The adjusted measure of realized volatility suggested in [20] is applied to high- frequency orderbook and transaction data of DAX and BUND futures from EU- REX in order to identify the drivers of intraday volatility. Four components are identified to have predictive power: an auto-regressive...

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...

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 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,...