Traditional empirical finance research uses hand-engineering and trial-and-error to look for the anomalies in the cross-section of stock returns. In this paper, we take advantage of deep learning and utilize both the price and fundamental information to separate stocks' winners from losers. For...

We create a market-wide measure of dispersion in options investors' expectations by aggregating across all stocks the dispersion in trading volume across moneynesses (DISP). DISP exhibits strong negative predictive power for future market returns and its information content is not subsumed by...

We obtain new methodological and empirical perspectives on the fundamental risk-return tradeoff in stock returns by imposing economic and asset pricing motivated constraints on the equity premium. In contrast to highly ambiguous past empirical findings, these constraints result in a nonlinear...

Recent studies find evidence in favour of return predictability, and argue that their positive findings result from their ability to capture expected returns. We assess the forecasting performance of two popular approaches to estimating expected equity returns, a dividend discount model (DDM)...

A significant number of institutional investors publicly state the belief that corporate stakeholder relations are associated with firm value in a manner that the financial market fails to understand. We investigate whether stakeholder information predicted risk-adjusted returns due to errors in...

We show that news stories contain information about economic linkages between firms and document that information diffuses slowly across linked stocks. Specifically, we identify linked stocks from co-mentions in news stories and find that linked stocks cross-predict one another's returns in the...

It is the literature consensus that firm size and book-to-market equity ratio (B/M) play important roles in explaining the return rate of stocks. The objective of this paper is to investigate the detailed relationship of stock index returns between different segments of the size factor and the...

This study investigates how returns on the S&P 500 index respond orthogonally to dividend yield and price-to-earnings innovations. The unrestricted vector autoregressive (VAR) analysis of monthly data from 1871 to 2012 shows that the response of returns on the S&P 500 index to dividend yield...

We develop a new measure for the probability of informed trading, called PCP. Using double-sorted portfolios, we find that excess returns increase from low to high PCP portfolios. In regression analysis, the effect of PCP on returns is significantly positive after controlling for illiquidity...

We study whether climate policy uncertainty (CPU) is priced cross-sectionally in individual stocks and find a significant negative relation. On average, the risk-adjusted annual future returns of stocks with low exposure to CPU are 6.5%–7.7% greater than the returns of stocks with high...