Much of capital market research in accounting over the past 20 years has assumed that the price adjustment process to information is instantaneous and/or trivial. This assumption has had an enormous influence on the way we select research topics, design empirical tests, and interpret research findings. In this discussion, I argue that price discovery is a complex process, deserving of more attention. I highlight significant problems associated with a naı̈ve view of market efficiency, and advocate a more general model involving noise traders. Finally, I discuss the implications of recent evidence against market efficiency for future research.

This study separates trading volume into buyer- and seller-initiated activities and examines the directional volume reaction in small and large trades to different types of earnings news. ‘Good’ (‘bad’) news triggers brief, but intense, buying (selling) in the large trades. However, a persistent period of unusually high buying activity is observed in the small trades irrespective of the news. This anomalous proclivity of small traders to buy is robust across firm size, trading volume, and different earnings expectation models. Several explanations are discussed, although the behavior does not seem fully explained by existing theories.

Between 2010 and 2017, Chinese investors used an investor interactive platform (IIP) to ask public companies around 2.5 million questions, the vast majority of which received a reply within two weeks. We analyze these IIP dialogues using a BERT-based algorithm and provide preliminary evidence on their causes and consequences. Our analyses show most questions reflect investors’ difficulties in processing information already in the public domain. Controlling for other news, higher IIP activity is associated with increases in trading volume, return volatility, market liquidity, and price informativeness as well as decreases in bid-ask spread. Financial statement-related postings increase around the adoption of new accounting standards. Collectively, our results show that investors face significant information processing costs but that IIP activities help reduce these costs, leading to improvements in stock price formation.

This study examines the usefulness of an analyst-based valuation model in predicting cross-sectional stock returns. We estimate firms' fundamental values (V) using I/B/E/S consensus forecasts and a residual income model. We find that V is highly correlated with contemporaneous stock price, and that the V/P ratio is a good predictor of long-term cross-sectional returns. This effect is not explained by a firm's market beta, B/P ratio, or total market capitalization. In addition, we find errors in consensus analyst earnings forecasts are predictable, and that the predictive power of V/P can be improved by incorporating these errors.

We examine the economic determinants of short-sale supply, and its consequences for future stock returns. Lendable supply increases with expected borrowing costs and decreases with financial statement constructs that indicate overvaluation. Although rising loan fees help ease supply constraints, we find shares are still least available when they are most attractive to short sellers. Using a number of firm characteristics, we derive useful instruments for real-time loan supply and demand conditions in the lending market. Further, we show that (1) when lendable supply is binding (non-binding), short-sale supply (demand) is the main predictor of future stock returns, (2) abnormal returns to the short-side of nine well-known market anomalies are attributable solely to “special” stocks, and (3) loan fees significantly reduce the profitability of the short side and several of these anomalies cease to be profitable. Overall our evidence highlights the central role played by the supply of lendable shares in equity price formation and returns prediction.