CEO Narcissism Score
Measuring personality is challenging since it is not as easy to observe as an individual’s financial position, socioeconomic background, education, or age. Instead of self-reported measurements, the use of archival data offers the major advantage in that data can be compiled independently of the CEO’s available time and willingness to cooperate. To capture the narcissism trait, we build on the CEO Narcissism Score (CNS) proposed by Rijsenbilt (
2011). There are several reasons why we choose this model over others that employ just one indicator such as first-person pronoun usage (Aktas et al.
2016), signature size (Ham et al.
2017), or ratings of video samples of CEOs (Petrenko et al.
2016). Closest to our study is the measurement proposed by Chatterjee and Hambrick (
2007) with a set of five indicators, which we explain in more detail later in this section. Some studies have adjusted this model and use a set of three (Olsen et al.
2014) or four indicators (Oesterle et al.
2016; Gerstner et al.
2013; Engelen et al.
2016). To the best of our knowledge, then, the defined model in this study uses the largest set of indicators to measure CEO narcissism and simultaneously mitigates a potential bias due to a single indicator’s weak ability to illustrate a specific trait. Furthermore, the CNS reflects the four core dimensions of narcissism: leadership/authority, superiority/arrogance, self-absorption/self-admiration, and exploitativeness/entitlement, which are theoretically grounded in the work of Emmons (
1987). Finally, the CNS has been empirically demonstrated to reveal a relationship between narcissism and financial reporting. Specifically, Rijsenbilt and Commandeur (
2013) identify a narcissistic CEO’s higher propensity to engage in fraud.
The CNS consists of fifteen indicators reflecting five determinants. These are (1) media exposure, (2) perquisites, (3) compensation, (4) power, and (5) growth. The wide range of determinants is chosen to adequately reflect the distinctive pattern of a CEO’s narcissistic trait since it affects their idiosyncratic actions.
First, excessive media exposure helps narcissistic CEOs to gain public acknowledgement and reinforcement. The exposure indicators are number of awards and number of lines in their biography as listed in the Marquis Who’s Who database. The number of publications in major news outlets is taken from Dow Jones Factiva and reflects the number of joint CEO/firm mentions. In addition, the size of the CEO’s photograph and its placement in the annual report are measured on a twelve-point scale. The score is one if there is no photograph of the CEO in the annual report and twelve if the CEO is pictured alone on a full page plus on an additional photograph elsewhere in the report. Where there is no annual report, the score is zero.
The second determinant, perquisites, consists of the personal use of the corporate jet since this reflects a CEO’s status and grandeur. The value of private jet use, expressed in U.S. dollars, is taken from Form DEF 14A, which is downloadable via the U.S. Security Exchange Commission’s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system. When no disclosure is available, the value is zero.
The third determinant, compensation, contains five variables indicating a CEO’s self-importance. Cash and total compensation data are taken from Compustat’s ExecuComp database and consist of the CEO’s salary and bonus as the cash component, plus all other forms of dollar-denominated compensation to determine the total package. The compensation structure within a firm can be interpreted as an appraisal system in which a larger compensation package expresses higher hierarchical status and prestige. Since the potential of a CEO to influence the compensation structure is high, relative cash and total compensation is also calculated. Relative compensation is derived from the ratio of the CEO’s compensation to that of the second-best paid executive. Finally, the CEO’s rank is measured as the ordinal rank, with the highest compensation ranked one, and higher ranks expressing less narcissism.
Fourth, narcissistic CEOs strive for attention and overestimate their own abilities. To cope with their personality traits, they centralize decision-making power, which is reflected in CEO duality, a higher number of role titles, and weaker CG. The variable for CEO duality takes the value of one if a CEO is also the Chairman of the Board. The number of role titles ranges from one to five and includes CEO, Chairman, Founder, President, Director, or Principal Executive. The data for the two variables are taken from Compustat’s ExecuComp database. The originally applied Gompers Index—a proxy for shareholder rights—is limited since it is only available up to 2006, so we replace it by Bebchuk et al.’s (
2009) entrenchment index measuring six actions that limit shareholder rights: staggered boards, limits to shareholder bylaw amendments, poison pills, golden parachutes, supermajority requirements for mergers, and charter amendments. These six actions are seen as the most relevant from the originally applied Gompers Index (Bebchuk et al.
2009) and they are available throughout our sample period from 1992 to 2012. For 2007 onwards, the variable referring to the supermajority requirement for mergers takes the value of one if a majority of more than 50% is required. This results in a final score ranging from zero to six, reflecting the number of provisions a firm has invoked.
Fifth, to corroborate their superiority, narcissistic CEOs tend to acquire other companies. To account for this behavior, the number and value of acquisitions are taken from the Thomson One Banker SDC database. An acquisition is taken into account if the acquiring firm purchases more than 50% of the target shares and the deal value is at least US$10 million. The value is scaled by the market value of the acquirer. Both variables are cumulated and divided by the years of overall tenure and set to zero if no data for acquisitions are available.
