We merge the literature on downside return risk and liquidity risk and introduce the concept of extreme downside liquidity (EDL) risks. The cross-section of stock returns reflects a premium if a stock's return (liquidity) is lowest at the same time when the market liquidity (return) is lowest....

This paper proposes a risk-based explanation of the momentum anomaly on equity markets. Regressing the momentum strategy return on the return of a self-financing portfolio going long (short) in stocks with high (low) crash sensitivity in the USA from 1963 to 2012 reduces the momentum effect from...

We examine the pricing of tail risk in international stock markets. We find that the tail risk of different countries is highly integrated. Introducing a new World Fear index, we find that local and global aggregate market returns are mainly driven by global tail risk rather than local tail...

We report strong evidence that changes of momentum, i.e. "acceleration", defined as the first difference of successive returns, provide better performance and higher explanatory power than momentum. The corresponding Γ-factor explains the momentum-sorted portfolios entirely but not the reverse....

We examine hurricane exposure as a systematic risk factor in the US stock market. Motivated by a consumption-based asset pricing model with heterogeneous agents, we derive a necessary and sufficient condition for a hurricane risk premium in the cross-section of stock returns. Empirically, we...

In the financial markets, contradictory opinions generate a set of constraints which mediate information through a system of expected target prices. As a result, prices are a measure of value as much as they are an indication of how these expectations concerning value remain valid. Thus, path...

Stock market fundamentals would not seem to meaningfully predict returns over a shorter-term horizon - instead, I shift focus to severe downside risk (i.e., crashes). I use the cointegrating relationship between the log S&P Composite Index and log earnings over 1871 to 2015, combined with...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...