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 signifcant correlations between Reuters sentiment and stock returns. We...

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...

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 F score in global emerging markets and show there is a meaningful premium attached to high F score stocks which is unrelated to the size, value and momentum premiums. It is larger for high value stocks, moderately higher for high momentum stocks and unrelated to stock size. This...

Sentiment from over 3.6 million Reuters news articles is tested in a vector autoregression model framework on its ability to forecast returns of the Dow Jones Industrials stock index. We show that Reuters sentiment can explain and predict changes in stock returns better than macroeconomic...

We construct a price, dividend, and earnings series for the Industrials sector, the Utilities sector, and the Railroads sector from the beginning of the 1870s until the beginning of the year 2013 from primary sources. To infer about mispricings in the sector markets over more than a century, we...

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...

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...

We examine the cross-section of international equity risk premia with machine learning methods. We identify, classify, and calculate 88 market characteristics and use them to forecast country returns with various machine learning techniques. While all algorithms produce substantial economic...

This paper evaluates the evidence on return predictability from an economic perspective: it asks whether investors would have been able and willing to exploit dividend price signals in order to allocate capital efficiently. We use a simple model that incorporates a time varying investment...