The results of academic and practitioners' event studies are often translated from excess log returns into excess dollar returns. The prior literature argues for a difference between the statistical significance of excess log returns and that of excess dollar returns. In contrast, we show...

This paper investigates how stock-specific and market-wide news sentiments, obtained from Thomson Reuters News Analytics, affect abnormal returns of S&P 500 stocks. It is well-known that the relationships between the stock-specific news sentiment and raw stock returns are rather weak. This can...

This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise...

This paper provides evidence that the market does not efficiently incorporate expected returns implied by analyst price targets into prices. I use a novel decomposition to extract information and bias components from these analyst-expected returns and develop an asset pricing framework that...

Motivated by investor disagreement and corporate disclosure literatures, we examine how stock price shocks affect future stock returns. We find that both large short-term price drops and hikes are followed by negative abnormal returns over the subsequent year, consistent with the conjecture that...

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. Bid and ask returns are decomposed into a common ("efficient return") factor and two market-side-specific components...

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. We decompose bid and ask returns into a common ("efficient return") factor and two market-side-specific components...

This paper examines how the release of industry rivals' earnings news during the IPO book-building period affects a firm's process of going public. The aggregate effect of rivals' earnings news is measured by a signal-to-noise ratio. Higher signal-to-noise ratios indicate better rivals' earnings...

Based on a standard Bayesian learning model, we propose a new measure of differential interpretation of public information, which is applicable to firms with analyst following. We validate our measure in the context of earnings announcements and provide evidence of its applicability relative to...

This paper studies the implications of disclosure repetitiveness on firm performance, information processing costs, and future stock returns. I propose a compression-based method to measure disclosure repetitiveness, which is assumption-free with respect to the underlying language models. I then...