CEO profile and earnings quality

Abstract

This paper introduces the PSCORE, which aggregates nine personal characteristics of chief executive officers (CEOs), to signal the quality of earnings. The PSCORE is a composite score based on publicly available data on CEOs. The study reports strong positive relationships between the PSCORE and two different proxies for earnings quality, (1) discretionary accruals and (2) financial statement errors, measured by deviations of the first digits of figures reported in financial statements from those expected by Benford’s Law. Further analyses indicate that the relationships between the PSCORE and the proxies for earnings quality become more pronounced when CEOs have high equity-based compensation incentives. The findings have some implications for practitioners.

Appendix: Variable definitions

Variable	Definition
Individual factors of the PSCORE
pAGE	Equals one if either (1) the age of the CEO is less than or equal to the 25th percentile of the industry-year (identified by Datastream Level 6) or (2) the CEO is 1 year or less from retirement age, and zero otherwise. The retirement ages of men and women in the UK are 65 and 60, respectively, for the period from 1948 to 2010; from April 2010 to March 2020, the retirement age of women increases by one month every month until it reaches 65 (Bozio et al. 2010)
pCERT	Equals one if the CEO does not have an MBA or CPA equivalent, and zero otherwise. CPA equivalent is defined as a professional accounting certification issued by one of the five qualifying bodies currently accredited by the Financial Reporting Council (FRC 2016): Association of International Accountants (AIA), Chartered Certified Accountants (ACCA), Chartered Accountants Ireland (CAI), Institute of Chartered Accountants in England and Wales (ICAEW), Institute of Chartered Accountants of Scotland (ICAS) (or international equivalent certifications)
pCFO	Equals one if the CEO does not have work experience as a chief financial officer, and zero otherwise
pCHAIRMAN	Equals one if the CEO serves as the chairperson of the board of directors of the firm, and zero otherwise
pEARLY	Equals one if the CEO is within their first three years of service at the firm, and zero otherwise
pFOUNDER	Equals one if the CEO is the founder or a co-founder of the firm, and zero otherwise
pPRESS	Equals one if the number of newspapers simultaneously citing the name of the CEO and the company they work for in the year question is less than the corresponding industry mean (identified by Datastream Level 6), and zero otherwise
pROA	Equals one if the average of the industry-adjusted return on assets (aveROA) during the last 3 years of the CEO’s tenure is negative, and zero otherwise, where aveROA is (1) the average of the industry-adjusted return on assets in years t, t − 1 and t-2 if the CEO is in their third year of tenure, or (ii) the average of the industry-adjusted return on assets in years t and t − 1 if the CEO is in their second year of tenure, or (3) the industry-adjusted return on assets in year t if the CEO is in their first year of tenure. Return on assets equals net income before extraordinary items divided by total assets
pROLE	Equals one if the number of years the CEO has been working as the CEO of their current firm is less than the corresponding industry-year mean (identified by Datastream Level 6), and zero otherwise
PSCORE	$= {\text{pCFO}} + {\text{pCERT}} + {\text{pROLE}} + {\text{pPRESS}} + {\text{pROA}} + {\text{pEARLY}} + {\text{pFOUNDER}} + {\text{pCHAIRMAN}} + {\text{pAGE}}$
PSCORE_O	PSCORE with CEO overconfidence: $PSCORE\_O = PSCORE + CEO\_OVERCONFIDENCE$ where CEO_OVERCONFIDENCE is CEO-level overconfidence. We first calculate the measure of firm-level overconfidence (Campbell et al. 2011; Schrand and Zechman 2012; Kim et al. 2016), denoted by OVERCONFIDENCE, which has a value of one if a firm-year observation meets at least three out of the following five conditions: (1) INVEST is ranked in the top industry-year (Datastream Level 6) quartile. INVEST is excess investment, which is the residual of the regression: $\frac{{Total Assets_{i,t} - Total Assets_{i,t - 1} }}{{Total Assets_{i,t - 1} }} = \alpha + \beta \frac{{Sale_{i,t} - Sale_{i,t - 1} }}{{Sale_{i,t - 1} }} + \varepsilon$ (2) ACQUISITION is ranked in the top industry-year (Datastream Level 6) quartile. ACQUISITION is equal to net acquisition (cash flow statements) scaled by total assets (3) The debt-to-equity ratio is ranked in the top industry-year (Datastream Level 6) quartile. The debt-to-equity ratio is equal to the sum of long-term debts and short-term debts divided by the market value of equity (4) Either convertible debt is greater than zero or preferred stock is greater than 0 (5) Zero dividend yield Otherwise, it has a value of zero. Following Campbell et al. (2011) and Kim et al. (2016), CEO-level overconfidence takes a value of one starting from the first year in which the CEO’s firm has a score for OVERCONFIDENCE equal to one