An overview of the indicators, including the data source and description, is given in Table
1. All companies in the S&P500 index are included for which data from the various databases for the period 1992 to 2012 is available. To ensure CEOs can unfold their narcissistic trait in full, a minimum tenure of 3 years is required. This requirement is chosen with respect to the CEO life cycle (Hambrick and Fukutomi
1991). A Principal Components Analysis (PCA) of these fifteen variables is performed based on the correlation matrix. PCA is a data reduction method used to re-express multivariate data with fewer dimensions. The goal is to re-orient the data so that the multitude of original variables can be summarized in just a few components that capture the maximum possible information from the original variables. In line with Rijsenbilt (
2011) and to ensure comparable results, we use four components. Accordingly, the five aforementioned determinants result in four factors representing leadership/authority, superiority/arrogance, self-absorption/self-admiration, and exploitativeness/entitlement. The factor loadings matrix is rotated in order to keep only a few factor loadings large. This simplifies the structure and allows us to easily interpret factors as clusters of variables that are highly correlated with a particular factor. The oblique rotation method is chosen since factors are not expected to be uncorrelated.
Table 1
Components of the CEO narcissism score
Publicity | Dow Jones Factiva | Number of publications divided by the number of tenure years |
Awards | Marquis Who’s Who | Number of awards |
Lines in biography | Marquis Who’s Who | Number of lines in biography |
Photograph | Annual report | Size of CEO’s photograph in annual report |
Cash compensation | Compustat’s ExecuComp | Salary and bonus for every fiscal year |
Total compensation | Compustat’s ExecuComp | Cash plus all other forms of compensation for every fiscal year |
Ratio of cash compensation | Compustat’s ExecuComp | CEO’s cash compensation compared to second-best paid executive |
Ratio of total compensation | Compustat’s ExecuComp | CEO’s total compensation compared to second-best paid executive |
Compensation rank | Compustat’s ExecuComp | Executive rank by salary and bonus |
Corporate jet use | EDGAR—Form DEF 14A | Amount in $ of personal use of corporate aircraft |
CEO duality | Compustat’s ExecuComp | CEO is also chairman |
Role titles | Compustat’s ExecuComp | Number of titles (CEO | President | COO | Chairman | Director | Principal Executive | Founder) |
Shareholder rights | Institutional Shareholder Services (ISS) | Bebchuk et al.’s ( 2009) entrenchment index (E index) based on six provisions: staggered boards, limits to shareholder bylaw amendments, poison pills, golden parachutes, and supermajority requirements for mergers as well as charter amendments |
Value of acquisitions | Thomson One Banker (SDC) | Amount in $ per CEO tenure year |
Number of acquisitions | Thomson One Banker (SDC) | Number per CEO tenure year |
Although our model to measure narcissism has already been applied in prior research, so far it has not been validated in a different context. Rijsenbilt (
2011) provides only weak evidence of construct validity by comparing the CNS of five highly narcissistic CEOs in her sample with the narcissistic CEOs identified by Rosenthal and Pittinsky (
2006). To further validate the CNS, we reconstruct the CEO narcissism score by Chatterjee and Hambrick (
2007), which has been externally validated and widely accepted in research. They use a set of five indicators to reflect the narcissistic construct using archival data: first, the size of the CEO’s photograph in the company’s annual report; second, the frequency of a CEO mention in the company’s press releases; third, the CEO’s use of first-person singular pronouns in interviews; fourth and fifth, the CEO’s ratio of cash and non-cash compensation to that of the second-highest-paid executive in the firm. We do this for a subsample of 436 CEOs and subsequently rate CEO narcissism for this subsample using the approach established by Chatterjee and Hambrick (
2007). We find that our CNS is significantly correlated with the narcissism measure defined by Chatterjee and Hambrick (
2007), with a correlation coefficient of 0.53 (
p < 0.001). These results are assurance that our scoring approach provides a reasonable measure of CEO narcissism.
Earnings Management
To measure the effect of the association between CEO narcissism and ABEM, we build on the cross-sectional modified Jones model (DeFond and Subramanyam
1998). We rely on a cross-sectional model in order to avoid the data loss inherent in a time-series approach. In addition, the model is adjusted for performance as proposed by Kothari et al. (
2005) by including return on assets in the prior year as an additional regressor in model (1), since extreme past performance has a mechanical relationship with accrual estimates. The following model is estimated based on the two-digit Standard Industry Classification (SIC) when there are 20 or more companies available per industry and year. To increase the model fit, discretionary accruals are identified as the error term from regressions, based on the one-digit SIC when fewer than 20 companies per industry and year are allocable (Lee and Masulis
2011):
$${\text{TA}}_{it} = \alpha_{0} + \alpha_{1} \left( {\Delta {\text{REV}}_{it} - \Delta {\text{REC}}_{it} } \right) + \alpha_{2} {\text{PPE}}_{it} + a_{3} {\text{IBXI}}_{it - 1} + \varepsilon_{it} ,$$
(1)
where TA
it are the total accruals for firm
i in year
t, and measured as the change in working capital, excluding the current portion of long-term debt minus depreciation and amortization. In addition, ΔREV
it is the change in revenues for firm
i in year
t,
ΔREC
it is the change in receivables for firm
i in year
t, PPE
it is PPE for firm
i in year
t, and IBXI
it is income before extraordinary items for firm
i in year
t. All variables—including the intercept term in Eq. (
1)—are scaled by lagged total assets. Discretionary accruals are first estimated for all available company years in Compustat (149,373 observations) for the period 1992 to 2012 and merged afterwards with existing data on the CNS. This step enhances the reliability of inferences drawn from discretionary accruals estimates.
