Accounting & Finance. 2023;00:1–26. wileyonlinelibrary.com/journal/acfi 1
1Department of Financial and Business Systems, Lincoln University, Christchurch, New Zealand
2UQ Business School, University of Queensland, St Lucia, Queensland, Australia
Correspondence
Maisam Ali, Department of Financial and Business Systems, Lincoln University, Christchurch 7647, New Zealand.
Email: [email protected]
Abstract
We examine the effect of a chief executive officer (CEO)'s expertise power on bank diversification. Using US bank data from 1990 to 2020, we find that a CEO's expertise power is positively associated with bank diversification.
Market competition and board composition (size and independence) positively affect this relationship. We also find that CEO delta and vega are the underlying mech- anisms through which expertise power leads to greater diversification. We address endogeneity concerns using the two-stage least squares, Heckman estimation and the difference-in-differences approaches and check result robustness in several ways. We provide a new explanation for bank diversification that is useful for policymakers in developing a bank strategy concerning CEO behaviour in diversification.
K E Y W O R D S
bank diversification, CEO's expertise power, competition, governance, strategic decisions
J E L C L A S S I F I C A T I O N G21, G32
R E S E A R C H A R T I C L E
A CEO's expertise power and bank diversification
Maisam Ali
1| Christopher Gan
1| Muhammad Nadeem
21 | INTRODUCTION
The chief executive officer (CEO) is a central element in banks' strategic decisions; CEOs make essential investment and financing decisions. The influence of CEOs on these decisions depends on their power and expertise (Chen et al., 2019; Finkelstein, 1992; Khan et al., 2021). CEO char- acteristics (such as education, experience, gender and values) are reflected more prominently in a bank's strategic decisions when the CEO is more powerful (Adams et al., 2005; Fang et al., 2020).
At the strategic level, the final decision will vary greatly from that made by top management, which reflects the direct influence of the CEO (Adams et al., 2005). It is believed that a power- ful CEO is more likely to invest in shareholder-value enhancing portfolios, e.g., acquisitions and research and development (Al Mamun et al., 2020), to grow the bank, secure his/her job,
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© 2023 The Authors. Accounting & Finance published by John Wiley & Sons Australia, Ltd on behalf of Accounting and Finance Asso- ciation of Australia and New Zealand.
maintain his/her reputation, and probably get more incentives (Chen et al., 2019). Managers make conservative decisions and prefer a safe portfolio because they are risk-averse and have non-diversifiable human capital (Pathan, 2009). However, an expert CEO makes decisions because their expertise is closely related to better risk management practices and the organisa- tion's critical components of the task environment, e.g., bank lending and non-lending activities (Ellul & Yerramilli, 2013; Finkelstein, 1992).
Previous studies demonstrate that managerial expertise plays an important role in bank stra- tegic decisions. The theoretical work by Finkelstein (1992) shows that expert CEOs better manage organisations' tasks and affect organisations' critical strategic decisions. Chen et al. (2019) find that a bank's management team's expertise is associated with securitising more loans and better risk management. Liang et al. (2016) show that corporate governance plays a key role in alleviat- ing the diversification discount in banking. They contend that the role of a CEO is more impor- tant in diversified, complex banks. King et al. (2016) illustrate that firms demand more skilled CEOs because of technological advances, operational complexity and innovation, suggesting a high impact of the CEO's skill and expertise on corporate strategies. Thus, we expand the litera- ture on corporate governance and organisations' strategic decisions by focusing on CEO expertise and bank diversification. We examine the effect of CEO expertise power on bank diversification.
Expertise power – a combination of a CEO's educational background, career path and expe- rience – greatly influences bank decision making (Fan et al., 2020; Finkelstein, 1992; Zacharias et al., 2015). The literature (e.g., Chen et al., 2019; Finkelstein, 1992; King et al., 2016) high- lights that managers' (CEOs') expertise highly impacts bank strategic decisions because of close connections to bank core knowledge and operations (Chen et al., 2019), financial risk manage- ment (Fang et al., 2020) and bank critical environment (King et al., 2016). The four dimensions of power – structural, expertise, ownership and prestige power – are all important. However, expertise power is the most salient and predominates (Finkelstein, 1992; King et al., 2016).
Moreover, CEO expertise makes CEOs more powerful, risk-takers and insulates them from the risk of termination (Al Mamun et al., 2020; Custódio et al., 2013, 2019). A CEO's expertise power increases the CEO's risk-taking ability, decision quality and bank profitability (Fang et al., 2020). Expert CEOs continuously learn from the market and quickly interpret the market.
Their prior experience, skills and connections enable them to acquire private information and make informed decisions. They handle complex matters, e.g., mergers and acquisitions, and make confident decisions because they trust their ability and judgement (Chikh & Filbien, 2011).
‘Bank diversification’ covers the income-generating, lending and non-lending financial services of banks. It includes both balance sheet and off-balance sheet activities1 (Laeven &
Levine, 2007; Shim, 2019). Diversification is an important management strategy adopted to grow bank revenue. Diversifying bank revenue to produce and sell fee-based services may provide economies of scale and scope benefits to the banks by reducing operating costs (Shim, 2013, 2019). Other than a source of additional revenue, diversification across and within the interest and fee-based income-generating activities decreases bank default and unsystematic risk and increases operational and risk-adjusted profit. Low or negatively correlated sources of income minimise risk and maximise profit (Liang et al., 2016; Shim, 2019).
