This paper investigates whether so-called anomalous returns predicted by accounting numbers reflect normal returns for risk or abnormal returns. It does so via a model showing how accounting numbers inform about normal returns if pricing were rational. The model equates expected returns to expectations of earnings and earnings growth, so that any variable that forecasts earnings and earnings growth also indicates the required return if the market prices those outcomes as risky. The empirical results confirm that many accounting anomaly variables (such as accruals, asset growth, and investment) forecast forward earnings and growth, and in the same direction in which they forecast returns. While the lack of an agreed-upon asset pricing model for required returns rules out definitive conclusions, the paper provides both a framework and supporting empirical results indicating that the observed “anomalous” returns associated with accounting numbers are consistent with rational pricing.

Accounting misstatements are often detected with substantial delays, leading to “look-ahead bias” in model predictions if the detection lag is not considered. Moreover, the misstatement data-generating process is evolving due to regulatory regime shifts, further complicating the evaluation of model predictions. We design an approach that accounts for detection lags and continuously updates models to adapt to the changing data-generating process. By comparing with the conventional approach that ignores detection lags, we show that the look-ahead bias can substantially inflate prediction performance. We also demonstrate that although leaving a temporal gap between training and test samples can mitigate the look-ahead bias, it sacrifices the model’s predictive power by disconnecting the dynamic data-generating process between training and test periods. We further implement a trading strategy to evaluate the practical utility of the continuously updating approach. Our study presents a new conceptual lens for understanding and evaluating misstatement prediction models. Data Availability: Data are available from the public sources identified in the study.

We use trade-level data to examine the role of actively managed funds (AMFs) in earnings news dissemination. We find that AMFs are drawn to, and participate disproportionately more in, earnings announcements (EAs) that include bundled managerial guidance. When the two pieces of news are directionally inconsistent, AMFs trade in the direction of future guidance rather than current earnings. AMFs exhibit an ability to discern, and adapt their trading to, the bias in bundled guidance. While AMF trades at EAs are generally more profitable than their non-EA trades, this result reverses when guidance bias is extreme. Overall, we find that increased AMF trading during EAs leads to faster price adjustment. Collectively, these findings suggest that AMFs are sophisticated processors of bundled earnings news, and their trading generally improves market price discovery.

We show that financial reporting influences consumer behavior by drawing consumer attention to announcing firms. Analyzing global positioning system (GPS) data, we document upticks in foot traffic to firms’ commercial locations immediately following their earnings announcements. This increase is more pronounced for announcements with substantial media attention, fewer concurrent announcements, heightened internet search volume, and extreme stock price jumps and earnings surprises—indicating that announcement coverage impacts consumer behavior by capturing attention. Furthermore, foot traffic increases with positive earnings for firms offering durable goods, suggesting consumers respond to news about firms’ financial prospects. Consumer attention patterns increase revenues and advertising effectiveness, ultimately suggesting that financial reporting serves a marketing function. Data Availability: All other data are available from the public sources cited in the text.

This study provides novel evidence on the magnitude of switching costs in auditing. Using a discrete choice approach, we infer switching costs from clients’ audit firm choices. The demand estimation reveals that switching costs are significant and vary by direction, with the highest costs associated with switching from non-Big 4 to Big 4 audit firms. Counterfactual analyses of forced switches suggest that switching costs are substantial, ranging from 0.7 billion U.S. dollars (14.2 percent of audit fees) to 1.2 billion U.S. dollars (24.0 percent of audit fees) when aggregated across all clients. Counterfactual analyses of voluntary switching show that the audit market would become highly dynamic and more concentrated if switching costs were removed. Additionally, clients would gain consumer surplus of up to 306 million U.S. dollars (5.4 percent of audit fees) in such a scenario. Overall, our study documents the importance of switching costs for understanding audit market dynamics. Data Availability: Data are available from the public sources cited in the text.

This study investigates the value of auditor industry specialization. In the first step, we use a discrete choice model to derive the first-order demand for auditor industry specialization. Our results reveal that clients have a general preference for auditor industry specialization, relating to both audit firm and audit office specialization. We observe that specializations at the audit firm and audit office level are substitutes. We also find that larger, more complex clients have a stronger demand for industry specialization at the audit office level. In the second step, we use the results from the discrete choice model to quantify the value of auditor industry specialists for clients. We find the overall value of industry specialization aggregated across all clients is 5.2 million USD (0.36 percent of audit fees) and that industry specialization at the firm (office) level is decisive for auditor choice in 4 percent (6 percent) of all cases.

We examine firms’ voluntary disclosures of innovation under technology coopetition, focusing on technology standard setting organizations (SSOs). Technology coopetition is characterized by (1) cooperation to determine technology standards, which requires information sharing to reach consensus, and (2) competition for standard implementation to obtain standard-essential patents, which create incentives for firms to deviate from the expected level of information sharing. We document a decrease in 10-K narrative R&D disclosures, more generic 10-K narrative R&D disclosures, and a longer delay of patent disclosures via the USPTO after a firm joins an SSO. Among alternative explanations, our evidence is most supportive of the hypothesis that firms strategically withhold innovation information.

Disclosure is of fundamental interest to accounting research. When the sign/magnitude of disclosed news is unclear, the information in disclosure events is inferred using the ratio of return volatilities during event and non-event windows (Beaver 1968). We show that return noise due to microstructure frictions and mispricing affects this ratio, and that effect is comparable to or exceeds that of information content. We use the SEC's Tick Size Pilot program to confirm the causal effect of return noise on the ratio, and to evaluate alternative ways to control for it. The most promising approach is to use the difference between, rather than the ratio of, return volatilities during event and non-event windows. We illustrate its benefits by showing how it alters prior inferences regarding time-series and cross-sectional variation in information content as well as changes in the information content of earnings announcements around the 2004 amendments to Form 8-K filings.

Although the shareholder benefits of investor conferences are well-documented, evidence on whether these conferences facilitate managerial opportunism is scarce. We examine whether managers opportunistically exploit heightened attention around the conference to “hype” the stock. We find that (1) managers increase the quantity of voluntary disclosure leading up to the conference, (2) these disclosures are more positive in tone and increase prices to a greater extent than post-conference disclosures, and (3) these disclosures are more pronounced when insiders sell their shares immediately prior to the conference. In circumstances where pre-conference disclosures coincide with pre-conference insider net selling, we find evidence of a significant return reversal––large positive returns before the conference and large negative returns after the conference––and that the firm is more likely to be named in a securities class action lawsuit. Collectively, our findings are consistent with some managers hyping the stock prior to the conference.

We investigate whether issuers on negative credit watch manage earnings upward and whether such earnings management favorably influences the watch resolution. We find that rating, industry, and performance matched discretionary accruals reported during negative watch are significantly higher than their respective pre- and post-watch levels, after controlling for accrual reversal. Consistent with its opportunistic nature, we find that accrual management increases with issuers' incentives to avoid downgrade, and decreases with their earnings management constraints and the strength of the external monitoring. Surprisingly, such accrual management significantly increases the likelihood of a favorable resolution—issuers in the top half of the discretionary accruals distribution are 24 percent less likely to be downgraded than those in the propensity score matched bottom half. We find that issuers that avoid downgrades through income-increasing accrual management significantly underperform those that do not over the ensuing year, mitigating the signaling or measurement error explanations for our results. Finally, we find that accrual management does not reflect attempts to improve short-term credit quality.