This paper develops a general equilibrium model and provides empirical support that the market volatility-of-volatility (VOV) predicts market returns and drives the time-varying volatility risk. In asset pricing tests with the market, volatility, and VOV as factors, the risk premium on VOV is...

Exploiting a screen display feature whereby the order of stock display is determined by the stock listing codes, we lever a novel identification strategy and study the impact of attention spillover on stock prices and turnover. We find that stocks with neighbors on the display that experience...

We study learning and uncertainty under the factor investing paradigm using an endogenous information model with correlated assets. As investors shift attention from firms towards systematic risk factors, stock prices become less informative, increasing systematic uncertainty and incentivizing...

This paper shows that the premium for systematic skewness in individual stocks is positive on average, has negative realized systematic skewness, and is time varying. When skewness preference is high rather than low, the risk premium is 4% higher. Systematic skewness also has significant...

The salience theory perspective on asset prices implies that investors overvalue stocks with salient upsides and undervaluing firms with salient downsides. The resulting mispricing is subsequently reverted, producing a predictable pattern in the cross-section of returns. This study is the first...

Investors’ return on their portfolios, as proxied by the market, is a theoretically appealing but empirically unsuccessful asset pricing factor. In practice, many institutional investors choose to deviate substantially from the market portfolio. We propose a simple model in the spirit of...

Controlling for numerous attributes tied to default and priced asset risk, including yield, credit spread, bond rating, and maturity, we find that a corporate bond’s book value divided by its market price strongly predicts its return. Bonds with the 20% highest “bond book-to-market ratios”...

Controlling for numerous attributes tied to default and priced asset risk, including yield, credit spread, bond rating, and maturity, we find that a corporate bond’s book value divided by its market price strongly predicts its return. Bonds with the 20% highest “bond book-to-market ratios”...

This paper evaluates the predictive performance of machine learning techniques in estimating time-varying betas of US stocks. Compared to established estimators, tree-based models and neural networks outperform from both a statistical and an economic perspective. Random forests perform the best...

Several academics have studied the ability of hybrid models mixing univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and neural networks to deliver better volatility predictions than purely econometric models. Despite presenting very promising results, the...