The model proposed by DeFond and Subramanyam (
1998) refers to the change in balance sheet items to calculate accruals and is hence allocated to the balance sheet approach. This approach can be weakened by non-operating events such as mergers and acquisitions (M&A) and exhibits discretionary accruals, although these are not caused by management’s pure discretion. Specifically, acquisitions lead to changes in balance sheet items but do not affect the income statement which finally causes immoderate accruals (Hribar and Collins
2002). By contrast, the model proposed by Dechow and Dichev (
2002) and amended by McNichols (
2002) refers directly to items in the operating section of the statement of cash flows. Thus, this model is not affected by non-operating changes in balance sheet items and is accordingly allocated to the cash flow approach. Again, the following model is estimated based on the two-digit SIC. When there are fewer than 20 companies available per industry and year, we estimate Eq. (
2) based on the one-digit SIC:
$$\Delta {\text{WC}}_{it} = \alpha_{0} + \alpha_{1} {\text{CFO}}_{it - 1} + \alpha_{2} {\text{CFO}}_{it} + a_{3} {\text{CFO}}_{it + 1} + \alpha_{4} \Delta {\text{REV}}_{it} + \alpha_{5} {\text{PPE}}_{it} + \varepsilon_{it} ,$$
(2)
where ΔWC
it is the change in working capital accruals for firm
i in year
t, measured as the sum of changes in accounts receivable (recch), the change in inventory (invch), the change in accounts payable (apalch), the change in taxes payable (txach), and change in other assets (aoloch). In addition, ΔWC
it is multiplied by minus one, and CFO is cash from operations (oancf). All variables—except the intercept term in Eq. (
2)—are scaled by average total assets. Unlike Dechow and Dichev (
2002), we do not rely on the standard deviation of these residuals as a measure of ABEM. We employ signed accruals to distinguish between increasing and decreasing ABEM. The proxies for working capital accruals are estimated for all available company years in the period 1992 to 2012, resulting in 127,966 observations.
To control for the finding that managers opt for one of the two earnings management choices—namely ABEM or RAM—according to their personal preferences (Zang
2012), we additionally calculate three proxies for RAM, namely abnormal cash flows from operations (AB_CFO), abnormal production costs (AB_PROD), and abnormal discretionary expenses (AB_EXP). Given management’s intention to increase reported earnings by granting discounts, total sales increase but cash inflow per sale decreases, leading to lower AB_CFO. AB_PROD will instead increase if management decides to capitalize rather than expend per-unit fixed costs by raising production levels. Finally, management may postpone period costs such as advertisement, research and development, or selling, general and administrative expenses, leading to decreasing AB_EXP for a given year (Cohen et al.
2008; Roychowdhury
2006). To generate the combined impact of RAM (COM_RAM), we multiply
AB_CFO and
AB_EXP by minus one. Thus, all proxies increase with higher levels of RAM. Accordingly, COM_RAM is defined as
AB_CFO plus
AB_PROD plus
AB_EXP. Again, the proxies for RAM are estimated for all available company years, resulting in 146,185 observations for COM_RAM.
Control Variables
Several variables are included to control for effects other than that of our variable of interest—the CNS or more specifically, its extreme decile—on accruals. At first, tenure, age, and gender are included, since according to upper echelons theory these may also affect personal behavior. It is found that long-tenured CEOs tend to refrain from making severe changes, which is known as the “stale in the saddle” paradigm (Miller
1991). Supporting this paradigm, Barker and Mueller (
2002) find that CEO tenure is negatively associated with a firm’s research and development (R&D) expenses. These researchers also find that CEO age negatively correlates with R&D expenses, indicating a lower willingness among long-tenured and older CEOs to take risks. In addition, it is found that older CEOs make more diversifying acquisitions, manage firms with more diversified operations, and maintain lower operating leverage (Serfling
2014). It is reported that female board members are more risk-averse and act more conservatively than their male counterparts, resulting in the (attempted) avoidance of earnings management (Adams and Ferreira
2009; Ho et al.
2015). Furthermore, Ingersoll et al. (
2017) find that female CEOs are less likely to exhibit a narcissistic trait than their male counterparts.
Since Petrenko et al. (
2016) find that CEO narcissism has positive effects on levels and profile of corporate social responsibility (CSR), we consider a measure of CSR. We further propose that CSR is associated with earnings management in two ways. On the one hand, it restricts managerial ability to manage earnings; on the other, it can also be used to satisfy different stakeholder groups in order to obtain more managerial discretion (Martínez-Ferrero and García-Sánchez
2015; Kim et al.
2012). To operationalize CSR, we include the standardized value of the sum of total strengths minus total concerns for the sub-dimensions community, diversity, employees, environment, and product, as taken from MSCI ESG STATS, formerly known as the Kinder, Lynderberg, and Domini database. Thus, higher levels of the proxy variable indicate higher levels of CSR.
