A growing body of literature analyses the impact of news on companies' equity prices. We add to this literature by showing that the transmission channel of news to prices differs across sectors. First, we disentangle sectoral equity prices into components of expected future earnings and equity...

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....

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

We develop a four-factor model intended to capture size, value, and credit rating transition patterns in excess returns for a panel of predominantly mid- and large-cap entities. Using credit transition matrices and rating histories from 48 US issuers, we provide evidence to support a...

We examine the pricing of both aggregate jump and volatility risk in the cross-section of stock returns by constructing investable option trading strategies that load on one factor but are orthogonal to the other. Both aggregate jump and volatility risk help explain variation in expected...

We generalize the Ferreira and Santa-Clara (2011) sum-of-the-parts method for forecasting stock market returns. Rather than summing the parts of stock returns, we suggest summing some of the frequency-decomposed parts. The proposed method signi cantly improves upon the original sum-of-the-parts...

In this paper we investigate sources and characteristics of value, size and momentum profits on the Polish stock market. The research aims to broaden the academic knowledge in a few ways. First, we deliver fresh out-of-sample evidence on value, momentum, and size premiums. Second, we analyzemthe...

This paper studies the out-of-sample predictability of the monthly market as well as size, value, and momentum premiums. We use a sample from each the US and the Swiss stock market between 1989 and 2007. Our Swiss sample provides an important new perspective as the repeated evaluation of the...

We find a negative relationship between the individual stocks' semivariance premia, defined as the difference between the risk-neutral and physical expected downside semivariances, and future stock returns. The high-minus-low hedge portfolio earns the excess return of -64 (-46) basis points per...

This paper evaluates the performance of machine learning methods in forecasting stock returns. Compared to a linear benchmark model, interactions and non-linear effects help improve predictive performance. But machine learning models must be adequately trained and tuned to overcome the high...