Earnings Management Using Discontinued Operations

Barua, Abhijit; Lin, Steve; Sbaraglia, Andrew M.

doi:10.2139/ssrn.1245863

Cited by 57 publications

(110 citation statements)

References 27 publications

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“…Byrnes et al (1999), for example, reviewed 150 psychology studies and found that female participants are less likely than male participants to take risk. Prior studies have also shown that this risk attitude is prevalent in accounting, economics, finance and management decisions (Barua et al 2010b;Croson and Gneezy 2009). For example, in their experimental study, Powell and Ansic (1997) found that females tend to avoid making risky financial decisions compared to their male counterparts.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 95%

“…In terms of earnings management, Krishnan and Parsons (2008) found that the presence of female executives within the top management level is positively related to earnings quality or low earnings management. In supporting this, Barua et al (2010b) found that firms with female chief financial officers (CFOs) have lower AEM. Focussing on blatant cases of manipulation, Cumming et al (2015) showed that the probability of fraud is lower in firms with more female representatives.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 96%

“…Nonetheless, the role of gender differences in securing the integrity of financial reports is not fully understood (Barua et al 2010b;Ho et al 2015), and the extant research is yet to provide a direct explanation for why female CEOs are less likely to engage in earnings management than their male counterparts are. 8 Furthermore, prior studies (Cumming et al 2015;Peni and Vahamaa 2010;Sun et al 2017) have investigated the relationship between female CEOs and AEM that, arguably, has higher potential litigation and reputation costs because of the intense public and regulatory scrutiny (Abbott et al 2012), particularly after the financial scandals in 2000 (Barua et al 2010b;Bruns and Merchant 1990;Merchant and Rockness 1994). Consequently, we argue that if female CEOs are more risk-averse than ethical, then they may be less inclined to engage in AEM largely because of the significant associated risk.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

“…For example, existing evidence indicates that the presence of women on corporate boards: (i) reduces the probability of corporate fraud (Capezio and Mavisakalyan 2016;Cumming et al 2015), (ii) enhances earnings quality (Peni and Vahamaa 2010;Srinidhi et al 2011;Zalata et al 2018); and reduces the likelihood of financial statement re-statements (Abbott et al 2012). Similarly, several findings suggest that executive female directors are associated with: (i) more conservative financial reporting (Ho et al 2015;Palvia et al 2015), (ii) higher accounting quality (Barua et al 2010b), and (iii) lower fraudulent financial reporting (Sun et al 2017). However, others have indicated that female auditors are less likely to issue a going-concern decision for financially distressed clients (Hossain et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…However, since the appointment of women in top executive positions is relatively low, we focus on CEOs gender. Whilst past studies (Arun et al 2015;Barua et al 2010b;Cumming et al 2015;Francis et al 2015;Owhoso 2002;Peni and Vahamaa 2010;Sun et al 2011) have examined the effect that gender has on less sophisticated forms of earnings management, such as fraud and AEM, little, however, is known about whether there are gender differences in the use of such sophisticated and subtle earnings management techniques, such as classification shifting. The current study, therefore, seeks to extend prior studies by focussing on classification shifting.…”

Section: Introductionmentioning

confidence: 99%

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Female CEOs and Core Earnings Quality: New Evidence on the Ethics Versus Risk-Aversion Puzzle

et al. 2018

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The question of whether females tend to act more ethically or risk-averse compared to males is an interesting ethical puzzle. Using a large sample of US firms over the 1992-2014 period, we investigate the effect that the gender of a chief executive officer (CEO) has on earnings management using classification shifting. We find that the pre-Sarbanes-Oxley (SOX) Act period was characterized by high levels of classification shifting by both female and male CEOs, but the magnitude of such practices is, surprisingly, significantly higher in firms with female CEOs than in those with male CEOs. By contrast, our results suggest that following the passage of the punitive SOX Act, classification shifting by female CEOs declined significantly, whilst it remained pervasive in firms with male CEOs. This suggests that the observable differences in financial reporting behavior between male and female CEOs seem to be because female CEOs are more risk-averse, but not necessarily more ethically sensitive than their male counterparts are. The central tenets of our findings remain unchanged after several additional checks, including controlling for alternative earnings management techniques, corporate governance mechanisms, CEO and chief financial officer characteristics and propensity score-matching.

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Section: Literature Review and Hypotheses Developmentmentioning

confidence: 95%

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 96%

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Female CEOs and Core Earnings Quality: New Evidence on the Ethics Versus Risk-Aversion Puzzle

et al. 2018

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Earnings management through financing activities: Evidence from early debt extinguishments

Barua

2022

J Corp Accounting Finance

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An alternative model for the analysis of detecting electronic industries earnings management using stepwise regression, random forest, and decision tree

Chen

2015

Soft Comput

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This study attempts to diagnose the detecting electronic industries' earnings management by integrating suitable soft computing methods. Accounting earnings information is a very crucial element for corporate stakeholders to determine their stock prices and evaluate their supervision and management authority's performance, while it is also essential information for measuring corporate value. Hence, whether an enterprise can faithfully express its true economic meaning over its financial statements and how the management handles its earnings have turned out to be a popular issue widely discussed by researchers. Detecting public companies' earnings management is an important and challenging issue that has served as the impetus in many academic studies over the last few decades. Data mining technique and machine learning methods have also been commonly applied by accounting and financial personnel to other fields of studies. The study used the stepwise regression and random forest techniques to screen the variables in the first place, followed by adopting three kinds of decision trees including Chi-squared automatic interaction detector, classification and regression trees and C5.0 to establish a model and find out if the tested enterprise had extreme earnings manipulation. The results show that the proposed hybrid approach (RF+C5.0) has the optimal classification rate (the accuracy rate is 91.24 %) and the lowest occurrence of Type I error and Type II error. Also, as discovered from the rules set of the Communicated by V. Loia. final additional testing, an enterprise's operating cash flow, times interest earned ratio and previous period's discretionary accruals play a decisive role in affecting its extreme earnings management.

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Earnings Management Using Discontinued Operations

Cited by 57 publications

References 27 publications

Female CEOs and Core Earnings Quality: New Evidence on the Ethics Versus Risk-Aversion Puzzle

Female CEOs and Core Earnings Quality: New Evidence on the Ethics Versus Risk-Aversion Puzzle

Earnings management through financing activities: Evidence from early debt extinguishments

An alternative model for the analysis of detecting electronic industries earnings management using stepwise regression, random forest, and decision tree

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