Proxies for earnings quality
DAC	Absolute values of discretionary total accruals (DAC), estimated by the modified-Jones models (Jones 1991; Dechow et al. 1995) with at least ten observations for each industry-year (Datastream Level 6). $DAC_{i,t} = \left| {\frac{{AC_{i,t} }}{{A_{i,t - 1} }} - \left[ {\hat{\alpha } + \hat{\beta }_{1} \left( {\frac{1}{{A_{i,t - 1} }}} \right) + \hat{\beta }_{2} \left( {\frac{{\Delta REV_{i,t} - \Delta REC_{i,t} }}{{A_{i,t - 1} }}} \right) + \hat{\beta }_{3} \varvec{ }\left( {\frac{{PPE_{i,t} }}{{A_{i,t - 1} }}} \right)} \right]} \right|$; where $\hat{\alpha },\; \hat{\beta }_{1} ,\;\hat{\beta }_{2} ,\;\hat{\beta }_{3}$ are coefficients estimated by the model: $\frac{{AC_{i,t} }}{{A_{i,t - 1} }} = \alpha + \beta_{1} \left( {\frac{1}{{A_{i,t - 1} }}} \right) + \beta_{2} \left( {\frac{{\Delta REV_{i,t} }}{{A_{i,t - 1} }}} \right) + \beta_{3} \varvec{ }\left( {\frac{{PPE_{i,t} }}{{A_{i,t - 1} }}} \right) + \varepsilon_{i,t}$; $AC_{i,t}$ is total accruals, which equals net income before extraordinary items minus net cash flows from operations; $A_{i,t - 1}$ is total assets of firm i at end of year t–1; $\Delta REV_{i,t}$ and $\Delta REC_{i,t}$ are change in sales and change in receivables from year t–1 to year t of firm i, respectively; $PPE_{i,t}$ is gross plant, property and equipment of firm i at end of year t
DAMP	Absolute values of discretionary working capital accruals (DAMP) estimated by the margin model of Peasnell et al. (2000) with at least ten observations for each industry-year (Datastream Level 6). DAMP are the absolute values of the residuals of the following regression: $\frac{{WAC_{i,t} }}{{A_{i,t - 1} }} = \alpha + \beta_{1} \left( {\frac{{REV_{i,t} }}{{A_{i,t - 1} }}} \right) + \beta_{2} \left( {\frac{{REV_{i,t} - \Delta REC_{i,t} }}{{A_{i,t - 1} }}} \right) + \varepsilon_{i,t} ;$ $WAC_{i,t}$ is working capital accruals, $WAC_{i,t} = \left( {\Delta CA_{i,t} - \Delta CHE_{i,t} } \right) - \left( {\Delta CL_{i,t} - \Delta STD_{i,t} } \right)$ [$\Delta CA_{i,t}$ is change in current assets; $\Delta CHE_{i,t}$ is change in cash and cash equivalents; $\Delta CL_{i,t}$ is change in current liabilities; $\Delta STD_{i,t}$ is change in short-term debts]; $A_{i,t - 1}$ is total assets of firm i at end of year t–1; $REV_{i,t}$ is sales of firm i in year t; $\Delta REC_{i,t}$ is receivables from year t–1 to year t of firm i
DWAC	Absolute values of discretionary working capital accruals (DWAC) estimated by the modified-Jones models (Jones 1991; Dechow et al. 1995) with at least ten observations for each industry-year (Datastream Level 6). $DWCA_{i,t} = \left| {\frac{{WAC_{i,t} }}{{A_{i,t - 1} }} - \left[ {\hat{\alpha } + \hat{\beta }_{1} \left( {\frac{1}{{A_{i,t - 1} }}} \right) + \hat{\beta }_{2} \left( {\frac{{\Delta REV_{i,t} - \Delta REC_{i,t} }}{{A_{i,t - 1} }}} \right) + \hat{\beta }_{3} \varvec{ }\left( {\frac{{PPE_{i,t} }}{{A_{i,t - 1} }}} \right)} \right]} \right|$; where $\hat{\alpha },\; \hat{\beta }_{1} ,\;\hat{\beta }_{2} ,\;\hat{\beta }_{3}$ are coefficients estimated by the model: $\frac{{WAC_{i,t} }}{{A_{i,t - 1} }} = \alpha + \beta_{1} \left( {\frac{1}{{A_{i,t - 1} }}} \right) + \beta_{2} \left( {\frac{{\Delta REV_{i,t} }}{{A_{i,t - 1} }}} \right) + \beta_{3} \varvec{ }\left( {\frac{{PPE_{i,t} }}{{A_{i,t - 1} }}} \right) + \varepsilon_{i,t}$; $WAC_{i,t}$ is working capital accruals, $WAC_{i,t} = \left( {\Delta CA_{i,t} - \Delta CHE_{i,t} } \right) - \left( {\Delta CL_{i,t} - \Delta STD_{i,t} } \right)$ [$\Delta CA_{i,t}$ is change in current assets; $\Delta CHE_{i,t}$ is change in cash and cash equivalents; $\Delta CL_{i,t}$ is change in current liabilities; $\Delta STD_{i,t}$ is change in short-term debts]; $A_{i,t - 1}$ is total assets of firm i at end of year t–1; $\Delta REV_{i,t}$ and $\Delta REC_{i,t}$ are change in sales and change in receivables from year t–1 to year t of firm i respectively; $PPE_{i,t}$ is gross plant, property and equipment of firm i at end of year t
