Market-wide attention-grabbing events — record levels for the Dow and front-page articles about the stock market — predict the trading behavior of investors and, in turn, market returns. Both aggregate and household-level data reveal that high market-wide attention events lead investors to sell their stock holdings dramatically when the level of the stock market is high. Such aggressive selling has a negative impact on market prices, reducing market returns by 19 basis points on days following attention-grabbing events.

This paper provides evidence that portfolio disagreement measured bottom-up from individual-stock analyst forecast dispersions has a number of asset pricing implications. For the market portfolio, market disagreement mean-reverts and is negatively related to ex post expected market return. Contemporaneously, an increase in market disagreement manifests as a drop in discount rate. For book-to-market sorted portfolios, the value premium is stronger among high disagreement stocks. The underperformance by high disagreement stocks is stronger among growth stocks. Growth stocks are more sensitive to variations in disagreement relative to value stocks. These findings are consistent with asset pricing theory incorporating belief dispersion.

Theory predicts that the quality of a firm's information disclosure can affect the term structure of its corporate bond yield spreads. Using cross-sectional regression and Nelson-Siegel yield curve estimation, I find that firms with higher Association for Investment Management and Research disclosure rankings tend to have lower credit spreads. Moreover, this transparency spread is especially large among short-term bonds. These findings are consistent with the theory of discretionary disclosure as well as the incomplete accounting information model of Duffie and Lando (Econometrica 69 (2001) 633). The presence of a sizable short-term transparency spread can attenuate some of the empirical problems associated with structural credit risk models.

A definition of market adjustment is proposed in terms of the time it takes market attributes to reflect new information. Properties of the proposed definition are discussed. In order to operationalize the concept, a statistical method is introduced to estimate the adjustment times. Empirical examples are used to illustrate the proposed method. Some possible economic interpretations are given. The properties of the estimator are also investigated by simulation and analytical methods.

Extremely long odds accompany the chance that spurious-regression bias accounts for investor sentiment׳s observed role in stock-return anomalies. We replace investor sentiment with a simulated persistent series in regressions reported by Stambaugh, Yu, and Yuan (2012), who find higher long-short anomaly profits following high sentiment, due entirely to the short leg. Among 200 million simulated regressors, we find none that support those conclusions as strongly as investor sentiment. The key is consistency across anomalies. Obtaining just the predicted signs for the regression coefficients across the 11 anomalies examined in the above study occurs only once for every 43 simulated regressors.

This study explores the role of investor sentiment in a broad set of anomalies in cross-sectional stock returns. We consider a setting in which the presence of market-wide sentiment is combined with the argument that overpricing should be more prevalent than underpricing, due to short-sale impediments. Long-short strategies that exploit the anomalies exhibit profits consistent with this setting. First, each anomaly is stronger (its long-short strategy is more profitable) following high levels of sentiment. Second, the short leg of each strategy is more profitable following high sentiment. Finally, sentiment exhibits no relation to returns on the long legs of the strategies.

Decentralized exchanges (DEXs) allow traders to express their willingness to pay for quick execution through a public priority fee bidding mechanism. We provide evidence that high-fee DEX trades are more informative and contribute more to price discovery. Using address-level blockchain transaction data, we show that informed traders persistently bid higher fees to secure early execution, revealing a strong willingness to pay for execution priority. Further, analysis of Ethereum mempool data demonstrates that informed traders employ a “jump bidding” strategy, placing high initial bids to deter potential competitors.