3.832*** (2.826)
4.013*** (2.992)
3.826*** (2.799)
3.745*** (2.743)
3.868*** (2.829)
4.018*** (2.954)
3.971*** (2.933)
π·π·πΌπΌππππ ,π‘π‘/NA 10.64*** (5.789)
10.61*** (5.692)
10.90*** (5.914)
10.43*** (5.572)
10.47*** (5.606)
10.37*** (5.556)
10.26*** (5.538)
9.681*** (5.151)
πΌπΌπ·π·πΌπΌππππ,π‘π‘/NA 0.536 (0.254)
1.156 (0.546)
0.996 (0.474)
1.278 (0.602)
1.217 (0.571)
1.208 (0.569)
1.590 (0.751)
1.371 (0.651)
πΌπΌπ·π·πΌπΌππππ,π‘π‘+2/NA 0.682 (0.259)
0.652 (0.244)
1.516 (0.575)
0.977 (0.366)
1.001 (0.375)
0.883 (0.330)
1.257 (0.474)
1.380 (0.519)
πΌπΌππππππ,π‘π‘+2/NA -0.266*** (-6.154)
-0.252*** (-5.749)
-0.262*** (-6.071)
-0.258*** (-5.859)
-0.255*** (-5.785)
-0.260*** (-5.928)
-0.260*** (-5.994)
-0.254*** (-5.834)
Year Fixed Fixed Fixed Fixed Fixed Fixed Fixed Fixed
Industry Fixed Fixed Fixed Fixed Fixed Fixed Fixed Fixed Number of observations
374 374 374 374 374 374 374 374
Adjusted π π 2 0.5090 0.4976 0.5093 0.4935 0.4940 0.4944 0.5017 0.5009
Appendix A. Description of variables
Table AI. Definition of variables
Variables Definition Cash Cash and cash equivalent Xcash Excess cash derived by the normal cash regression MV (Market capitalization +book value of liabilities)/total assets L Leverage, Total liabilities divided by total equity ME Market capitalization E Earnings before interest and tax NA Total assets β cash and cash equivalent RD Research and development expense I Interest expense DIV Common cash dividend SIZE Log (total assets) MTB Market capitalization / book value of total equity CPX Capital expenditure. Tangible assetsπ‘π‘ - Tangible assetsπ‘π‘β1 NWC Net working capital. Current assets β Current liabilities LTD Long term debt CF Operating cash flow Industry risk Median of standard deviation of industry for 10 years. Dividend dummy 1 if a firm pay dividend in t year, otherwise0. Business 1 if a firm with Business major CEO, otherwise 0. SCI_ENG 1 if a firm with Science or Engineer major CEO, otherwise 0. MBA 1 if a firm with MBA degree CEO, otherwise 0. Master 1 if a firm with Masterβs degree CEO, otherwise 0. Doctor 1 if a firm with Doctorβs degree CEO, otherwise 0. Age Age of CEO Tenure Difference between the first year that the CEO was appointed and the current fiscal year. New Appointment 1 if a firm with new CEO, otherwise 0.
Appendix B. OLS Result of normal cash regression
Table BI. OLS Regression Results of Normal Cash Regression
The dependent variable is Cash/assets. Cash/assets is the cash and cash equivalent divided by total assets. Ln(TA) is the log value of total assets. CF(Cash flow) is the operational cash flow to the firm. CPX(Capital expenditure)is the difference of property, plant, and equipment from year t-1 to year t. NWC(Working capital) is the current assets minus current liabilities. Industrial risk is the median industry standard deviation of the past 10 year cash flow. R&D is the research and development expenditure. We used two stage model to solve the endogeneity issue, and the instrument variable is 2 year lagged sales growth. All ratios are divided by the total assets. All variables are winsorized at 1% level. All models include year dummies. VIFs of all variables are less than 10. The ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels respectively.
The main independent variable is excess cash, and we can calculate this variable by using the normal cash regression of (Opler, Pinkowitz, Stulz and Williamson (1999 ))and (Bates, Kahle and Stulz (2009 )). (Opler, Pinkowitz, Stulz and Williamson (1999 ))examine the determinants of cash holdings and marketable securities by publicly traded U.S. firms. Previous literature measure the excess cash using similar regression model, and we mainly follow the model of (Bates, Kahle and Stulz (2009 )). The excess cash is defined as the amount of cash holdings that exceeds the normal level of cash holdings. Economics and finance literature show why firms hold cash, and these reasons are referred to the motives of holding cash. (Bates, Kahle and Stulz (2009 ))classify four motives why firms hold cash: the transaction motive, the precautionary motive, the tax motive, and the agency motive. Previous literature use these motives as variables in the normal cash regression to find the normal level of cash through the normal cash regression ((Dittmar and Mahrt-Smith (2007 )); (FrΓ©sard and Salva (2010 )); (Schauten, Van Dijk and van der Waal (2013 ))).
Variable (Cash/TA)
(1) 1stStage (MTB) (2) 2ndStage (Cash/TA)
Ln(TA) 0.055*** (7.83)
0.005*** (5.10)
CF/TA 2.464*** (19.26)
0.068*** (3.90)
NWC/TA 0.130** (2.51)
0.123*** (18.52)
CPX/TA 0.027 (0.30)
0.006 (0.53)
Leverage 0.439*** (7.95)
0.027*** (3.86)
Industry risk 0.181*** (6.08)
0.012*** (3.03)
Dividend dummy -0.137*** (-7.34)
-0.004* (-1.87)
R&D/TA 7.822*** (11.79)
0.169* (1.93)
2yr lagged sales growth 0.059*** (3.88)
MTB 0.004 (1.45)
Year effect Fixed Fixed
Number of observations 2906 2894
Adjusted π π 2 0.1998 0.1609