To complement CSR, we include an aggregated CG measure since Cornett et al. (
2008) find that effective CG reduces management’s discretion to manage earnings. The aggregated CG measure is derived from the CG dimension taken from MSCI ESG STATS. The CG measure refers to a firm’s sustainability reporting quality, its support for public policies, its business ethics, governance practices, and executive compensation. The final element is of particular importance for this study since prior literature states that performance-based compensation is a vital incentive for CEOs to manage earnings, which may also lead to misreporting and increased litigation risk (Burns and Kedia
2006; Holthausen et al.
1995; Bergstresser and Philippon
2006). We measure CG as the difference between the strengths and concerns in the CG dimension of the MSCI ESG STATS database. Again, higher levels of the proxy variable indicate higher levels of overall CG.
Since executive compensation captures a predominant value of the CNS, our results may be driven by exaggerated compensation. Therefore, considering the CG measure helps to control for a possible mechanical relationship between the CNS construction and discretionary accruals. A mechanical relationship may also be established by considering the number and value of acquisitions in the CNS. Hribar and Collins (
2002) argue that discretionary accruals measured by a model relying on the balance sheet approach correlate with M&A transactions in a given year. Thus, a higher number and value of acquisitions result in a higher CNS—all else being equal—and, accordingly, in higher discretionary accruals. To control our results concerning this mechanical relationship—beside the additional model to measure accruals with the cash flow approach—we include a binary variable which takes the value of one if an M&A transaction is indicated in Compustat’s revenue footnote one for a given year.
The natural logarithm of market capitalization and market-to-book ratio are also included as proxies for firm size and growth opportunity (Roychowdhury
2006). Leverage is included because it has been established that companies manage earnings to avoid debt covenant violations (DeFond and Jiambalvo
1994). Empirical evidence suggests that companies report lower levels of discretionary accruals if they are audited by one of the big auditing firms (Becker et al.
1998; Francis et al.
1999). Therefore, a binary variable is included which takes the value of one if the company is audited by one of the big auditors. Companies planning a seasoned equity offering (SEO) in the near future strive to present their financial status in the best light possible (Rangan
1998; Teoh et al.
1998). Therefore, a binary variable is included which takes the value of one if the company issues equity in the following year. A company’s age is measured by the first entry of the time series in the Center for Research and Security Prices to control for different development stages of the firm.
All continuous variables are winsorized at the top and bottom 1% level, except in the case of the absolute value of discretionary accruals, which is only winsorized at the top 1% level, respectively. An overview of the dependent and independent variables is provided in Table
2.
Table 2
Variable description
Discretionary accruals (DA) | Compustat | Discretionary accruals (cross-sectional modified Jones model adjusted for performance) |
Working capital accruals (WCA) | Compustat | Working capital accruals are calculated based on the model proposed by Dechow and Dichev ( 2002) and modified by McNichols ( 2002) |
Abnormal cash flow from operations (AB_CFO) | Compustat | AB_CFO is defined as the residual from normal CFO as a linear function of sales and change in sales. The variable is multiplied by minus one so that a positive coefficient indicates increasing earnings management |
Abnormal production costs (AB_PROD) | Compustat | AB_PROD is defined as the residual from cost of goods sold and change in inventory as a linear function of contemporaneous sales |
Abnormal discretionary expenses (AB_EXP) | Compustat | AB_EXP is defined as the residual from discretionary expenses as a function of lagged sales. The variable is multiplied by minus one so that a positive coefficient indicates increasing earnings management |