FSD_SCORE	Mean absolute deviation of the first digits of figures reported in the financial statements of firm i in year t from what are expected by Benford’s Law. $FSD\_SCORE_{i,t} = \frac{{\mathop \sum \nolimits_{d = 1}^{9} \left| {OBSERVED_{d,i,t} - EXPECTED_{d} } \right|}}{9}$; where $OBSERVED_{d,i,t}$ is the observed (actual) probability of digit d of firm i in year t; $EXPECTED_{d}$ is the expected probability of first digit d as defined by Benford’s Law; and d = 1, 2, …, 9
KS	The maximum cumulative absolute deviation of the first digits of items reported in the financial statements from those expected by Benford’s Law: $KS_{i,t} = max\left\{ {\left| {OD_{1,i,t} - ED_{1} } \right|,\left| {\left( {OD_{1,i,t} + OD_{2,i,t} } \right) - \left( {ED_{1} + ED_{2} } \right)} \right|, \ldots ,\left| {\left( {OD_{1,i,t} + OD_{2,i,t} + \ldots + OD_{9,i,t} } \right) - \left( {ED_{1} + ED_{2} + \ldots + ED_{9} } \right)} \right|} \right\}$ where $OD_{d,i,t}$ is the cumulative observed probability of the first digit d (d = 1, 2, …, 9) of firm i in year t; $ED_{d}$ is the expected probability of the first digit d (d = 1, 2, …, 9), as defined by Benford’s Law
DCF	Absolute value of abnormal cash flow. DCF is the absolute value of the residual of the following regression for each (Datastream Level 6) industry and each year with at least ten observations: $\frac{{CFO_{i,t} }}{{A_{i,t - 1} }} = \alpha + \beta_{1} \left( {\frac{1}{{A_{i,t - 1} }}} \right) + \beta_{2} \left( {\frac{{REV_{i,t} }}{{A_{i,t - 1} }}} \right) + \beta_{3} \left( {\frac{{\Delta REV_{i,t} }}{{A_{i,t - 1} }}} \right) + \varepsilon_{i,t}$ where $CFO_{i,t}$ is net cash flows from operations; $A_{i,t - 1}$ is total opening assets; $REV_{i,t}$ is sales; $\Delta REV_{i,t}$ is sales in year t minus sales in year t − 1; i is firm i; t is the year; and $\varepsilon_{i,t}$ is the error term
DDISEXP	Absolute value of abnormal discretionary expenditures. DDISEXP is the absolute value of the residual of the following regression for each (Datastream Level 6) industry and each year with at least ten observations: $\frac{{{\text{DISEXP}}_{{{\text{i}},{\text{t}}}} }}{{{\text{A}}_{{{\text{i}},{\text{t}} - 1}} }} =\upalpha\left( {\frac{1}{{{\text{A}}_{{{\text{i}},{\text{t}} - 1}} }}} \right) +\upbeta_{1} \left( {\frac{{{\text{REV}}_{{{\text{i}},{\text{t}} - 1}} }}{{{\text{A}}_{{{\text{i}},{\text{t}} - 1}} }}} \right) +\upvarepsilon_{{{\text{i}},{\text{t}}}}$ where $DISEXP_{i,t}$ is discretionary expenditures, which equals R&D expenses plus selling and administrative expenses; $A_{it - 1}$ is total opening assets; $REV_{i,t - 1}$ is sales in year t− 1; i is the firm; t is the year; $\varepsilon_{i,t}$ is the error term
DPROD	Absolute value of abnormal production costs. DPROD is the absolute value of the residual of the following regression for each (Datastream Level 6) industry and each year with at least ten observations: $\frac{{{\text{PROD}}_{\text{it}} }}{{{\text{A}}_{{{\text{i}},{\text{t}} - 1}} }} =\upalpha\left( {\frac{1}{{{\text{A}}_{{{\text{i}},{\text{t}} - 1}} }}} \right) +\upbeta_{1} \left( {\frac{{{\text{REV}}_{{{\text{i}},{\text{t}}}} }}{{{\text{A}}_{{{\text{i}},{\text{t}} - 1}} }}} \right) +\upbeta_{2} \left( {\frac{{\Delta {\text{REV}}_{{{\text{i}},{\text{t}}}} }}{{{\text{A}}_{{{\text{i}},{\text{t}} - 1}} }}} \right) +\upbeta_{3} \left( {\frac{{\Delta {\text{REV}}_{{{\text{i}},{\text{t}} - 1}} }}{{{\text{A}}_{{{\text{i}},{\text{t}} - 1}} }}} \right) +\upvarepsilon_{{{\text{i}},{\text{t}}}}$ where $PROD_{i,t}$ is production costs, which equal the sum of cost of goods sold and change in inventories from year t − 1 to year t; $REV_{i,t}$ is sales; $\Delta REV_{it}$ is sales in year t minus sales in year t − 1; $\Delta REV_{it - 1}$ is sales in year t − 1 minus sales in year t−2; $A_{it - 1}$ is total opening assets; i is the firm; t is the year; $\varepsilon_{i,t}$ is the error term
REM	Total real earnings management. REM = DCF + DPROD + DDISEXP
Control variables
aACIND	Industry-adjusted audit committee independence, where audit committee independence is the percentage of independent members on the audit committee. Industry-adjusted value equals the firm value minus the mean for the corresponding industry-year.
