Proxies and Databases in Financial Misconduct Research

Karpoff, Jonathan M.; Koester, Allison; Lee, D. Scott; Martin, Gerald S.

doi:10.2308/accr-51766

Cited by 253 publications

(60 citation statements)

References 75 publications

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“…On average, we find that the misstatement became publicly known 1,038 days before the respective AAER was released, which is very similar to the time lag reported in Karpoff et al. (). Presumably, credit ratings were already impacted shortly after the misstatement became publicly known (and not only when the AAER was released), which is why we focus on the date of the misstatement becoming publicly known.…”

Section: Methodssupporting

confidence: 88%

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What Drives the Consequences of Intentional Misstatements? Evidence from Rating Analysts’ Reactions

Bierey¹,

Schmidt

2016

Business Fin & Account

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This paper aims to identify the mechanisms through which intentional misstatements adversely affect firms by analyzing rating analysts’ reaction to misstatements. In order to identify the mechanisms through which the misstatement affects firms’ credit ratings, we analyze the content of rating reports. Rating analysts are concerned about seven different mechanisms. They are most concerned about misstatement‐related violations of debt covenants that increase a firm's liquidity risk. We find that, subsequent to an intentional misstatement becoming publicly known, credit ratings of misreporting firms are adversely affected for up to seven years. The adverse impact of an intentional misstatement on a firm's credit rating is most pronounced in cases in which rating analysts mention concerns about misstatement‐related violations of covenants. Our results suggest that these covenant violations are the most severe mechanism through which misstatements adversely affect firms’ creditworthiness.

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Section: Methodssupporting

confidence: 88%

“…We acknowledge that not all misstatements might be included in the GAO or AAER datasets (Karpoff et al., ). Thus, our analysis might suffer from a Type II‐error, since we potentially classify firms as non‐misstatement firms (control group), even though there might have been a misstatement.…”

mentioning

confidence: 99%

What Drives the Consequences of Intentional Misstatements? Evidence from Rating Analysts’ Reactions

Bierey¹,

Schmidt

2016

Business Fin & Account

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“…To identify accused peers, we used a database on financial misconduct in the U.S., listing firms that were subjected to enforcement actions for financial misrepresentation (''cooking the books'') by the SEC (Karpoff, Lee, and Martin, 2008;Karpoff et al, 2017). The data covered all known instances of financial misconduct in the U.S. that violated provisions of the 1934 Securities Exchange Act.…”

Section: Research Setting and Samplementioning

confidence: 99%

“…All the events were comparable because they referred to violations of clearly stated provisions. 5 Compared with alternative data sources (the Government Accountability Office, Audit Analytics, Stanford Securities Class Action Clearinghouse, and SEC Accounting and Auditing Enforcement Releases), our database more effectively excludes errors or non-fraudulent activities such as minor accounting irregularities; it is also more accurate in identifying the first date at which the financial misconduct was revealed to the public (Karpoff et al, 2017). Identifying the earliest event date is essential for making valid causal inferences in stock market event studies (MacKinlay, 1997) because this date represents the point in time when stakeholders such as investors first learned about the misconduct and realized that the performance of the accused firm was not as positive as had been previously reported.…”

Section: Research Setting and Samplementioning

confidence: 99%

When an Industry Peer Is Accused of Financial Misconduct: Stigma versus Competition Effects on Non-accused Firms

Naumovska¹,

Lavie

2021

Administrative Science Quarterly

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Research on misconduct suggests that accusations against industry peers generate negative consequences for non-accused firms (a “stigma effect”). Yet, building on research on competitive dynamics, we infer that such accusations can benefit non-accused firms that compete with these peers (a “competition effect”). To reconcile these opposing perspectives, we posit that the negative stigma effect will increase with greater product market overlap between the non-accused firm and its accused peer, up to a point, beyond which the positive competition effect will counterbalance it. We further conjecture that the competition effect will be relatively more pronounced when the market classification used by investors for assessing the market overlap is more fine-grained. Accordingly, we suggest that more sophisticated investors, who rely on more fine-grained market classifications, increase their shareholdings in non-accused firms to a greater extent than less sophisticated investors as the market overlap between the non-accused firm and the accused peer increases. Using elaborate data on products and investments, we analyze investors’ shareholdings and stock market returns of non-accused firms in the U.S. software industry following accusations of financial misconduct by their industry peers, and we find support for our predictions. Our study elucidates the interplay between stigma and competition following misconduct by industry peers.

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“…Most of the early studies on the detection of financial statement fraud refer to financial ratios as the research variable. Current research widely uses both financial ratios and non-financial variables (or corporate governance variables), given the advocacy and emphasis of corporate governance by practitioners [6,8,10,28,32,[37][38][39].…”

Section: Related Workmentioning

confidence: 99%

Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry

Jan

2021

Sustainability

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Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep learning is being applied to many different domains. This study examines both the financial data and non-financial data of TWSE/TEPx listed companies in 2001–2019 by sampling a total of 153 companies, consisting of 51 companies reporting financial statement fraud and 102 companies not reporting financial statement fraud. Two powerful deep learning algorithms (i.e., recurrent neural network (RNN) and long short-term memory (LSTM)) are used to construct financial statement fraud detection models. The empirical results suggest that the LSTM model outperforms the RNN model in all performance indicators. The LSTM model exhibits accuracy as high as 94.88%, the most frequently used performance indicator.

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Proxies and Databases in Financial Misconduct Research

Cited by 253 publications

References 75 publications

What Drives the Consequences of Intentional Misstatements? Evidence from Rating Analysts’ Reactions

What Drives the Consequences of Intentional Misstatements? Evidence from Rating Analysts’ Reactions

When an Industry Peer Is Accused of Financial Misconduct: Stigma versus Competition Effects on Non-accused Firms

Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry

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