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Welcome to the Gray Zone: Shades of Honesty and Earnings Management

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Abstract

We examine the influence of face-based judgments of CFO and CEO honesty on earnings management for the largest publicly traded companies in America. After controlling for incentives and opportunities to manage earnings, CFOs perceived to be less honest engage in higher levels of accruals earnings management and real earnings management. The beneficial impact of perceived honesty on earnings quality is most pronounced when both the CFO and the CEO are perceived to be more honest. Findings are consistent with our conjecture that both the CFO and CEO contribute to a firm’s financial reporting environment.

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Notes

  1. While early concepts of physiognomy were misguided, recent studies support the assertion that facial characteristics influence personality perception and may be reliable indicators of behavior (Re and Rule 2015).

  2. Subclinical narcissism has all the facets of Narcissistic Personality Disorder, a clinical psychopathology, but is less extreme. It is not immediately apparent and is often difficult to diagnose.

  3. Honesty-Humility shows an upward trend of about one standard deviation unit between the ages of 18 and 60. However, the rank order of this personality trait is constant. Developmental changes are likely to be the main explanation for this age trend (Ashton and Lee 2016).

  4. The form is available at http://hexaco.org/hexaco-inventory (Lee and Ashton 2018).

  5. We only include a rater’s scores in our sample if their ratings are of consistent quality, i.e., if both of the following criteria for a given rater are met: (1) the standard deviation of mean scores is at least 0.3; and (2) the average standard deviation of responses to the 10 honesty questions is less than 1.1.

  6. Accrual-based earnings management measures are noisy proxies due to construct validity concerns (e.g., DeFond 2010; Jackson 2018). However, prior literature and industry experts agree that accruals, real activities, and classification shifting are the three ways that firms routinely engage in earnings management.

  7. There are other discretionary accrual models, including the modified Jones (1991) model and the McNichols (2002) model. Given the significant overlap of explanatory variables across the discretionary accrual models, we select two models which are consistently used in the literature and collectively incorporate most of the explanatory variables.

  8. The revised “classification shifting” score is strictly increasing in both UCE and SPI: firms with a high (low) UCE and a high (low) SPI receive a high (low) score.

  9. We test for heteroscedasticity and autocorrelation across panels using the Breusch–Pagan and Wooldridge tests, respectively, and reject the null of independent and identically distributed disturbances. We calculate panel-corrected standard errors to control for heteroscedasticity in all models and also control for autocorrelation across panels for Dechow–Dichev, REM1, and REM2 using the Prais–Winsten regression.

  10. Berry and Wero (1993) provide an excellent summary of this theoretical issue, including discussion of physiognomy which fell into disrepute in the early 1900s because its assertions about the relationship between personality and physical appearance were not based on scientific observation. More specifically, early studies examined relationships between isolated facial features and personality, whereas modern studies examine how overall face shape and the configuration of features within a face relate to measures of behavior. This shift to a holistic approach has a strong scientific basis.

  11. In psychology/sociology research, correlations between psychological/sociological constructs of between about − 0.20 and + 0.20 are considered small, correlations between − 0.20 and − 0.40 and between + 0.20 and + 0.40 are considered moderate in size, and correlations beyond − 0.40 or beyond + 0.40 are considered large (Ashton 2013). Research by Penton-Voak et al. (2006), in the context of the Big 5 personality traits, finds that observers make accurate judgments of extraversion (r = 0.24), emotional stability (r = 0.18), openness (r = 0.22), and extraversion (r = 0.25).

  12. We use three different proxies for stock returns: raw returns less value-weighted market returns; raw returns less equal-weighted market returns; and raw returns less S&P500 returns. Results are consistent across proxies, ranging from 2.0% excess return to 2.4% excess return. Market adjusted returns are a better proxy than raw returns to assess manager performance as it removes idiosyncratic differences in market returns across years.