To date, there is no unified theory that explains how CEO expertise power affects bank diver- sification. Upper echelon theory states that a CEO's expertise power greatly influences bank strategic decisions (decisions at the upper echelon) because decisions at the upper echelon are highly unprogrammed – decisions that are not routinely made – and uncertain – the outcome is not predictable because the environment is always changing. Expertise gives the CEO power to address the uncertainties in the bank task environment and risk management. An expert CEO has a deep understanding of and strong connection with the environment in which the bank
1 See, for example, bank management expertise and asset securitization policies by Chen et al. (2019).
operates. The rich knowledge of the CEO and the contacts he/she makes enable him/her to cope with the contingencies of the bank task environment. Prospect and social psychology theory states that CEOs are optimistic and overconfident in decision making, leading to more diversi- fied, risky decisions (Sheikh, 2019). An expert CEO may adopt risky strategies to signal to the market their superior abilities (Gounopoulos & Pham, 2018).
In this study, we use US bank data from 1990 to 2020. Consistent with the prediction, we find that CEO expertise power increases bank diversification. Market competition and board size also positively affect this relationship. We also find that CEOs' risk-taking and performance (delta and vega) are the underlying mechanisms through which expertise power leads to higher diversification. Our findings are robust to controlling for several CEO and bank-level variables known to impact bank diversification: year and bank fixed effects, based on an instrumental variable (2SLS), the Heckman two-stage and difference-in-differences (DID) approaches.
We highlight the channels through which a CEO's expertise power affects bank diversifi- cation. First, we use the CEO vega – the CEO risk channel – to substantiate the results that expert CEOs enhance bank risk-taking, which eventually leads to bank diversification. Expert CEOs strengthen bank risk management practices leading to better risk management and more diversification (Chen et al., 2019; Ellul & Yerramilli, 2013; Fang et al., 2020). Prior studies high- light that if diversification is risky (Abuzayed et al., 2018; DeYoung & Torna, 2013), then a CEO's vega will be a source of incentives that will motivate the CEO to take more risks by diversifying the bank's portfolios (Dunbar et al., 2020). Secondly, a CEO's delta – the CEO's pay-for-performance channel – is another way through which CEOs affect the diversification of their bank. Expert CEOs enjoy higher compensation (incentives) because of the higher demand for their expertise (Al Mamun et al., 2020; Custódio et al., 2013). We show that expert CEOs receive more incentives (pay-for-performance), motivating them to add new portfolios by diver- sifying their bank's activities (Ellul & Yerramilli, 2013).
We examine the role of market competition and board governance mechanisms to see if they influence the positive association between CEO expertise power and bank diversification.
Using the bank deposits index (Shim, 2019) as a proxy for market competition, we find that competition positively influences the relationship between a CEO's expertise power and bank diversification. We also find evidence that board governance proxied by board size and board independence (Liang et al., 2016) positively influence the relationship between a CEO's expertise power and bank diversification.
We contribute to the literature in several ways. First, we add to the corporate governance literature of the banking industry by investigating the role of a CEO's expertise power in bank diversification strategies.2 With deregulation, banks diversified from their main interest-based activities to include more fee-based activities. This study contributes to our understanding of shift in the bank business model by showing that CEO expertise plays a key role in bank diversification.
Secondly, we contribute to the literature by proposing a new measurement for diversification that explains diversification across the traditional (loan) and fee-based banking portfolios. Previ- ous studies use two variables for diversification that are subject to measurement error. Laeven and Levine (2007) and Liang et al. (2016) state that income-based measures are subject to differ- ent measurement errors whereas the asset-based measure of diversification is less exposed to measurement error. The proposed measure addresses the measurement error problem and meets our objective of measuring bank diversification.
2 Most studies exclude banks and other financial institutions because of differences in the business model and regulations, even though banks are an integral part of the economy. For example, Gawehn and Müller (2019) highlight two reasons why banks are excluded:
the business model and the impact of regulations. Banks are subject to different accounting treatments (rules) as a result of the model change. Similarly, in addition to accounting compliance, banks are also subject to regulations that do not function at firm level.
Thirdly, we add to the literature by investigating the role of a CEO's expertise power in determining bank diversification. We show that CEO expertise power has a significant impact on bank diversification. Fourthly, we test the channels through which a CEO's expertise power affects bank diversification. We identify that a CEO's expertise power affects bank diversifica- tion via two channels: the CEO risk channel (CEO vega) and the CEO performance incentives channel (CEO delta). Finally, we contribute to the literature by finding that market competi- tion and bank governance influence the relationship between CEO expertise power and bank diversification.
The rest of the paper is organised as follows. Section 2 presents a brief literature review;
Section discusses the development of the hypothesis; Section 3 discusses the sample, methodol- ogy, variables and their description. In Section 4, we present the empirical results and Section 5 concludes the paper and provides recommendations for future research.
2 | THE LITERATURE AND HYPOTHESIS DEVELOPMENT 2.1 | CEO expertise power and bank diversification
The CEO is the most powerful governing person and essential human capital in an organisa- tion, making all significant financial decisions (Fan et al., 2019). Upper echelon theory states that decisions at the upper echelon are highly bureaucratic and uncertain. A CEO's expertise power in bank strategic decisions has a high impact because these decisions (upper echelon) are highly unprogrammed and uncertain. Expertise gives the CEO the power to address the uncer- tainties of the bank task environment and risk management. An expert CEO would have a rich, deep understanding of the organisation and its products and services (Finkelstein, 1992), which helps in diversification decisions. An expert CEO is more competent, powerful and effective in executing bank strategy, e.g., acquisitions (Liang et al., 2016). A CEO's expertise comes from different aspects, e.g., educational background (Mun et al., 2020; Wang & Yin, 2018), career path (Finkelstein, 1992) and experience (Smith & White, 1987). Managers holding different posi- tions and expertise in a particular subject yield a better performance in solving various prob- lems. The relevant expertise empowers them to better implement their firm's strategic choices (Finkelstein, 1992; Hickson et al., 1971; Smith & White, 1987; Yetton & Bottger, 1982).
