We use machine learning tools to analyze industry return predictability based on theinformation in lagged industry returns from across the entire economy. Controlling forpost-selection inference and multiple testing, we nd significant in-sample evidence ofindustry return predictability. Lagged...

This paper characterizes the forces that determine time-variation in expected international asset returns. We offer a number of innovations. By using the latent factor technique, we do not have to prespecify the sources of risk. We solve for the latent premiums and characterize their...

We consider a setting where investors receive private signals about cash flows as well as their betas. We obtain a closed-form solution for the case where informed agents are risk neutral and the market maker is risk averse. Market liquidity is non-linear and non-monotonic (under reasonable...

We explore the effects of fundamental extrapolation on stock returns. Empirically, we propose a novel approach to extrapolate firms' fundamental information and find that a strategy based on fundamental extrapolation earns an average return of 0.80% per month. Theoretically, we show that...

The identical cash flow rights of Chinese A and B shares provide a natural experiment that allows us to explore how investor clienteles affect stock return patterns. Chinese domestic retail investors are responsible for the majority of trades in A shares, while foreign institutional investors...

This paper extends the machine learning methods developed in Han et al. (2019) for forecasting cross-sectional stock returns to a time-series context. The methods use the elastic net to refine the simple combination return forecast from Rapach et al. (2010). In a time-series application focused...

This paper constructs a manager sentiment index based on the aggregated textual tone of corporate financial disclosures. We find that manager sentiment is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R2 of 9.75% and 8.38%,...

While trading appears to be hazardous to most individual investors' wealth, some individual investors with well-functioning informational networks may be able to turn a profit. Indeed, we find that in the Chinese stock market, wealthy investors with portfolio values above the 99.5th percentile...

Our research on data for the S&P 500 ETF from 1993-2013 documents an intraday momentum pattern: the first half-hour return on the market (from the previous day's close) predicts the last half-hour return. The predictability, both statistically and economically significant, is stronger on more...

This paper shows that investors do not fully incorporate cost behavior information into valuation. Firms with higher growth in operating costs generate substantially lower future stock returns and operating performance. An equal-weighted long-short spread portfolio earns an average return of 82...