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Acknowledgements

We gratefully acknowledge helpful comments from Hamza Warraich, Christine Wiedman, conference participants at the CPA Manitoba Accounting Research Conference, the Inaugural Conference on Intelligent Information Retrieval in Accounting and Finance (CUHK, Shenzhen), the 2019 AAA Forensic Accounting Section Conference, the 2019 CAAA annual conference, the 2019 AAA Annual Meeting, the 2019 Conference on the Convergence of Financial and Managerial Accounting Research (CFMA), the 2020 Hawaii Accounting Research Conference (HARC) and seminar participants at McMaster University, the University of Waterloo, and Carleton University. We acknowledge the financial support from the CPA/Brock Institute for International Issues in Accounting and the Social Sciences and Humanities Research Council (SSHRC). All errors are our own.

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Appendices

Appendix 1

Variable Definitions

Variable

Definition

Abnormal Accruals

Absolute value of the estimated residuals from the following industry-year regressions:

\(\frac{{TotalAccruals}_{t}}{{Assets}_{t-1}}={\alpha }_{1}\left(\frac{1}{{Assets}_{t-1}}\right)+\frac{{\beta }_{1}\left(\Delta {S}_{t}-\Delta {AR}_{t}\right)}{{Assets}_{t-1}}+{\beta }_{2}\left(\frac{{PPE}_{t-1}}{{Assets}_{t-1}}\right)+{\beta }_{3}{ROA}_{t-1}+{\varepsilon }_{t}\)

where TotalAccruals equals net income before extraordinary items minus operating cash flows; ΔS is change in net sales; ΔAR is change in accounts receivables; PPE is net property, plant, and equipment; ROA is return on assets; and Assetst-1 are lagged total assets

Dechow–Dichev

Absolute value of the estimated residuals from the following industry-year regressions:

\(WC={\alpha }_{0}+{\beta }_{1}{CFO}_{t-1}+{\beta }_{2}{CFO}_{t}{+\beta }_{3}{CFO}_{t+1}{+\varepsilon }_{t}\)

where WC is working capital accruals; and CFO is cash flows from operations

ABCFO

Absolute value of the estimated residuals from the following industry-year regressions:

\(\frac{{CFO}_{t}}{{A}_{t-1}}={\alpha }_{0}{+\alpha }_{1}(\frac{1}{{A}_{t-1}}){+\alpha }_{2}(\frac{{S}_{t}}{{A}_{t-1}})+{\alpha }_{3}(\frac{\Delta {S}_{t}}{{A}_{t-1}})+{\epsilon }_{t}\)

where CFOt is cash flows from operations, A is total assets, and S is net sales

ABEXP

Absolute value of the estimated residuals from the following industry-year regressions:

\(\frac{{DISX}_{t}}{{A}_{t-1}}={\alpha }_{0}{+\alpha }_{1}(\frac{1}{{A}_{t-1}}){+\alpha }_{2}(\frac{{S}_{t-1}}{{A}_{t-1}})+{\epsilon }_{t}\)

where DISX is the sum of advertising, research and development and SGA expenses, A is total assets, and S is net sales

ABPROD

Absolute value of the estimated residuals from the following industry-year regressions:

\(\frac{{PROD}_{t}}{{A}_{t-1}}={\alpha }_{0}{+\alpha }_{1}(\frac{1}{{A}_{t-1}}){+\alpha }_{2}(\frac{{S}_{t}}{{A}_{t-1}})+{\alpha }_{3}(\frac{\Delta {S}_{t}}{{A}_{t-1}})+{\alpha }_{4}(\frac{\Delta {S}_{t-1}}{{A}_{t-1}})+{\epsilon }_{t}\)

where PROD is cost of goods plus change in inventory, A is total assets, S is net sales, ΔS is change in sales

REM1

ABEXP + ABPROD

REM2

ABCFO + ABEXP

SPI

Special items as a percentage of sales. Special items are multiplied by negative 1 when they are income-decreasing and are set to zero when they are income-increasing