Similarly, prospect and social psychology theory state that CEOs' characteristics affect CEO behaviour and decision making; optimistic and overconfident CEOs make diversified and risky decisions (Sheikh, 2019). Diversification may increase banks' revenue volatility, lower the capital ratio (DeYoung & Roland, 2001) and reduce loan monitoring incentives (Acharya et al., 2006).
Expert CEOs may adopt risky strategies to show and signal to the market their superior abilities (Gounopoulos & Pham, 2018).
Expert CEOs, by their superior abilities, address bank financial issues because they better understand and manipulate the task environment; they address the market dynamics, industry deregulation and the challenges of technology changes better than their less-expert counter- parts (Chahine, 2007). A CEO's ability and expertise are essential, unique management resources that affect a bank's subsequent management decisions (e.g., diversification) and performance (Al Mamun et al., 2020; Koester et al., 2017). In their study, Nadeem et al. (2021) contend that it is the heterogeneous behaviour of CEOs reacting to various environments based on their ability.
CEOs with general ability and a high reputation may be able to invest resources efficiently in various projects, e.g., research and development, and reduce costs (Fang et al., 2020; Koester et al., 2017). A CEO's expertise power is associated with reduced information uncertainty and fair decision making. Expert, able CEOs, because of their reputation and career concerns, engage in various financial and investment policies (e.g., tax avoidance, research and development and corporate social responsibility) and dealings in financial markets (e.g., funding costs, portfolio
management) that enhance bank earnings quality and performance (Cheung et al., 2017; Yuan et al., 2017).
CEO expertise power may affect bank diversification in several ways. First, expert CEOs can enhance bank risk-taking; we refer to this as the CEO risk channel. Expert CEOs are engaged in the economies of scale and scope and improving their bank's risk profile (Fang et al., 2020). The real power resides in the hands of CEOs who control the risk exposure of their bank. We expect expert CEOs to be associated with better risk management and more diversification because they have greater managerial and technical skills as is evident from prior studies that document that expert managers tend to securitise more loans with lower credit risk (Chen et al., 2019).
The arguments follow the prospect and social psychology theory discussed above. Diversifica- tion across the loan portfolio (the core business) reduces risk because of a bank's economies of scale and scope. However, diversification across non-traditional activities increases a bank's systematic risk, revenue, profitability and stability (see Ahamed & Mallick, 2017; Holzmayer
& Schmidt, 2020; Shim, 2019; Tran et al., 2021) and reduces a bank's monitoring incentives (Acharya et al., 2006). Expert CEOs may adopt risky strategies to signal to the market their superior abilities and that they have incentives (i.e., industrywide career opportunities) to make such risky decisions (Gounopoulos & Pham, 2018). Thus, we expect the CEO vega to be a source of incentives that motivate CEOs to take more risk and diversify their bank's portfolio (Dunbar et al., 2020).
Secondly, CEO incentives measured by CEO pay for performance (CEO delta) is another channel through which CEOs may affect bank diversification. Custódio et al. (2013) and Al Mamun et al. (2020) show that expert (skilled) CEOs enjoy higher remuneration (incentives).
Diamond (1984) and Shim (2019) find that diversification is driven by managerial incentives, implying that higher CEO incentives (pay-for-performance) are likely to be associated with more diversification. Given the upper echelon theory, it is evident that increasing delta reflects the expertise of the upper echelon, which improves a bank's performance and stability. Thus, CEOs strive to improve their bank's performance by diversifying the bank's portfolios (Ellul &
Yerramilli, 2013).
The above discussion suggests that expert CEOs would have more knowledge of bank oper- ations and the ability to scan the environment for potential opportunities. Therefore, CEOs with greater expertise power may be associated with more bank diversification. Thus, we hypothesise the following relationship:
Hypothesis 1 A CEO's expertise power is positively associated with bank diversification.
3 | DATA AND METHODOLOGY 3.1 | Data and sample
We collect data on US commercial banks from 1990 to 2020. We obtain the data from data- bases such as Wharton Research Data Services (WRDS), Compustat, Business Week, Forbes and local newspapers. Bank financial data are from Compustat and governance data are from BoardEx. Our initial sample of banks from Compustat and BoardEx were merged based on
‘GVKEY’, which yielded 24,480 bank-year observations. Following prior literature (e.g., Laeven
& Levine, 2007; Shim, 2019), we eliminate banks with missing data for accounting variables such as income, loans, equity capital, deposits and CEO characteristics. There are many extreme values among the observations of the independent and dependent variables (CEO expertise power and bank diversification constructed from raw data). We therefore winsorise CEO expertise power, bank diversification and other control variables at the 1% and 99% levels. Finally, banks that do
not have at least five continuous yearly time-series observations are excluded to obtain highly balanced data. This procedure leads to a final sample of 1752 bank-year observations. Table 1 presents the distribution of the full sample.
3.2 | Model specification
We empirically test the relationship between a CEO's expertise power and bank diversification using the following regression model:
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵= 𝛼𝛼𝐵𝐵+ 𝛼𝛼1𝐶𝐶𝐶𝐶𝐶𝐶 𝐵𝐵𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐶𝐶𝐵𝐵𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵+ 𝛼𝛼𝐵𝐵′𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵+ 𝛿𝛿𝐵𝐵+ 𝜀𝜀𝐵𝐵𝐵𝐵
(1) where Bank diversification represents bank diversification; CEO expertise power is the CEO's expertise power; CV is a matrix of all other control variables defined below in subsequent para- graphs; subscripts i and t stand for cross-section and time, respectively; δt denotes time-invariant effects; and εit is the error term. The key variable of interest is CEO expertise power.