Real activities management (COM_RAM) | Compustat | AB_CFO + AB_PROD + AB_EXP |
CEO tenure | Compustat’s ExecuComp | Logarithm of full tenure in years |
CEO age | Compustat’s ExecuComp | Age in years |
CEO gender | Compustat’s ExecuComp | A binary variable taking the value of one if the CEO is female |
CSR Z-score | MSCI ESG STATS | Standardized value of the sum of total strengths minus total concerns for the sub-dimensions: community, diversity, employees, environment, product |
Corporate governance score | MSCI ESG STATS | Total strengths minus total concerns for the sub dimension: corporate governance |
Acquisition | Compustat | A binary variable which takes the value of one if an M&A transaction is indicated in Compustat’s revenue footnote one |
Size | Compustat | Logarithm of total assets as a proxy for firm size |
Market-to-book ratio | Compustat | Market value divided by book value of equity |
Leverage | Compustat | Debt-to-assets ratio as a proxy for leverage |
BIG auditing firm | Compustat | A binary variable taking the value of one if the company is audited by a Big auditor |
Seasoned equity offering | Thomson One Banker | A binary variable taking the value of one if the company issues equity in the following year |
Firm age | CRSP | One plus the natural logarithm of years since the company is listed in the CRSP database |
Table 3
Descriptive statistics for the full sample
Discretionary accruals (absolute value) | 6939 | 0.15 | 0.37 | 0.00 | 0.02 | 0.04 | 0.11 | 2.64 |
Discretionary accruals (signed) | 6939 | 0.03 | 0.34 | − 1.01 | − 0.04 | 0.00 | 0.05 | 2.23 |
Discretionary accruals (positive) | 3606 | 0.17 | 0.38 | 0.00 | 0.02 | 0.04 | 0.12 | 2.23 |
Discretionary accruals (negative) | 3333 | − 0.11 | 0.19 | − 1.01 | − 0.11 | − 0.04 | − 0.02 | 0.00 |
Working capital accruals (absolute value) | 6736 | 0.03 | 0.03 | 0.00 | 0.01 | 0.02 | 0.04 | 0.14 |
Working capital accruals (positive) | 3175 | 0.03 | 0.02 | 0.00 | 0.01 | 0.02 | 0.04 | 0.11 |
Working capital accruals (negative) | 3561 | − 0.03 | 0.03 | − 0.11 | − 0.04 | − 0.02 | − 0.01 | 0.00 |
Real activities management | 6939 | − 0.06 | 1.13 | − 5.58 | − 0.27 | 0.00 | 0.24 | 4.83 |
CEO narcissism score | 6939 | 0.00 | 2.71 | − 10.44 | − 1.81 | − 0.13 | 1.67 | 19.30 |
CEO narcissism score (10th decile) | 6939 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
CEO tenure (ln) | 6939 | 1.93 | 0.68 | 0.69 | 1.39 | 1.95 | 2.40 | 3.89 |
CEO age | 6939 | 56.35 | 6.42 | 34.00 | 52.00 | 57.00 | 61.00 | 85.00 |
CEO gender | 6939 | 0.02 | 0.13 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
CSR Z-score | 6939 | 0.49 | 1.45 | − 4.03 | − 0.42 | 0.48 | 1.38 | 7.68 |
Corporate governance score | 6939 | − 0.47 | 0.77 | − 4.00 | − 1.00 | 0.00 | 0.00 | 2.00 |
Acquisition | 6939 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
Size (ln) | 6939 | 9.08 | 1.20 | 6.44 | 8.28 | 9.02 | 9.79 | 12.21 |
Total assets (in $ million) | 6939 | 34,020 | 130,000 | 189 | 3,255 | 8,037 | 21,625 | 2,400,000 |
Market-to-book ratio | 6939 | 3.59 | 3.53 | − 3.28 | 1.69 | 2.64 | 4.17 | 22.25 |
Leverage | 6939 | 0.60 | 0.20 | 0.14 | 0.46 | 0.60 | 0.73 | 1.02 |
BIG auditor | 6939 | 0.91 | 0.29 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Seasoned equity offerings | 6939 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
Firm age (ln) | 6939 | 4.33 | 0.81 | 1.00 | 3.89 | 4.47 | 4.91 | 5.45 |
Table 4
Descriptive statistics by firms with highly narcissistic CEOs versus control firms
Discretionary accruals (absolute value) | 0.15 | 0.04 | 0.21 | 0.06 | − 3.83 | − 5.66 |
Discretionary accruals (signed) | 0.03 | 0.00 | 0.04 | 0.00 | − 0.06 | 1.66 |
Discretionary accruals (positive) | 0.16 | 0.04 | 0.23 | 0.06 | − 2.75 | − 4.19 |
Discretionary accruals (negative) | − 0.11 | − 0.04 | − 0.14 | − 0.06 | 2.98 | 3.87 |
Real activities management | − 0.05 | 0.00 | − 0.13 | 0.03 | 1.72 | − 2.10 |
CEO narcissism score | − 0.56 | − 0.46 | 5.05 | 4.50 | − 66.65 | − 43.45 |
CEO narcissism score (10th decile) | 0.00 | 0.00 | 1.00 | 1.00 | − 226.10 | − 83.14 |
CEO tenure (ln) | 1.92 | 1.95 | 1.99 | 1.95 | − 2.69 | − 2.55 |
CEO age | 56.30 | 57.00 | 56.83 | 57.00 | − 2.08 | − 1.98 |
CEO gender | 0.02 | 0.00 | 0.01 | 0.00 | 0.56 | 0.56 |
