Deep learning is an active area of research in machine learning. I train deep feedforward neural networks (DFN) based on a set of 68 firm characteristics (FC) to predict the US cross-section of stock returns. After applying a network optimization strategy, I find that DFN long-short portfolios...

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...

Based on 58,256 news articles published in the Financial Times during a 15-year period that cover companies in the DJIA, we find that a trading strategy that longs stocks with the most negative news and shorts stocks with the least negative news is not profitable. Consistent with this result, we...

The recent increase in passive investment products has provided investors with easy access to international markets. The basic motivation of this paper is to offer new tools to investors who want to allocate assets across countries. This study investigates the performance of equity country...

The value of an equity investment can be framed as an embedded call option on a firm's assets. The embedded call option creates a non-linear relationship between stock returns and underlying risk factors; however, such option value and the impact of this non-linearity are often underestimated or...

This paper suggests a novel approach for predicting aggregate stock returns at quarterly and annual frequencies. Weak return predictability is consistent with the view that a stationary component of stock prices is highly persistent. In such cases, expected returns are time-varying but also...

Recent evidence indicates that market model alphas are stronger predictors of mutual fund flows than alphas with other models. Berk and van Binsbergen (2016) claim that this evidence indicates CAPM is the best asset pricing model but Barber, Huang and Odean (2016) (BHO) claim it is evidence...

We examine the effect of ambiguity exposure on the cross-section of stock returns in the US equity market. In order to quantify ambiguity, we use a recently-developed methodology that measures ambiguity by perturbations in uncertain probabilities, and aversion to ambiguity by aversion to...

We use intraday data to compute weekly realized variance, skewness, and kurtosis for equity returns and study the realized moments' time-series and cross-sectional properties. We investigate if this week's realized moments are informative for the cross-section of next week's stock returns. We...

We build a parsimonious international asset pricing model in which deviations of government bond yields from a fitted yield curve of a country measure the tightness of investors' capital constraints. We compute these measures at daily frequency for six major markets and use them to test the...