3.2.1 | Dependent variable
We follow Laeven and Levine (2007), King et al. (2016) and Liang et al. (2016) and measure diversification as HHINT =𝐴𝐴 ln|
||
𝐹𝐹 𝐹𝐹𝐹𝐹−𝑏𝑏𝑏𝑏𝑏𝑏𝐹𝐹𝑏𝑏 𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝐹𝐹 𝑇𝑇 𝑏𝑏𝑇𝑇𝑏𝑏𝑇𝑇 𝐴𝐴𝑏𝑏𝑏𝑏𝐹𝐹𝑇𝑇𝑏𝑏
||
| ; which covers both diversification and speciali-
T A B L E 1 Distribution of observations.
Year Frequency Percent Cumulative frequency
2000 44 2.51 2.51
2001 46 2.63 5.14
2002 50 2.85 7.99
2003 56 3.2 11.19
2004 63 3.6 14.78
2005 74 4.22 19.01
2006 86 4.91 23.92
2007 114 6.51 30.42
2008 110 6.28 36.7
2009 105 5.99 42.69
2010 103 5.88 48.57
2011 97 5.54 54.11
2012 102 5.82 59.93
2013 103 5.88 65.81
2014 105 5.99 71.8
2015 105 5.99 77.8
2016 99 5.65 83.45
2017 100 5.71 89.16
2018 97 5.54 94.69
2019 93 5.31 100
Total 1752 100
Note: This table presents the sample distribution by year.
sation. The larger (smaller) HHINT, the more (less) diversified the bank is. There are different measurements of diversification in the corporate and financial literature. Among them, the Herfindal Index is a common measurement of diversification. There is variation in the content of the index, depending on the sector and study purpose. We are interested in the diversification across the interest- and fee-based activities of banks.
3.2.2 | Independent variable
We measure CEO expertise power as an index of: (a) the CEO's number of positions (titles) held in the bank, such as chair, president, chief operating officer (COO), chief finance officer (CFO), vice president, vice chair, director and a member of key committees (Chen et al., 2019; Haynes et al., 2019); (b) the CEO's qualifications (King et al., 2016); (c) the CEO's functional specialisa- tion (Smith & White, 1987); and (d) the CEO's experience (tenure) (Chen et al., 2021). The CEO's number of positions (titles) variable takes the value of the number of positions (titles) the CEO has held in the bank. The qualification variable is a dummy variable equal to 1 if the highest qualification is MBA or CPA or PhD, and 0 otherwise. The CEO's functional specialisation is also a dummy variable. It equals 1 if the CEO specialises in production, sales, general manage- ment, finance or law, and 0 otherwise. We use factor analysis because it addresses subjective judgement, resolves the problem associated with highly intercorrelated variables and acts as an exploratory technique that extracts the structure and composition of its dimensions used in the analyses (King et al., 2016). We calculate the CEO expertise power index for each year following Custódio et al. (2013) and King et al. (2016). The CEO expertise power index is the first factor of the principal component analysis.
3.2.3 | Market competition
The literature has various proxies for market competition. For banks, we use the deposit index,
𝐴𝐴 𝐴𝐴 𝐴𝐴 𝐴𝐴 =
∑𝑛𝑛
𝑖𝑖=1
(
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑑𝑑𝑖𝑖𝑖𝑖𝑑𝑑
∑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑑𝑑
)2
. It has been used in previous studies (Cetorelli & Strahan, 2006;
Dick, 2006; Shim, 2019). HHI accounts for the concentration of banking activities in a region.
3.2.4 | Other control variables
Following prior literature, we control for bank leverage, which acts as a monitoring and disci- plining mechanism, balancing the risk between financial decisions. Diversification across non-traditional activities is not risk-free, hence leverage is expected to reduce bank diversifica- tion; the same is true for the risk-weighted capital ratio. We control for bank profitability, earnings volatility and revenue growth, which capture the impact of bank income, bank returns and bank return volatility on bank decisions to diversify. We expect profitability and revenue growth to be positively associated and return volatility negatively associated with bank diversification (de la Fuente & Velasco, 2020; Liang et al., 2016; Shim, 2013). We add the variable tax avoidance, which is regarded as a risky activity that can cost the bank if the authorities find any irregularity in managing tax. In addition, the managerial cost of tax management is high and can be exacer- bated by diversification (Hsu et al., 2017). Thus, we expect diversified banks to be involved in less tax avoidance. To account for the directors' interests in the bank, we add the variable, directors' shareholding, in the belief that directors with larger stakes have more incentive to closely super-
vise the bank's business activities that can earn more profit (Fang et al., 2020). Thus, we expect a positive impact of directors' shareholding on bank diversification.
We also control for bank free cashflow, which creates bank liquidity that enables the bank to extend more loans. Thus, a negative relationship is expected between free cash flow and bank diversification. The loan loss provision affects the bank information opacity because it is a source of management performance manipulation. Thus, under high loan loss provision, we expect that bank diversification decreases. The inclusion of non-performing assets would control for performance manipulation in the bank. A bank may extend more loans to generate more fees and related charges that mask the poor performance of the bank's management, generating more non-performing loans (Kupiec et al., 2017). Gender diversity – more female directors on the board – is associated with risk aversion and lower preference. Thus, we expect higher gender diversity to negatively affect bank diversification (Faccio, 2016). A bank's growth opportunities provide new ways of earning. Banks should invest more in new opportunities that generate more profit. The more the growth opportunities, the higher the bank's diversifica- tion (de la Fuente & Velasco, 2020). We also control for macro-level variables. Country GDP growth is associated with life prosperity and resource abundance; the opposite is true for the unemployment rate. Thus, we expect a positive sign for GDP growth and a negative sign for the unemployment rate (Kupiec et al., 2017). Definitions of the study variables are presented in the Appendix 1.