CSR Z-score | 0.49 | 0.48 | 0.48 | 0.03 | 0.17 | 1.81 |
Corporate governance score | − 0.45 | 0.00 | − 0.69 | − 1.00 | 8.10 | 8.78 |
Acquisition | 0.21 | 0.00 | 0.35 | 0.00 | − 8.73 | − 8.68 |
Size (ln) | 9.00 | 8.96 | 9.80 | 9.71 | − 17.12 | − 14.99 |
Market-to-book ratio | 3.56 | 2.64 | 3.79 | 2.73 | − 1.60 | − 2.23 |
Leverage | 0.60 | 0.60 | 0.61 | 0.60 | − 1.64 | − 1.45 |
BIG auditor | 0.91 | 1.00 | 0.95 | 1.00 | − 4.17 | − 4.16 |
Seasoned equity offerings | 0.08 | 0.00 | 0.10 | 0.00 | − 1.41 | − 1.41 |
Firm age (ln) | 4.31 | 4.47 | 4.44 | 4.62 | − 3.95 | − 5.59 |
(1) | Discretionary accruals (absolute value) | 1 | | | | | | | | | | | | | | | | | | |
(2) | Discretionary accruals (signed) | 0.50 | 1 | | | | | | | | | | | | | | | | | |
(3) | Discretionary accruals (positive) | 1.00 | 1.00 | 1 | | | | | | | | | | | | | | | | |
(4) | Discretionary accruals (negative) | − 0.91 | 1.00 | . | 1 | | | | | | | | | | | | | | | |
(5) | Real activities management | 0.05 | 0.01 | 0.03 | − 0.09 | 1 | | | | | | | | | | | | | | |
(6) | CEO narcissism score | 0.06 | 0.02 | 0.06 | − 0.07 | − 0.02 | 1 | | | | | | | | | | | | | |
(7) | CEO narcissism score (10th decile) | 0.05 | 0.00 | 0.05 | − 0.05 | − 0.02 | 0.62 | 1 | | | | | | | | | | | | |
(8) | CEO tenure (ln) | 0.00 | − 0.02 | − 0.01 | − 0.02 | − 0.01 | 0.03 | 0.03 | 1 | | | | | | | | | | | |
(9) | CEO age | − 0.03 | 0.00 | − 0.04 | 0.03 | 0.02 | 0.06 | 0.03 | 0.42 | 1 | | | | | | | | | | |
(10) | CEO gender | 0.06 | 0.03 | 0.07 | − 0.05 | − 0.01 | 0.00 | − 0.01 | − 0.06 | − 0.08 | 1 | | | | | | | | | |
(11) | CSR Z-score | 0.08 | 0.04 | 0.11 | − 0.07 | − 0.03 | 0.03 | 0.00 | 0.01 | − 0.04 | 0.13 | 1 | | | | | | | | |
(12) | Corporate governance score | − 0.06 | − 0.03 | − 0.07 | 0.05 | − 0.01 | − 0.12 | − 0.10 | − 0.01 | 0.03 | − 0.01 | 0.15 | 1 | | | | | | | |
(13) | Acquisition | 0.04 | 0.02 | 0.04 | − 0.03 | 0.01 | 0.15 | 0.10 | 0.02 | − 0.01 | − 0.01 | − 0.02 | − 0.03 | 1 | | | | | | |
(14) | Size (ln) | 0.11 | 0.04 | 0.12 | − 0.12 | − 0.05 | 0.28 | 0.20 | 0.00 | 0.03 | 0.03 | 0.25 | − 0.15 | 0.04 | 1 | | | | | |
(15) | Market-to-book ratio | 0.06 | 0.03 | 0.08 | − 0.06 | − 0.14 | 0.01 | 0.02 | 0.02 | − 0.05 | 0.03 | 0.12 | − 0.04 | 0.01 | 0.25 | 1 | | | | |
(16) | Leverage | − 0.04 | − 0.02 | − 0.05 | 0.06 | 0.10 | 0.12 | 0.02 | − 0.09 | 0.07 | 0.03 | − 0.03 | − 0.04 | − 0.06 | 0.00 | − 0.01 | 1 | | | |
(17) | BIG auditor | 0.04 | 0.02 | 0.04 | − 0.05 | − 0.02 | 0.08 | 0.05 | − 0.01 | − 0.07 | 0.04 | 0.09 | − 0.05 | 0.03 | 0.15 | 0.02 | − 0.01 | 1 | | |
(18) | Seasoned equity offerings | − 0.01 | − 0.01 | − 0.01 | 0.01 | 0.04 | 0.05 | 0.02 | 0.01 | − 0.02 | − 0.02 | − 0.02 | − 0.04 | 0.03 | − 0.01 | − 0.05 | 0.09 | − 0.02 | 1 | |
(19) | Firm age (ln) | − 0.03 | − 0.03 | − 0.05 | 0.02 | 0.03 | 0.10 | 0.05 | 0.01 | 0.16 | − 0.02 | − 0.01 | 0.13 | − 0.04 | 0.11 | − 0.02 | 0.18 | − 0.07 | − 0.04 | 1 |
To mitigate concerns of an omitted variable bias, we control for the influence of CEO optimism (Campbell et al.
2004b; Schrand and Zechman
2012), insider and blockholder ownership (Cheng and Warfield
2005; Shleifer and Vishny
1986), poor financial status (Altman
1968), a firm’s headquarters location (Leuz et al.
2003), business and operating risk as well as a crisis dummy in additional specifications. In summary, all results remain the same and are available upon request.
Empirical Models
To capture the relationship between ABEM and behaviorally driven accounting choices, we set the following specifications: first absolute, second positive, and third negative discretionary accruals are regressed on CEO narcissism while controlling for RAM, various CEO-specific controls, as well as firm, time, and sector controls. To test our hypotheses, we also include a binary variable as our main variable of interest reflecting the effect of extreme narcissism occurrence.
The narcissistic personality disorder affects around 6% of the entire U.S. population, rising to nearly 8% among men (Stinson et al.
2008). Self-confidence and self-esteem are seen as essential characteristics for being appointed to a CEO position. However, a narcissist’s exaggerated sense of self-worth may often be misinterpreted as self-confidence and inherently helps a narcissistic individual to be selected as CEO (O’Reilly et al.
