Enforcement actions and their effectiveness in securities regulation: Empirical evidence from management earnings forecasts

Artikel

Due to resource constraints, securities regulators cannot find or punish all firms that have conducted irregular or even illegal activities (hereafter referred to as fraud). Those who study securities regulations can only find the instances of fraud that have been punished, not those that have not been punished, and it is these unknown cases that would make the best control sample for studies of enforcement action criteria. China's mandatory management earnings forecasts solve this sampling problem. In the A-share market, firms that have not forecasted as mandated are likely in a position to be punished by securities regulators or are attempting to escape punishment, and their identification allows researchers to build suitable study and control samples when examining securities regulations. Our results indicate that enforcement actions taken by securities regulators are selective. The probability that a firm will be punished for irregular management forecasting is significantly related to proxies for survival rates. Specifically, fraudulent firms with lower return on assets (ROAs) or higher cash flow risk are more likely to be punished. Further analysis shows that selective enforcement of regulations has had little positive effect on the quality of listed firms' management forecasts.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: China Journal of Accounting Research ; ISSN: 1755-3091 ; Volume: 5 ; Year: 2012 ; Issue: 1 ; Pages: 59-81 ; Amsterdam: Elsevier

Classification and Topics

• Classification: Management ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models ; Bureaucracy; Administrative Processes in Public Organizations; Corruption ; Accounting
• Subject: Enforcement actions ; Management earnings forecasts ; Irregularities ; Selection bias

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Song, Yunling ; Ji, Xinwei
• Event: Veröffentlichung
• (who): Elsevier
• (where): Amsterdam
• (when): 2012

Further information

• DOI: doi:10.1016/j.cjar.2012.03.003
• Handle: http://hdl.handle.net/10419/187566
• Last update: 10.03.2025, 11:43 AM CET