4 | RESULTS AND DISCUSSION 4.1 | Descriptive statistics
Table 2 summarises the statistics for the dependent, independent and control variables for US commercial banks. The average logged value (2.85) of bank diversification, with a maximum value of 5.62, in this study is comparable with the mean in prior literature (e.g., Shim, 2013).3 This indicates that, on average, US commercial banks are well diversified. The summary statistics on CEO expertise power show CEOs in the sample have a mean of 0.473. Custódio et al. (2013, 2019) use an index for CEO skills for ExecuComp and S&P 1500 firms with CEOs' positions in different firms, the number of firms, conglomerates and industries in which a CEO has worked.
Our descriptive statistics show means (min, max) of 0.26 (0.00, 0.94) and 0.00 (−1.50, 7.23), respectively. On average, as for the CEOs of firms in ExecuComp and S&P 1500, CEOs in the banking sector are expert and skilful. The mean value of 8.76 for CEO tenure indicates that, on average, a CEO has served the bank for almost 9 years. The mean value of CEO qualifications (2.13) indicates that, on average, a CEO has two qualifications (e.g., MBA, CPA and PhD). Simi- larly, on average, CEOs in the sample have almost four titles, indicating the CEO has served in four different positions. On average, a CEO specialised in one functional area (e.g., production, sales, general management, finance and law) and, at most, in three areas.
Table 3 presents the univariate comparisons of the bank variables for two subgroups: banks with high and low CEO expertise power. A bank with high CEO expertise power is more diversi- fied and has larger values than a bank with low CEO expertise power. Thus, banks with higher CEO expertise power are associated with more bank diversification; the results are statistically significant.
Table 4 lists the pair-wise correlations between bank diversification and CEO expertise power and the control variables. The univariate correlation between bank diversification and CEO expertise power is positively, statistically significant at the 10% level, indicating that banks with
3 Shim (2013) analyses US commercial data from 1992 to 2011 and shows the values as follows: revenue diversity: mean (median) value, 0.4782 (0.4867); minimum value, 0.0014; and maximum value, 0.8493.
an expert CEO undergo more diversification. We calculated the variance inflation factors (VIFs);
untabulated results show no multicollinearity problem among the variables.
4.2 | Baseline regression
We present our baseline results in Table 5. Columns (1), (3) and (5) show the univariate regression analysis results and columns (2), (4) and (6) show the multivariate regression analysis results. In columns (1) and (2), we use our proposed measure of diversification; in columns (3) and (4), we use a diversification measure developed by Kim et al. (2020); and in columns (5) and (6), we use a diversification measure developed by Laeven and Levine (2007). The latter two measures are used as the alternative dependent variables for the robustness check of our proposed dependent variable for bank diversification. The positive coefficient on the CEO expertise power variable confirms our hypothesis H1 that expert CEOs increase bank diversification. A one standard deviation increase in CEO expertise power leads to a 1.3513% increase in bank diversification. Our
T A B L E 2 Descriptive statistics of the variables.
Variable Obs. Mean SD Min Max
Bank diversification 1752 2.856 1.487 0.978 5.629
CEO expertise power 1752 0.473 0.727 −3.324 2.996
CEO tenure 1752 8.757 7.904 0.000 42.000
No. qualifications 1752 2.135 0.935 1.000 7.000
CEO titles 1752 3.720 2.017 1.000 10.000
CEO specialisation 1752 1.109 0.347 0.000 3.000
Board independence 1752 3.838 1.256 0.000 8.000
Board size 1752 5.595 1.010 3.000 10.000
Bank competition 1752 0.001 0.002 0.000 0.009
CEO incentives 1752 6.975 1.488 3.929 9.310
CEO delta 1728 4.099 1.275 1.888 6.457
CEO vega 1560 2.339 2.128 −6.908 5.549
Bank leverage 1752 −0.109 0.027 −0.167 −0.066
Bank profitability 1743 −4.589 0.373 −5.298 −4.075
Risk weighted capital ratio 1748 2.635 0.168 2.392 3.001
Directors' shares 1416 6.899 1.045 2.773 8.762
Non-performing assets 1741 −0.039 3.409 −5.538 5.534
Bank free cashflow 1750 4.482 3.012 −4.929 8.203
GDP growth 1752 1.964 1.450 −2.537 4.128
Loan loss provision 1705 3.156 2.083 −3.817 7.257
Market to book value 1752 1.464 0.586 0.000 2.727
Gender diversity 1752 0.469 0.679 0.000 2.000
Revenue growth 1752 9.460 13.959 −11.444 44.004
Earnings volatility 1752 0.002 0.002 0.000 0.007
Tax avoidance 1752 0.479 0.451 −0.328 1.425
Unemployment rate 1752 6.025 1.858 3.670 9.630
Note: This table presents the descriptive statistics for the key variables used in the analyses. All variables are defined in the Appendix 1.
results confirm the theoretical arguments that expert CEOs have a rich, deep understanding of the organisation and its products and services. They are more competent, powerful and effective in executing bank strategy, e.g., bank diversification and acquisitions (Liang et al., 2016; Mun et al., 2020; Wang & Yin, 2018). We find consistent results when we apply alternative measures of bank diversification (see columns 3–6).
As the negative coefficients on bank leverage and risk-weighted capital ratio in column (2) indi- cate, highly leveraged banks and banks with a larger capital adequacy ratio are less diversified.
This implies that those banks do not tie up their capital in heavy investments outside their core business and concentrate on lending. The positive coefficient on bank profitability suggests that there is a positive relationship between bank diversification and bank profitability, indicating that banks with more profit are more diversified. The positive coefficients on non-performing loans indicate that banks with more dead assets and more asset losses diversify more. The negative coefficient on loan loss provision suggests that bank diversification decreases with information asymmetry. Similarly, the negative coefficients on directors' shares and gender diversity indicate that banks whose directors have more shares and banks with more women directors show less diversification. The positive coefficients on market to book ratio and GDP growth indicate that banks with more growth opportunities in growing economies are involved in more diversification activity. The negative coefficient on unemployment suggests that banks in places with fewer job opportunities are less diversified.
