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...

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...

We develop a multivariate statistical arbitrage strategy based on vine copulas - a highly flexible instrument for linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and economically significant returns of 9.25 percent p.a. and...

This paper presents the first large-scale application of deep reinforcement learning to optimize the placement of limit orders at cryptocurrency exchanges. For training and out-of-sample evaluation, we use a virtual limit order exchange to reward agents according to the realized shortfall over a...

This paper presents the first large-scale application of deep reinforcement learning to optimize the placement of limit orders at cryptocurrency exchanges. For training and out-of-sample evaluation, we use a virtual limit order exchange to reward agents according to the realized shortfall over a...