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 examines the predictive power of the U.S. term structure over return volatility in emerging stock markets. Decomposing the term structure of U.S. Treasury yields into two components, the expectations factor and the maturity premium, we show that the U.S. term structure indeed contains...

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...

This study examines the relationship between investor sentiment and intraday return dynamics for safe haven assets, with particular focus on crash risk in these assets. Examining intraday returns for a wide range of safe havens proposed in the literature, we find that shocks to investor...

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...