UCE

Reported minus expected core earnings, where expected core earnings is estimated using the following industry-year regressions:

\({CE}_{i, t}={\alpha }_{0}+{\beta }_{1}{CE}_{i, t-1}+{\beta }_{2}{ATO}_{i, t}{+\beta }_{3}{ACCRUALS}_{i, t-1}{+{\beta }_{4}{\Delta S}_{i, t}{+\beta }_{5}{NEG\_SALES}_{i, t}+\varepsilon }_{i, t}\)

where CE is (sales – cost of goods sold – SGA expenses); ATO is asset turnover, defined as sales/average net operating assets; ACCRUALS equals net income before extraordinary items minus operating cash flows; ΔS is change in sales; and NEG_SALES equals 1 if ΔS is negative, and 0 otherwise

Classification Shifting

Sum of standardized value of SPI and standardized value of UCE

Honesty

Normalized mean honesty score (between 0 and 1) for each CEO(CFO) picture

CEO Power

Indicator variable equal to 1 if the CEO is also Chairman of the Board, and 0 otherwise

Honesty Spread

Absolute value of CFO Honesty minus CEO Honesty

Stockcomp

Simple average of CEO and CFO stock-based compensation, defined as the ratio of equity-based pay to total annual compensation

Bonus

Simple average of CEO and CFO bonus compensation, defined as the ratio of bonus-based pay to total annual compensation

Loss

Indicator variable equal to 1 if net income is negative, and 0 otherwise

Leverage

Ratio of total debt to total assets

Section 302

Indicator variable equal to 1 if the company reports internal control deficiencies under SOX Sect. 302, and 0 otherwise

PercAuditFees

Ratio of audit and audit-related fees to total fees

Size

Natural logarithm of total assets

MTB

Ratio of the market value of common shares at the end of the fiscal year to the book value of common shares

OCFlow

Cash flow from operations scaled by lagged total assets

Gender

Indicator variable equal to 1 if the CEO(CFO) is a male, and 0 otherwise

Appendix 2

Honesty-Humility Observer Report Form

Question #

Question

1

He/she would not use flattery to get a raise or promotion at work, even if he/she thought it would succeed

2

If he/she knew that he/she could never get caught, he/she would be willing to steal a million dollars. (reverse coded)

3

Having a lot of money is not especially important to him/her

4

He/she thinks that he/she is entitled to more respect than the average person is. (reverse coded)

5

If he/she wants something from someone, he/she will laugh at that person’s worst jokes. (reverse coded)

6

He/she would never accept a bribe, even if it were very large

7

He/she would get a lot of pleasure from owning expensive luxury goods. (reverse coded)

8

He/she wants people to know that he/she is an important person of high status. (reverse coded)

9

He/she would not pretend to like someone just to get that person to do favours for him/her

10

He/she would be tempted to use counterfeit money, if he/she were sure he/she could get away with it. (reverse coded)

Appendix 3

Honesty-Humility Self Report Form

Question #

Question

1

I would not use flattery to get a raise or promotion at work, even if I thought it would succeed

2

If I knew that I could never get caught, I would be willing to steal a million dollars. (reverse coded)

3

Having a lot of money is not especially important to me

4

I think that I am entitled to more respect than the average person is. (reverse coded)

5

If I want something from someone, I will laugh at that person’s worst jokes. (reverse coded)

6

I would never accept a bribe, even if it were very large

7

I would get a lot of pleasure from owning expensive luxury goods. (reverse coded)

8

I want people to know that I am an important person of high status. (reverse coded)

9

I would not pretend to like someone just to get that person to do favours for me

10

I would be tempted to use counterfeit money, if I were sure I could get away with it. (reverse coded)

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Lapointe-Antunes, P., Veenstra, K., Brown, K. et al. Welcome to the Gray Zone: Shades of Honesty and Earnings Management. J Bus Ethics 177, 125–149 (2022). https://doi.org/10.1007/s10551-020-04713-z

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