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...

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...

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 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...

We conduct a comprehensive asset pricing analysis for the U.S. property/liability insurance industry using monthly data from 1988 to 2015. We find that state-of-the-art models such as the Fama and French (2015) five-factor model cannot explain the returns of property/liability insurance stocks...

We decompose total variance into its bad and good components and measure the premia associated with their fluctuations using stock and option data from a large cross-section of firms. The total variance risk premium (VRP) represents the premium paid to insure against fluctuations in bad variance...

A prominent factor used in most models predicting stock returns is firm size. Yet no consensus has emerged on the magnitude and stability of the size premium, with some researchers even questioning the usefulness of the factor. To take stock of the voluminous academic literature on the size...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

We derive a model-free option-based formula to estimate the contribution of market frictions to expected returns (CFER) within an asset pricing setting. We estimate CFER for the U.S. optionable stocks. We document that CFER is sizable, it predicts stock returns and it subsumes the effect of...

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....