2014). Accordingly, we expect CEOs to be more narcissistic than the general population and thus set the threshold at 10% to indicate if a CEO is highly narcissistic. The higher threshold is also justified since almost all CEOs in the sample are male. Therefore, the coefficient
\(\alpha_{2}\) is a binary variable, which takes the value of one if a CEO is in the top decile. While the regressions in Table
6 are based on the balance sheet approach, we refer to the cash flow approach in Table
7. Thus, our model to measure the impact of CEO narcissism on ABEM is specified as follows:
Table 6
CEO narcissism and accrual-based earnings management (balance sheet approach)
CEO narcissism score | − 0.000577 | − 0.00462** | − 0.00610** | 0.00269* | 0.000403 | − 0.00488 | − 0.00377 | 0.00380 |
(0.00186) | (0.00208) | (0.00284) | (0.00160) | (0.00324) | (0.00325) | (0.00598) | (0.00269) |
CEO narcissism score (10th decile) | | 0.0560*** | 0.0619** | − 0.0340** | | 0.0739*** | 0.0958** | − 0.0444** |
| (0.0217) | (0.0295) | (0.0134) | | (0.0257) | (0.0443) | (0.0179) |
Real activities management | 0.0179*** | 0.0179*** | 0.0172** | − 0.0158*** | 0.0186*** | 0.0187*** | 0.0175 | − 0.0173*** |
(0.00585) | (0.00585) | (0.00841) | (0.00451) | (0.00678) | (0.00678) | (0.0111) | (0.00640) |
CEO tenure | 0.0189* | 0.0181* | 0.0235* | − 0.00729 | | | | |
(0.0102) | (0.0102) | (0.0130) | (0.00681) | | | | |
CEO age | − 0.00119 | − 0.00117 | − 0.000579 | 0.00105 | | | | |
(0.000962) | (0.000962) | (0.00123) | (0.000674) | | | | |
CEO gender | 0.0321 | 0.0320 | 0.0447 | 0.0147 | | | | |
(0.0426) | (0.0428) | (0.0542) | (0.0276) | | | | |
CSR Z-scoret−1 | 0.0110** | 0.0114*** | 0.0119** | − 0.00472 | 8.79e−05 | − 0.000100 | 0.0166 | 0.00728 |
(0.00432) | (0.00434) | (0.00564) | (0.00302) | (0.00935) | (0.00933) | (0.0139) | (0.00655) |
Corporate governance scoret−1 | 0.00470 | 0.00488 | 0.00313 | − 0.00827 | 0.0111 | 0.0109 | 0.0165 | − 0.00906 |
(0.00767) | (0.00769) | (0.00950) | (0.00507) | (0.0105) | (0.0106) | (0.0138) | (0.00846) |
Acquisition | 0.0237* | 0.0228* | 0.00585 | − 0.0170* | 0.0242 | 0.0237 | − 0.00904 | − 0.0186 |
(0.0126) | (0.0126) | (0.0158) | (0.00970) | (0.0152) | (0.0152) | (0.0200) | (0.0122) |
Size (ln)t−1 | − 0.00302 | − 0.00369 | 0.00440 | 0.00567 | − 0.00646 | − 0.00604 | 0.000445 | 0.00294 |
(0.00589) | (0.00591) | (0.00735) | (0.00398) | (0.0173) | (0.0173) | (0.0298) | (0.0143) |
Market-to-book ratiot−1 | − 0.000947 | − 0.00108 | − 0.00345 | − 0.000599 | 0.000289 | 3.35e−05 | − 0.00435 | 0.00124 |
(0.00235) | (0.00235) | (0.00305) | (0.00122) | (0.00317) | (0.00316) | (0.00548) | (0.00229) |
Leveraget−1 | 0.000634 | 0.00537 | 0.0351 | 0.0250 | 0.0364 | 0.0414 | 0.231 | 0.0736 |
(0.0331) | (0.0330) | (0.0446) | (0.0230) | (0.113) | (0.113) | (0.168) | (0.0893) |
BIG auditing firm | − 0.0397** | − 0.0398** | − 0.0299 | 0.0318** | 0.00120 | 0.00280 | 0.000519 | − 0.0202 |
(0.0198) | (0.0198) | (0.0246) | (0.0137) | (0.0362) | (0.0356) | (0.0667) | (0.0301) |
Seasoned equity offeringst+1 | − 0.0300* | − 0.0284* | − 0.0371* | 0.00794 | − 0.0529** | − 0.0507** | − 0.0298 | 0.0266* |
(0.0162) | (0.0163) | (0.0210) | (0.0112) | (0.0206) | (0.0206) | (0.0289) | (0.0155) |
Firm age (ln) | − 0.00479 | − 0.00501 | − 0.0133 | − 0.00515 | | | | |
(0.00833) | (0.00832) | (0.0107) | (0.00496) | | | | |
Constant | 0.0468 | 0.0175 | − 0.00290 | − 0.0877* | 0.0835 | 0.0690 | − 0.0786 | − 0.112 |
(0.0797) | (0.0841) | (0.129) | (0.0466) | (0.166) | (0.166) | (0.279) | (0.139) |
Observations | 5,813 | 5,813 | 3,047 | 2,766 | 5,813 | 5,813 | 3,047 | 2,766 |
R-squared | 0.216 | 0.218 | 0.282 | 0.284 | 0.054 | 0.056 | 0.105 | 0.093 |
Time controls | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Industry controls | Yes | Yes | Yes | Yes | No | No | No | No |
CEO fixed effects | No | No | No | No | Yes | Yes | Yes | Yes |
Number of CEOs | | | | | 1126 | 1126 | 1015 | 993 |
Table 7
CEO narcissism and accrual-based earnings management (cash flow approach)