4.3 | Endogeneity tests
A potential concern with our empirical analysis is endogeneity. Our study is likely to suffer from reverse causality between CEO expertise and bank diversification; it could be that more diversi- fied banks attract better CEOs. Unobservable factors may also affect the relationship. To address the endogeneity concern in the estimation of the impact of CEO expertise power on bank diver- sification, we use three different techniques: the panel data technique, the 2SLS (instrumental) technique and the difference-in-differences technique.
T A B L E 3 Mean-difference test.
Variable High-power subsample Low-power subsample Difference p-Value
Bank diversification −0.022 −0.082 0.06 0.011
Bank leverage 0.676 0.962 −0.285 0.001
Bank profitability −1.653 −1.812 0.159 0.055
Risk weighted capital ratio 2.594 2.641 −0.046 0.000
Directors' shares 6.525 6.952 −0.427 0.000
Non-performing assets −0.103 −0.03 −0.072 0.749
Bank free cashflow 4.096 4.535 −0.44 0.041
GDP growth 2.212 1.929 0.283 0.002
Loan loss provision 2.743 3.214 −0.471 0.001
Market to book value 1.508 1.458 0.051 0.241
Gender diversity 0.649 0.472 0.177 0.009
Revenue growth 8.203 9.632 −1.43 0.123
Earnings volatility 0.007 0.003 0.004 0.077
Tax avoidance 0.512 0.475 0.036 0.252
Note: This table presents a mean-difference analysis for the key variables used in the analyses. All variables are defined in the Appendix 1.
TABLE 4The correlation matrix. Variable12345678910111213141516 (1) Bank diversification1.00 (2) CEO expertise power0.050*1.00 0.04 (3) Bank leverage−0.080*−0.031.00 0.000.21 (4) Bank profitability0.03−0.030.183*1.00 0.280.200.00 (5) Risk weighted capital ratio−0.040.00−0.296*0.001.00 0.100.890.000.90 (6) Directors' shares−0.245*0.187*0.069*0.030.011.00 0.000.000.010.320.64 (7) Non-performing assets0.098*0.040.117*0.148*−0.215*0.064*1.00 0.000.060.000.000.000.02 (8) Bank free cashflow−0.099*−0.01−0.086*−0.050*0.010.155*−0.090*1.00 0.000.770.000.040.570.000.00 (9) GDP growth0.03−0.03−0.066*−0.317*−0.121*−0.01−0.347*0.076*1.00 0.300.200.010.000.000.630.000.00 (10) Loan loss provision−0.290*−0.020.207*0.239*−0.179*0.318*0.149*0.243*−0.239*1.00 0.000.340.000.000.000.000.000.000.00 (11) Market to book value0.088*0.000.158*−0.211*−0.090*−0.05−0.040.020.250*−0.256*1.00 0.000.970.000.000.000.060.080.340.000.00 (12) Gender diversity−0.097*−0.087*0.094*0.050.052*−0.05−0.04−0.04−0.010.02−0.021.00 0.000.000.000.060.030.070.140.100.830.400.46 (Continues)
TABLE 4(Continued) Variable12345678910111213141516 (13) Revenue growth0.030.051*−0.100*−0.153*−0.119*−0.076*0.152*−0.148*−0.02−0.091*0.103*−0.054*1.00 0.290.030.000.000.000.000.000.000.490.000.000.03 (14) Earnings volatility0.03−0.074*−0.020.239*0.02−0.053*0.00−0.073*−0.131*0.02−0.159*−0.01−0.047*1.00 0.300.000.430.000.540.050.870.000.000.360.000.720.05 (15) Tax avoidance−0.04−0.04−0.076*0.124*0.184*−0.125*−0.186*0.00−0.109*0.121*−0.153*0.03−0.050*0.021.00 0.140.100.000.000.000.000.000.910.000.000.000.230.040.40 (16) Unemployment rate
−0.055*0.062*0.030.230*0.354*−0.02−0.082*−0.083*−0.435*0.195*−0.274*−0.01−0.054*0.148*0.332*1.00 0.020.010.170.000.000.580.000.000.000.000.000.770.020.000.00 Note: This table presents pair-wise correlations between bank diversification, CEO expertise power, and other important BHC characteristics. All variables are defined in the Appendix 1. * denotes statistical significance at the 10% or lower level.
T A B L E 5 The impact of CEO expertise power on bank diversification.
Variable
Bank diversification Bank diversification Bank diversification
(1) (2) (3) (4) (5) (6)
CEO expertise power 0.020*** 0.029*** 0.011*** 0.010*** 0.038*** 0.047***
(0.007) (0.011) (0.003) (0.003) (0.009) (0.014)
Bank leverage −0.021*** −0.011*** −0.037***
(0.005) (0.002) (0.007)
Bank profitability 0.019** 0.010*** 0.034***
(0.008) (0.001) (0.009)
Risk weighted capital ratio −0.027*** −0.007*** −0.048***
(0.007) (0.003) (0.011)
Directors' shares −0.088*** −0.037*** −0.146***
(0.017) (0.004) (0.021)
Non-performing assets 0.020*** 0.009*** 0.036***
(0.007) (0.001) (0.009)
Bank free cashflow −0.006 −0.008*** −0.013
(0.007) (0.002) (0.010)
Loan loss provision −0.072*** −0.041*** −0.130***
(0.011) (0.003) (0.017)
Market to book value 0.063** 0.014** 0.079**
(0.027) (0.007) (0.033)
Gender diversity −0.059*** −0.002 −0.080***
(0.020) (0.005) (0.028)
Revenue growth −0.002 0.000 −0.002
(0.001) (0.000) (0.002)
Earnings volatility 0.084 0.050 −0.267
(0.562) (0.260) (0.779)
Tax avoidance 0.040 −0.097*** 0.014
(0.031) (0.010) (0.042)
Unemployment rate −0.073* 0.065*** −0.055
(0.044) (0.015) (0.069)
GDP growth 0.275*** −0.155*** 0.260**
(0.084) (0.026) (0.125)
Constant −0.075*** 3.288** −0.396*** −1.892*** −0.272*** 3.478
(0.025) (1.534) (0.028) (0.511) (0.069) (2.384)
Observations 1752 1382 1735 1367 1752 1382
R 2 0.008 0.181 0.031 0.543 0.006 0.270
Note: This table reports the results of baseline regressions with two alternative dependent variables that examine the relationship between CEO expertise power and bank diversification. In columns (1) and (2), we present the results based on our proposed measure of diversification. In columns (3) and (4), we present the results for the diversification measure based on Kim et al. (2020). In columns (5) and (6), we present the results for the diversification measure developed by Laeven and Levine (2007). All variables are defined in the Appendix 1. The robust standard errors are presented in parentheses. ***, ** and * denote statistical significance at 1%, 5% and 10%
levels, respectively.
