We study the predictability of stock returns using an iterative model-building approach known as quantile boosting. Examining alternative return quantiles that represent normal, bull and bear markets via recursive quantile regressions, we trace the predictive value of extensively studied...

This paper explores whether firm characteristics matter in determining the effect of investor herding on asset returns. We find that the level of herding alone does not command a significant effect on industry returns, implied by insignificant return spreads between industries that experience...

In a novel take on the gradual information diffusion hypothesis of Hong et al. (2007), we examine the predictive role of industries over aggregate stock market volatility. Using high frequency data for U.S. industry indexes and various heterogeneous autoregressive (HAR) type and machine learning...

Utilizing a dataset of 1,899 U.S. hedge funds, we present evidence of anti-herding behavior among hedge fund managers in the U.S. Hedge funds anti-herd primarily based on fundamental information and irrespective of market volatility and credit deterioration conditions although funding...

Yes, they do. Utilizing a machine-learning technique known as random forests to compute forecasts of realized (good and bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out-of sample forecasts of aggregate stock market...