CEO narcissism score | − 0.000350 | − 0.000249 | 0.000363 | − 1.15e−05 | 0.000537 | 0.000495 |
(0.000225) | (0.000278) | (0.000294) | (0.000341) | (0.000457) | (0.000454) |
CEO narcissism score (10th decile) | 0.00447*** | 0.00434** | − 0.00419** | 0.00526*** | 0.00496* | − 0.00612** |
(0.00161) | (0.00210) | (0.00207) | (0.00178) | (0.00289) | (0.00250) |
Real activities management | − 0.000115 | − 0.000142 | − 7.75e−05 | 7.12e−05 | 0.000734 | 6.81e−05 |
(0.000370) | (0.000482) | (0.000470) | (0.000380) | (0.000557) | (0.000523) |
CEO tenure | 0.000207 | − 0.000124 | − 0.000429 | | | |
(0.000851) | (0.000962) | (0.00101) | | | |
CEO age | − 3.00e−05 | − 1.02e−05 | 4.18e−05 | | | |
(8.30e−05) | (9.36e−05) | (0.000101) | | | |
CEO gender | 0.00220 | − 0.00109 | − 0.00459 | | | |
(0.00424) | (0.00427) | (0.00562) | | | |
Constant | 0.0639*** | 0.0656*** | − 0.0447*** | 0.0168 | 0.0160 | − 0.00592 |
(0.0134) | (0.0160) | (0.00723) | (0.0166) | (0.0208) | (0.0232) |
Observations | 5,632 | 2,656 | 2,976 | 5,632 | 2,656 | 2,976 |
R-squared | 0.114 | 0.134 | 0.128 | 0.028 | 0.033 | 0.045 |
Firm controls | Yes | Yes | Yes | Yes | Yes | Yes |
Time controls | Yes | Yes | Yes | Yes | Yes | Yes |
Industry controls | Yes | Yes | Yes | No | No | No |
CEO fixed effects | No | No | No | Yes | Yes | Yes |
Number of CEOs | | | | 1125 | 1010 | 1045 |
Table 8
Accrual-based earnings management surrounding CEO turnovers
CEO change * after | − 0.0369 | − 0.166* |
(0.0486) | (0.0940) |
CEO change | 0.00826 | 0.128 |
(0.0213) | (0.0934) |
After | − 0.00111 | 0.0138 |
(0.0276) | (0.0495) |
Real activities management | − 0.0190** | − 0.0233 |
(0.00887) | (0.0186) |
CEO tenure | 0.00689 | |
(0.0163) | |
CEO age | − 0.00106 | |
(0.00154) | |
CEO gender | 0.00522 | |
(0.0680) | |
Constant | − 0.112 | − 1.327*** |
(0.148) | (0.370) |
Observations | 582 | 582 |
R-squared | 0.328 | 0.191 |
Firm controls | Yes | Yes |
Time controls | Yes | Yes |
Industry controls | Yes | No |
CEO fixed effects | No | Yes |
Number of CEOs | | 443 |
$${\text{EM}}_{{{\text{measure}}_{it} }} = \alpha_{0} + \alpha_{1} {\text{Narcissism}}_{it} + \alpha_{2} {\text{Narcissism }}\left( {{\text{Top}} {\text{Decile}}} \right)_{it} + \alpha_{3} {\text{COM}}_{{{\text{RAM}}_{it} }} + \alpha_{4} {\text{Controls}}_{it} + \alpha_{5} {\text{Time}}_{t} + \alpha_{6} {\text{Industry}}_{it} + \varepsilon_{it} .$$
(3)
The model is re-estimated using fixed effects regression so as to avoid misleading inferences resulting from a potential correlation between the unobservable component of the error term and the CNS. It also mitigates the potential criticism that accounting choices are influenced by many actors such as accountants, board members, or auditors who are involved in a company’s financial reporting. The estimation using fixed effects has to be run at the cost of losing variables with a stable mean, like gender or tenure, since they drop out. We also skip the variables representing CEO and firm age, since an observation of a 52-year-old CEO with a mean age of 55 results in the same value as a 62-year-old CEO with a mean age of 65. In both cases, the variable would reflect a relative difference of 3 years, whereas in fact there is an absolute difference of 10 years between these observations. Accordingly, the model controlling for potential firm-specific and unobservable heterogeneity is specified as follows:
$${\text{EM}}_{{{\text{measure}}_{it} }} = \alpha_{0} + \alpha_{1} {\text{Narcissism}}_{it} + \alpha_{2} {\text{Narcissism}} \left( {{\text{Top}} {\text{Decile}}} \right)_{it} + \alpha_{3} {\text{COM}}_{{{\text{RAM}}_{it} }} + \alpha_{4} {\text{Controls}}_{it} + \alpha_{5} {\text{Time}}_{t} + \alpha_{6} {\text{Industry}}_{it} + \eta_{i} + \varepsilon_{it} .$$
(4)
In additional regressions, the CEO’s first year of tenure is excluded to control for a potential bias resulting from anomalies surrounding CEO takeovers and successions (Hazarika et al.
2012; Wilson and Wang
2010). The untabulated results display the same pattern and thus can be seen as robust to this aspect.