A Critical Analysis of Databases used in Financial Misconduct Research

TLDR

Financial misreporting research relies on four major databases, but these sources capture only proxy events, miss early disclosures, include many non‑fraud incidents, and omit numerous relevant events, potentially biasing empirical findings. The study aims to investigate why empirical results vary across databases by comparing them to a detailed sample of 1,243 SEC enforcement cases. The authors compare each database to the enforcement case sample, quantify four key characteristics that influence inference, and estimate their economic impact. The analysis reveals that database characteristics significantly affect empirical conclusions and provides recommendations for researchers to mitigate these biases.

Abstract

An extensive accounting and finance literature examines the causes and effects of financial misreporting or misconduct based on samples drawn from four popular databases that identify restatements, securities class action lawsuits, and Securities and Exchange Commission (SEC) Accounting and Auditing Enforcement Releases (AAERs). We show, however, that the results from empirical tests can depend on which database is accessed. To examine the causes of such discrepancies, we compare the information in each database to a detailed sample of 1,243 case histories in which the SEC brought enforcement action for financial misrepresentation. These comparisons allow us to identify, measure, and estimate the economic importance of four characteristics of each database that affect inferences from empirical tests. First, these databases contain information on only the event that is used to proxy for misconduct (e.g., restatements), so they omit other relevant announcements that affect a researcher’s interpretation and use of the events. Second, the initial public revelation of financial misconduct occurs, on average, months before the initial coverage in these databases, leading to discrepancies in event study measures and pre/post comparison tests. Third, most of the events captured by these databases are unrelated to financial fraud, and efforts to cull out non-fraud events yield heterogeneous results. Fourth, the databases omit large numbers of events they were designed to capture. We show the extent to which each database is subject to these concerns and offer suggestions for researchers seeking to use these databases.