aBDIND	Industry-adjusted board independence, where board independence is the percentage of independent directors on the board.
aLEV	Industry-adjusted leverage, where leverage (LEV) equals the sum of long-term debts and short-term debts, scaled by total assets.
aLOGMBT	Industry-adjusted market-to-book ratio, where the market-to-book ratio (LOGMTB) is the natural log of the ratio of the market value to the book value of equity.
aLOGMVE	Industry-adjusted firm size, where firm size (LOGMVE) equals the natural log of the market value of equity.
aNOA	Industry-adjusted net operating asset ratio (NOA), where $NOA = \left[ {CEQ + \left( {DLTT + DLC} \right) - CHE} \right]/REV$, where ${\text{CEQ}}$ is the total book value of equity, ${\text{DLTT}}$ is long-term debts, ${\text{DLC}}$ is short-term debts; ${\text{CHE}}$ is cash and cash equivalent; ${\text{REV}}$ is sales.
AUDIT	equals one if the firm is audited by a Big Four audit firm, and zero otherwise.
CYCLE	Indicator for the business life cycle, calculated based on Dickinson (2011), and equals one if a firm has negative CFO, negative CFI, and positive CFF (young firm), or positive CFO, negative CFI, and positive CFF (growth firm), and zero if a firm has positive CFO, negative CFI, and negative CFF (mature firm), where CFO is cash flows from operating activities, CFI is cash flows from investing activities, and CFF is cash flows from financing activities
DISTRESS	equals one if ZSCORE is negative, and zero otherwise, where ZSCORE, following Taffler (1983), is calculated as follows: ${\text{ZSCORE}} = 3.2 + 12.18 * {\text{X}}_{1} + 2.50 * {\text{X}}_{2} - 10.68 * {\text{X}}_{3} + 0.029 * {\text{X}}_{4}$, where ${\text{X}}_{1} = \frac{\text{Profit before tax}}{\text{current liabilities}}$, ${\text{X}}_{2} = \frac{\text{Current assets}}{\text{Total liabilities}}$, ${\text{X}}_{3} = \frac{\text{Current liabilities}}{\text{Total assets}}$, and ${\text{X}}_{4} = \frac{{\left( {{\text{Quick assets}} - {\text{Current liabilities}}} \right)}}{{\left( {{\text{Sales}} - {\text{Pretax income}} - {\text{Depreciation}}} \right)/365}}$.
INCENTIVE	Equity-based incentives of the CEO: $INCENTIVE = \frac{ONEPCT}{{\left( {ONEPCT + SALARY + BONUS} \right)}}$ $ONEPCT = 0.01 x PRICE x \left( {SHARE + OPTION} \right)$ where $INCENTIVE$ is the equity-based incentives of the CEO; $ONEPCT$ is the dollar change in the CEO’s equity holdings following a 1% change in the stock price; $PRICE$ is the closing share price; $SHARE$ is the number of shares held by the CEO; $OPTION$ is the number of options held by the CEO; $SALARY$ is the total cash salary the CEO receives; and $BONUS$ is the cash bonus the CEO receives. Compensation data are manually collected from the Bloomberg database
M&A	Equals one if a firm announces a share-financed merger and acquisition deal, and zero otherwise
SEO	Equals one if a firm issues a significant portion of equity (outstanding shares increase by at least 5% and proceeds from equity issuance are positive), and zero otherwise