How Common Are Intentional GAAP Violations? Estimates from a Dynamic Model

Zakolyukina, Anastasia A.

doi:10.1111/1475-679x.12190

Cited by 82 publications

(37 citation statements)

References 65 publications

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“…To achieve this we use simulated method of moments. The approach is similar to what is applied by others in the foray into structural estimation in the accounting literature (Bird et al (2019); Beyer et al (2019) and Zakolyukina (2018)). We simulate the reported earnings and the market response from the model and then match the moments of the simulated data with the actual data.…”

Section: Estimating the Loss-aversion Coefficientmentioning

confidence: 89%

Loss aversion and the zero-earnings discontinuity

Rosa

Niebuhr

2019

SSRN Journal

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Prior literature suggests that the zero-earnings discontinuity is caused by earnings management. This makes sense if investors are naïve. We test for the possibility of investor naïveté and find that they are aware of firms performing earnings management around zero reported earnings and that there is no obvious gain of reaching zero reported earnings. We extend a signaling model to include loss-averse investors and we find that earnings management is not only rational, but in equilibrium, it is not possible for investors to deduce the correct value of firms' earnings around the discontinuity. Assuming our model generates the observed data, a loss-aversion coefficient of 1.2595 matches the discontinuity below zero reported earnings observed in the data simulated from the model and in the actual data. This loss-aversion coefficient is consistent with Tversky and Kahneman (1992), who find that losses are weighted roughly twice as heavily as gains.

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Section: Estimating the Loss-aversion Coefficientmentioning

confidence: 89%

Loss aversion and the zero-earnings discontinuity

Rosa

Niebuhr

2019

SSRN Journal

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“…From the perspective of the differences between the two models, we can notice that our model has the more applied character, the elimination of the qualitative variables and the validation of the results on certain data. The model proposed by Zakolyukina (Zakolyukina 2018) represents an originality in the dynamic approach of systemic risk produced in the interest of changing the accounting values. Considering that the model was reported to GAAP (Zakolyukina 2018), the model proposed by us has few elements that can be correlated with the model in question.…”

Section: Discussionmentioning

confidence: 99%

“…The model proposed by Zakolyukina (Zakolyukina 2018) represents an originality in the dynamic approach of systemic risk produced in the interest of changing the accounting values. Considering that the model was reported to GAAP (Zakolyukina 2018), the model proposed by us has few elements that can be correlated with the model in question. It is highlighted that the errors evaluated by our model can be integrated into the model of the researcher, bringing value to the dynamic model proposed by it.…”

Section: Discussionmentioning

confidence: 99%

“…To estimate the impact of intentional violation of accounting standards (GAAP study), (Zakolyukina 2018) developed a dynamic model for estimating accounting errors at the CEO level with the intention of affecting the stock market price over a 5 years horizon, in which case an average of 13 errors occurred in 91% of cases, which is a significant threshold for the study case presented. Modeling values have shown that the company size influences the analyzed indicator.…”

Section: Retrospective Treatmentmentioning

confidence: 99%

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Correction of Accounting Errors through Post Balance Sheet Event Analysis for Romanian Companies

2019

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The study scope is to present the typology of the events analyzed through our research and their impact on the quality of reported financial data. The objectives of the study are to analyze the vulnerability of enterprises according to methodological criteria such as risks and calculations of the risk profile, as well as to establish the necessary measures for correcting the accounting errors based on the conclusions drawn from the analysis. The method used is prospective, financial analysis of the data taken from the financial statements of the companies included in the sample, dynamic for a period of 6 years (2011–2016). Based on the method used, a risk model has been conceptualized to identify the vulnerabilities and risks reported in the financial statements and to define a company risk profile based on which error correction measures can be adopted. Considering the amplitude of the necessary check-ups and the methodology of the imposed accounting treatments, we believe that the topic addressed is a real area of interest for the professional accountants because it organizes the application procedures and limits the impact of errors on the quality of financial reporting in Romania.

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“…We use a second approach to identify irregularities from the AA database based on the types of accounting errors corrected by the restatement. This approach mirrors the classification procedure used by Larcker and Zakolyukina [] and Zakolyukina []. In these studies, restatements related to revenue recognition and serious core expense errors are treated as irregularities, because these corrections are generally associated with negative stock returns when revealed to investors (Palmrose, Richardson, and Scholz [], Scholz []).…”

Section: Extended Analyses and Robustness Testsmentioning

confidence: 99%

What Are You Saying? Using topic to Detect Financial Misreporting

Brown

Crowley

Elliott

2020

J of Accounting Research

165

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We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10-K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10-K filing amendments. Our out-of-sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting. JEL codes: C80; K22; K42; M40; M41; M48

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How Common Are Intentional GAAP Violations? Estimates from a Dynamic Model

Cited by 82 publications

References 65 publications

Loss aversion and the zero-earnings discontinuity

Loss aversion and the zero-earnings discontinuity

Correction of Accounting Errors through Post Balance Sheet Event Analysis for Romanian Companies

What Are You Saying? Using topic to Detect Financial Misreporting

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