2016
DOI: 10.2139/ssrn.2811778
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Proxies and Databases in Financial Misconduct Research

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Cited by 60 publications
(122 citation statements)
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“…Karpoff et al. () argue that each of these four databases has potential issues that may lead to meaningful biases. They also present a detailed discussion of the KKLM database.…”
mentioning
confidence: 99%
“…Karpoff et al. () argue that each of these four databases has potential issues that may lead to meaningful biases. They also present a detailed discussion of the KKLM database.…”
mentioning
confidence: 99%
“…We obtain a list of federal enforcement actions for financial misrepresentation (henceforth, fraud cases) from the Federal Securities Regulation (FSR) database, compiled by Jonathan Karpoff, Scott Lee, and Gerald Martin (Karpoff, Lee, and Martin ()). The FSR database is the most comprehensive database of federal securities enforcement actions brought about by the SEC and/or the Department of Justice (DOJ).…”
Section: Data Sources and Main Variables Of Interestmentioning
confidence: 99%
“…Karpoff et al (2013) refer to this phenomenon as the 'late initial revelation dates'. Hence, it is difficult to capture the time-series dimension of fraud in a panel data set, and more importantly, an unknown percentage of fraud occurrences remain undetected and unobservable to the researcher.…”
Section: Sample -Identifying Fraud Firmsmentioning
confidence: 99%
“…Researchers have found various ways to identify and observe instances of financial statement fraud, for example, using account restatements (Ahmed and Goodwin, 2007; and international databases such as Government Audit Office; Audit Analytics), securities class actions (Chapple et al, 2014; and international databases such as Stanford's Securities Class Action Clearing House), Australian Securities and Investments Commission (ASIC) actions (Da Silva Rosa et al, 2008) and the SEC's Accounting and Audit Enforcement Releases (AAERs) (Karpoff et al, 2013). However, it is much more difficult to identify and measure misappropriation-type fraud.…”
Section: Introductionmentioning
confidence: 99%