We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

This paper demonstrates that the forecasted CAPM beta of momentum portfolios explains a large portion of the return, ranging from 40% to 60% for stock level momentum, and 30% to 50% for industry level momentum. Beta forecasts are from a realized beta estimator using daily returns over the prior...

Implied correlation and variance risk premium stand out in predicting market returns. However, while the predictive ability of implied correlation lasts for up to a year, the variance risk premium predicts market returns only for one quarter ahead. Contrary to the accepted view, implied...

We show that an increase in a stock's breadth of institutional ownership or turnover is followed by a significant but temporary increase in its CAPM beta estimate and a decrease in its CAPM alpha. The increasing effect of breadth of ownership on beta estimates strengthens if we classify...

This paper examines the funding liquidity faced by hedge funds and the resulting implication for stocks' excess return co-movement. We find that hedge fund ownership tends to induce a higher stocks' return co-movement with each other, compared to other institutional investors like mutual funds...

I examine the sample selection bias in portfolio horse race. Numerous studies propose mean-variance portfolio rules to outperform the naive 1/N portfolio rule. However, the outperformance is often justified by a small number of pre-selected datasets. Using a new performance test based on a large...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

Rational expectation equilibrium (REE) models were considerably developed over the past 40 years. However, still relatively little has been done on their empirical applications, private signals being unobservable. We propose a new methodology, theoretically premised, to reconstitute these...