Average skewness, which is defined as the average of monthly skewness values across firms, performs well at predicting future market returns. This result still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. We also find that average...

We examine the statistical power of fundamental and behavioural factors with regards to stock returns of the Dow Jones Industrials Index. With a novel sentiment dataset from over 3.6 million Reuters news articles, we find significant correlations between Reuters sentiment and stock returns. We...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

This paper investigates whether news suggestive of irrationality within financial markets have an impact on stock returns. We construct a lexicon of words for 'market irrationality' and score daily news articles based on the number and proportion of words they contain from the lexicon. We find...

Three concepts: stochastic discount factors, multi-beta pricing and mean-variance efficiency, are at the core of modern empirical asset pricing. This chapter reviews these paradigms and the relations among them, concentrating on conditional asset-pricing models where lagged variables serve as...

Anomalies are empirical results that seem to be inconsistent with maintained theories of asset-pricing behavior. They indicate either market inefficiency (profit opportunities) or inadequacies in the underlying asset-pricing model. After they are documented and analyzed in the academic...

We analyze the earnings information and stock prices of S&P500 firms and find that investors following S&P500 stocks (i) respond more to pro forma earnings than to GAAP earnings, (ii) respond to an emphasis on pro forma earnings, and (iii) are fixated on pro forma earnings. We provide the first...