4.3.1 | Panel data regression approach
To check that our baseline model is consistent, we use a panel regression model approach to estimate the results. This approach addresses the unobservable, time-invariant and bank-specific factors such as bank management style and its quality, bank culture, bank exposure, unique business strategy and market perception. It also addresses endogeneity caused by bank and year-specific effects (Andres & Vallelado, 2008; Tran et al., 2021). Our estimated fixed effect results are presented in Table 6, column (1). The positive coefficient on CEO expertise power is still significant at the 95% confidence level. This model suggests that our baseline results are not driven by unobserved time-invariant and bank-specific omitted variables.
4.3.2 | Two-stage least squares method
We replicate our baseline regression equation using an IV regression approach to address the endogeneity caused by time-varying omitted variables. We identify many instruments that are correlated with CEO expertise power but not with bank diversification. First, we use bank size – we call it CEO span of control – and CEO's compensation as instruments based on prior studies (e.g., Custódio et al., 2013; Edmans et al., 2009), arguing that bank size and compensation are
T A B L E 6 CEO expertise power and bank diversification: fixed effects and 2SLS estimates.
Model Panel model 2SLS
Variable Fixed effects Stage 1 Stage 2
CEO expertise power 0.029** 0.123**
(0.011) (0.062)
CEO span of control 0.046***
(0.011)
Cash compensation 0.034***
(0.010)
CEO age 0.009*
(0.005)
CEO outsiderness 0.038**
(0.015)
Constant 1.208*** 2.021** 1.058***
(0.230) (0.871) (0.258)
Observations 1382 1382 1382
Bank dummy YES NO NO
Year dummy YES YES YES
R 2 0.177 0.178 0.183
Adj. R 2 0.169 0.160 0.163
IV F-Stat 10.07
Sargan (p-value) 3.045(0.218)
Hausman p-value 0.109
Note: This table presents the results of the fixed effect and 2SLS regressions with cluster-robust standard errors of bank diversification and CEO's expertise power. The robust standard errors are presented in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. All variables are defined in the Appendix 1.
commensurate with the talent of top management; larger banks hire expert, talented CEOs and pay higher remuneration to them.
Second, we use CEO outsiderness, calculated as a dummy variable equal to 1 if the CEO was hired from outside and 0 otherwise; outsider CEOs demonstrate superior leadership and mana- gerial skills (Gupta, 1984). They are thought to be more expert, i.e., have more knowledge and experience. An outsider CEO may have extensive contacts with multiple organisations' task envi- ronments; the more complicated the task environment a CEO faces, the more expert the CEO will be (Al Mamun et al., 2020). Third, we use CEO age as an instrument. Ageing CEOs undergo many psychological or mental changes, such as wisdom and decisiveness; older CEOs show more ability that affects bank decisions (Yim, 2013).
In the first stage, we regress CEO expertise power on all exogenous variables, including all independent and instrumental variables.4 The positive, significant coefficients of all instruments indicate that they are good predictors of CEO expertise power. The instrumental variables are positive and statistically significant as expected. The F-statistic for the excluded instrument in the first-stage regression is significant at the 1% level, indicating a good fit of the first-stage regres- sion model. The Sargan test value is statistically insignificant suggesting that the instruments are not overidentified.
Our estimated results of the IV regression approach using the two-stage least squares (2SLS) estimator are presented in Table 6, columns (2) and (3). The empirical specification is similar to that in Table 5 for our baseline ordinary least squares (OLS) regression. Overall, the results in Table 6 support the argument that banks with more expert CEOs are more diversified.
4.3.3 | Difference-in-differences approach
Following Farag and Dickinson (2020), we use the difference-in-differences approach to address endogeneity concerns in the study. This approach is also known as a quasi-experiment; it captures the differences between two groups (control and treatment) based on a specific event. We set the event as when the bank CEO changes. In our quasi-experiment, the treatment group comprises banks where the CEO has changed; the control group comprises banks where the CEO has not changed. With a change in the CEO, the CEO expertise power also changes. The post-change CEOs' expertise would show if the CEO expertise power has a significant impact on bank diver- sification. We check if there is any change in diversification strategy after the bank CEO has changed. To obtain answer, we estimate the following equation:
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 = 𝛽𝛽1+ 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶 𝐵𝐵𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐶𝐶𝐵𝐵𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵+ 𝛽𝛽3𝑃𝑃 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵
+𝛾𝛾 𝐶𝐶𝐶𝐶𝐶𝐶 𝐵𝐵𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐶𝐶𝐵𝐵𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵× 𝑃𝑃 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵+ 𝛼𝛼𝐵𝐵′
𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵+ 𝛿𝛿𝐵𝐵+ 𝜀𝜀𝐵𝐵𝐵𝐵
(2) where Bank diversification is the dependent variable; and Post stands for a change in CEO (it is a dummy variable equal to 1 for years following a change in CEO and 0 otherwise; we are more interested in the interaction term because it estimates the post-treatment effect); CVit is a matrix of control variables: δt denotes the time-invariant effects: and ɛit is the model error term.
