In this paper, we provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and implied volatility, and find that implied...

The consumption-based models have a lack of predictive power for explaining variability of stock returns. This paper examines two well-known models, Campbell and Cochrane (1999)'s habit model and Bansal and Yaron (2004)'s long-run risks model, to see whether they produce a significant power of...

This article analyzes the manifold situations in which the efficient-market hypothesis (EMH) has influenced — or has failed to influence — federal securities regulation and state corporate law, and the prospective roles for the EMH in these contexts. In federal securities regulation, the EMH...

Predicted stock issuers (PSIs) are firms with expected “high-investment and low-profit” (HILP) profiles that earn unusually low returns. We carefully document important features of PSI firms to provide insights on the economic mechanism behind the HILP phenomenon. Top-PSI firms are...

We analyze the relation between volatility and speculative activities in the crude oil futures market and provide short-term forecasts accordingly. By incorporating trading volume and opening interest (speculative ratio) into the volatility dynamics, we document the subtle interaction between...

Presentation Slides for "Overconfidence, Arbitrage, and Equilibrium Asset Pricing" This paper offers a model in which asset prices reflect both covariance risk and misperceptions of firmsapos prospects, and in which arbitrageurs trade against mispricing. In equilibrium, expected returns are...

As supply chain channels physical, financial, and information flows as well as associated risks, a firm’s supply chain information should be helpful in understanding and predicting its credit risks. Credit ratings as an approximate but important measure of corporate credit risks have been...

We use machine learning techniques to conduct out-of-sample predictions of the underpricing of U.S. initial public offerings (IPOs) from 1990 to 2019. Using predicted underpricing based on ex ante information to sort the IPOs into 10 groups, we find that the underpricing averages for the top and...

We document an improvement in analysts’ forecast accuracy following increased sector ETF ownership. We identify a possible channel for this result, i.e., because ETFs are more informative with respect to industry-level information, analysts learn directly and efficiently from ETFs about this...

This paper reviews research that uses big data and/or machine learning methods to provide insight relevant for equity valuation. Given the huge volume of research in this area, the review focuses on studies that either use or inform on accounting variables. The article concludes by providing...