Most statistical arbitrage strategies in the academic literature soley rely on price time series. By contrast, alternative data sources are of growing importance for professional investors. We contribute to bridging this gap by assessing the price-predictive value of more than nine million...

We present a comprehensive simulation study to assess and compare the performance of popular machine learning algorithms for time series prediction tasks. Specifically, we consider the following algorithms: multilayer perceptron (MLP), logistic regression, naïve Bayes, knearest neighbors,...

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...

This paper examines earnings momentum strategies in the U.S. stock universe from an investor's perspective. Specifically, we use the software Stock Investor Pro from the American Association of Individual Investors (AAII) to obtain the composition of the U.S. stock universe from 2005-2015 on a...

Machine learning is increasingly applied to time series data, as it constitutes an attractive alternative to forecasts based on traditional time series models. For independent and identically distributed observations, cross-validation is the prevalent scheme for estimating out-of-sample...

We apply state-of-the-art financial machine learning to assess the return-predictive value of more than 45,000 earnings announcements on a majority of S&P1500 constituents. To represent the diverse information content of earnings announcements, we generate predictor variables based on various...

We apply state-of-the-art financial machine learning to assess the return-predictive value of more than 45,000 earnings announcements on a majority of S&P1500 constituents. To represent the diverse information content of earnings announcements, we generate predictor variables based on various...