Our estimates from the difference-in-differences approach estimating the impact of CEO expertise power on bank diversification using a quasi-natural experiment are presented in Table 7, column (1). The results show that high CEO expertise leads to more bank diversification. A
4 While using the Wu–Hausman test, we follow the two-step estimation process to investigate whether there is endogeneity in the data. In step one, we estimate the first-stage model by regressing the CEO's expertise power on all variables, including the instrumental variables and all exogenous variables. In step two, we regress the diversification on all variables, including all independent variables and residuals obtained from the first step. We reject the null hypothesis of exogeneity if the coefficient on the residuals is statistically significant. Also, we regress the CEO expert power on all variables, including instrumental variables and all other exogenous variables. We follow the rule of thumb that if the F-test value of the excluded IVs is >10, we reject the null hypothesis that the instruments are weak.
one standard deviation increase in CEO expertise power leads to a 6.108% increase in bank diversification.
4.3.4 | The Heckman two-stage procedure
We use the Heckman two-stage model to control for self-selection bias. For example, expert CEOs might prefer to work for specific banks that appeal to them, or certain banks might appoint CEOs with higher expertise. These unobservable omitted variables may drive both CEO expertise power and bank diversification. Although we use the bank fixed effect model to control for potential omitted variables, it may not fully rule out self-selection bias in the data (Luo et al., 2018). A technique that may address unobservable omitted variable bias (self-selection bias) is the Heckman (1979) two-stage method (Islam et al., 2021). In the first stage, we estimate the probit model using all control variables in the baseline model. The dependent variable in the probit model is censored, based on the median value of CEO expertise power. It equals 1 if the CEO expertise power is above the median value and 0 otherwise.
𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶= β0+ β2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶+ 𝛿𝛿𝐶𝐶+ ε𝐶𝐶𝐶𝐶
(3) In the second stage, we regress bank diversification on CEO expertise power with all other control variables included in the baseline model as explanatory variables. In the second stage, we include the inverse Mills ratio obtained from the first stage to control for self-selection bias.
The results are presented in Table 7, column (2). They show that almost all the explanatory variables are significant and the directions agree with our expectations. The results are quite similar to those estimated by the OLS method, which shows that the Heckman two-stage model is unlikely to suffer from the selection of weak instruments. The coefficient of CEO expertise power is posi tive and statistically significant, indicating that bank diversification increases with an increase in CEO expertise.
T A B L E 7 Difference-in-difference and Heckman two-stage estimates.
Model DID approach Heckman approach
Variable DID Stage 2
CEO expertise power 0.028***
(0.010)
DID 0.029***
(0.010)
Inverse Mills ratio 0.026
(0.159)
Constant 3.315 1.093***
(5.732) (0.263)
All controls YES YES
Year dummies YES YES
Observations 1382 1373
R 2 0.181 0.182
Note: This table presents the results of the difference-in-differences and Heckman two-step regressions with cluster-robust standard errors of bank diversification and CEO expertise power. In column (1), we report difference-in-differences and in columns (2) and (3), we report Heckman two-step. The robust standard errors are presented in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. All variables are defined in the Appendix 1.
4.4 | Moderation effect 4.4.1 | Market competition
Market competition may also affect a CEO's diversification decisions. Market competition compels management to expand the business, either relevant or irrelevant, to enhance bank performance (Ljubownikow & Ang, 2020). For competitive initiatives, such as resource allocation and busi- ness expansion, the CEO plays a greater role in expansion strategies (Zacharias et al., 2015). The disciplining force of competition generates a fear of liquidation and the drive-out-of-business, which builds pressure on CEOs to make difficult decisions. The competition-discipline theory also states that competition increases bank risk (Arping, 2019; Azmi et al., 2019; Chou et al., 2011;
Sheikh, 2018). When competition threatens, capable, expert managers (CEOs) are more likely to invest in developing new products than in existing products (Yung & Nguyen, 2020).
In this section, we examine how market competition affects the association between CEO expertise power and bank diversification. The results are presented in Table 8, column (1). They show that the interaction variable is significant, and the direction agrees with our expectations.
The coefficient on the interaction term is positive and statistically significant, indicating that market competition positively influences bank diversification.
4.4.2 | Board governance
The literature shows that board governance affects banks' strategic decisions. A larger board positively affects bank and CEO decision making. More members on the board bring more intel- lectual capital and resources – they may have external linkages – to the bank (Fang et al., 2020).
Moreover, a larger board has greater incentives for members to secure their jobs and improve bank performance by finding an optimal solution to problems banks face and supporting the CEO's
T A B L E 8 CEO expertise power and Bank diversification: The moderating effect of competition and board structure.
Variable Competition Board size Board independence
CEO expertise power 0.045*** 0.022** 0.072***
(0.010) (0.011) (0.016)
Competition * CEO expertise power 0.044***
(0.008)
Board size * CEO expertise power 0.060**
(0.028)
Board. independence * CEO expertise power 0.039***
(0.010)
Constant 0.767*** 1.150*** 1.216***
(0.178) (0.208) (0.214)
All controls YES YES YES
Year dummies YES YES YES
Observations 1382 1382 1382
R 2 0.254 0.174 0.179
Note: This table presents the results of the role of competition and board structure in the relationship between CEO expertise power and Bank diversification with robust standard errors. In column (1), we report the model for competition and in column (2) we report the model for board structure. The robust standard errors are presented in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. All variables are defined in the Appendix 1.
