Journal of Accounting Research最新文献

This study examines whether the efficiency of federal district courts affects the likelihood of SEC enforcement. The results indicate that the SEC is less likely to initiate enforcement actions against firms in less efficient federal district courts. In addition, the study examines the implications of court efficiency for firms’ financial reporting quality (FRQ). The evidence suggests that firms residing in more efficient federal districts have higher FRQ and the effect is stronger in subsamples with weak private enforcement regimes. Additional analysis of the choice of venue suggests that the SEC's decision to file in the District Court for D.C. is not affected by local court efficiency. Collectively, this study highlights the importance of an efficient federal judicial system for public enforcement.

We study the impact of social connections between judges and executives on the outcomes of Securities Class Action Litigation (SCAL). Judges who are socially connected to a firm's executives are significantly more likely to dismiss lawsuits against the firm. There is also evidence of faster resolution and lower payout amounts in connected cases. The favorable outcomes cannot be explained by the lower severity of connected cases, or by court, judge, or firm characteristics. Our results are more pronounced when executives connected to the judge are named defendants in the lawsuits, when connected cases involve less visible lawsuits or firms, and when connections between judges and executives are likely more direct. Our evidence indicates that social connections influence judge impartiality and meaningfully alter SCAL outcomes.

Using a large sample of U.S. commercial banks from 1994 to 2019, we find that loan fair values are highly relevant for depositor decision making. A one-standard-deviation decrease in loan fair value performance is associated with more than 10% lower uninsured deposit flows than the sample average. Information in fair values about loan credit quality is quite limited and cannot account for the bulk of the relevance. Instead, consistent with models of bank fragility, the relevance seems to stem more from information on the decline in loan liquidation values, triggering panic-based withdrawals motivated by (self-fulfilling) expectations of withdrawals by other depositors. The findings inform the cost-benefit tradeoff of reporting loan fair values.

Research documents price co-movements, or “spillovers,” between focal firms and their peers at focal firms’ earnings announcements. We find that both signed and absolute co-movements between focal- and peer-firm returns are significantly lower at earnings-announcement dates compared to other dates. Analytically, we demonstrate that co-movements do not necessarily indicate common information; instead, co-movements measure the relative proportion of focal firm-specific information to common information in focal-firm earnings announcements. We study three settings where information transfers might be higher: when focal firms report significant earnings surprises, are industry leaders, or share correlated earnings patterns with peer firms. We continue to find lower return correlations during focal-firm earnings announcements. We conduct two alternative tests but fail to find evidence that common information released during focal-firm earnings announcements is significantly greater than on other days. These results raise doubt about the extent of the information externality attributable to financial-report releases.

Fixed effects (FE) have emerged as a ubiquitous and powerful tool for eliminating unwanted variation in observational accounting studies. Unwanted variation is plentiful in accounting research because we often use rich data to test precise hypotheses derived from abstract theories. By eliminating unwanted variation, FE reduce concerns that omitted variables bias our estimates or weaken test power. FE are not costless, though, so their use should be carefully justified by theoretical and institutional considerations. FE also transform samples and variables in ways that are not immediately apparent, and in doing so affect how we should interpret regression results. This primer explains the mechanics of FE and provides practical guidance for the informed use, transparent reporting, and careful interpretation of FE models.