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Original title:
Financial Crime and Punishment: A Meta-Analysis
Authors: de Batz, L. ; Kočenda, Evžen
Document type: Research reports
Year: 2020
Language: eng
Series: IES Working Paper Series, volume: 2020/40
Abstract: We examine how the publication of intentional financial crimes committed by listed firms is interpreted by financial markets, using a systematic and quantitative review of existing empirical studies. Specifically, we conduct a meta-regression analysis and investigate the extent and nature of the impact that the publication of financial misconducts exerts on stock returns. We survey 111 studies, published between 1978 and 2020, with a total of 439 estimates from event studies. Our key finding is that the average abnormal returns calculated from this empirical literature are affected by a negative publication selection bias. Still, after controlling for this bias, our meta-analysis indicates that publications of financial crimes are followed by statistically significant negative abnormal returns, which suggests the existence of an informational effect. Finally, the MRA results demonstrate that crimes committed in common law countries, alleged crimes, and accounting crimes carry particularly weighty information for market participants. The results call for more transparency on side of enforcers along enforcement procedures, to foster timely and proportionate market reactions and support efficient markets.
Keywords: Event study; Financial Markets; Financial Misconduct; Fraud; Information and Market Efficiency; Listed Companies; Meta-Analysis; Returns
Project no.: GA20-17044S (CEP)
Funding provider: GA ČR
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2020/E/kocenda-0533386.pdf
Original record: http://hdl.handle.net/11104/0311791
Permalink: http://www.nusl.cz/ntk/nusl-432273
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports
Authors: de Batz, L. ; Kočenda, Evžen
Document type: Research reports
Year: 2020
Language: eng
Series: IES Working Paper Series, volume: 2020/40
Abstract: We examine how the publication of intentional financial crimes committed by listed firms is interpreted by financial markets, using a systematic and quantitative review of existing empirical studies. Specifically, we conduct a meta-regression analysis and investigate the extent and nature of the impact that the publication of financial misconducts exerts on stock returns. We survey 111 studies, published between 1978 and 2020, with a total of 439 estimates from event studies. Our key finding is that the average abnormal returns calculated from this empirical literature are affected by a negative publication selection bias. Still, after controlling for this bias, our meta-analysis indicates that publications of financial crimes are followed by statistically significant negative abnormal returns, which suggests the existence of an informational effect. Finally, the MRA results demonstrate that crimes committed in common law countries, alleged crimes, and accounting crimes carry particularly weighty information for market participants. The results call for more transparency on side of enforcers along enforcement procedures, to foster timely and proportionate market reactions and support efficient markets.
Keywords: Event study; Financial Markets; Financial Misconduct; Fraud; Information and Market Efficiency; Listed Companies; Meta-Analysis; Returns
Project no.: GA20-17044S (CEP)
Funding provider: GA ČR
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2020/E/kocenda-0533386.pdf
Original record: http://hdl.handle.net/11104/0311791
Permalink: http://www.nusl.cz/ntk/nusl-432273
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports
Record created 2020-12-03, last modified 2021-11-24