ABSTRACT This study examines the conditions under which financial restatements lead corporate boards to dismiss external auditors and how the market responds to those dismissal announcements. We find that auditors are more likely to be dismissed after more severe restatements but that the severity effect is primarily attributable to the dismissal of non-Big 4 auditors rather than Big 4 auditors. We also document that among corporations with Big 4 auditors, those that are larger and more complex operationally are less likely to dismiss their auditors. Combined, this evidence suggests that firms with higher switching costs and fewer replacement auditor choices are less likely to dismiss their auditors after a restatement, which is informative to the debates about the costs and benefits of mandatory auditor rotation and limited competition in the audit market. Additionally, we examine contemporaneous executive turnover and find evidence that boards view auditor dismissals as complementary rather than substitute responses to restatements. Finally, we investigate the market reaction to auditor dismissals after restatements. The market reaction to the dismissal is significantly more positive following more severe restatements (5.9 percent) relative to less severe restatements (0.6 percent) when the client engages a comparably sized auditor. This positive market reaction is consistent with firms restoring financial reporting credibility by replacing their auditors and highlights the important role that auditors play in the financial markets. Data Availability: Data are available from public sources indicated in the text.

ABSTRACT The expectations management literature has so far focused on firms meeting the analyst consensus forecast—the expectations of analysts as a group—at earnings announcements. In this study we argue that investors may use individual analyst forecasts as additional benchmarks in evaluating reported earnings because the consensus forecast underutilizes private information contained in individual analyst forecasts. We predict that measures reflecting such private information have incremental explanatory power over the consensus forecast for the market's reaction to earnings news. We find results consistent with this prediction by examining two measures: (1) the percentage of individual forecasts met and (2) meeting the key analyst forecast. We extend the literature by documenting the role of individual analyst forecasts in investors' evaluations of reported earnings. JEL Classifications: G10; G11; G17; G14; G24. Data Availability: Data are publicly available from the sources identified in the paper.

ABSTRACT We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high‐frequency data that identify computer‐generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of high‐frequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.

ABSTRACT Rating agencies have become more conservative in assigning corporate credit ratings over the period 1985 to 2009; holding firm characteristics constant, average ratings have dropped by three notches. This change does not appear to be fully warranted because defaults have declined over this period. Firms affected more by conservatism issue less debt, have lower leverage, hold more cash, are less likely to obtain a debt rating, and experience lower growth. Their debt spreads are lower than those of unaffected firms with the same rating, which implies that the market partly undoes the impact of conservatism on debt prices. This evidence suggests that firms and capital markets do not perceive the increase in conservatism to be fully warranted.

ABSTRACT This study shows that the guardians behind underaged accounts are successful at picking stocks. Moreover, they tend to channel their best trades through the accounts of children, especially when they trade just before major earnings announcements, large price changes, and takeover announcements. Building on these results, we argue that the proportion of total trading activity through underaged accounts (labeled BABYPIN ) should serve as an effective proxy for the probability of information trading in a stock. Consistent with this claim, we show that investors demand a higher return for holding stocks with a greater likelihood of private information, proxied by BABYPIN .

ABSTRACT We investigate how the tone of sell-side debt analysts' discussions about debt-equity conflict events affects the informativeness of debt analysts' reports in debt markets. Conflict events such as mergers and acquisitions, debt issuance, share repurchases, or dividend payments potentially generate asset substitution or wealth expropriation by equity holders. We document that debt analysts routinely discuss these conflict events in their reports. More importantly, discussions about conflict events that we code as negative are associated with increases in credit spreads and bond trading volume. Consistent with the informational value of debt analysts' discussions in secondary debt markets, we find that negatively coded conflict discussions predict higher bond offering yields in the primary bond market. In additional analyses, we measure the tone of debt analysts' discussions based on their disagreement with the tone of equity analysts' discussions and find that the informativeness of debt analysts' reports is higher when our coding indicates that conflict events are viewed negatively by debt analysts but positively by equity analysts. JEL Classifications: G12, G14, G32, M49.