Pattern Learning Via Artificial Neural Networks for Financial Market Predictions by Andreas Gabler, Dominique Perez, Ueli Sutter, Daniel Kucharczyk, Joerg Osterrieder, Markus Reitenbach :: SSRN

Convolutional neural networks (CNN) and long short-term memory (LSTM) networks have become a staple of sequence learning. Due to the well-established fact that financial time series data exhibit exceptionally noisy characteristics, capital market anomalies are virtually impossible to detect. We deploy CNN networks for predicting out-of-sample stock movements for 200 high-volume European stocks from 1994 until 2014, and compare its overall performance with a modified LSTM model as in Fischer, Krauss (2017). Specifically, we compare empirical training and validation accuracies of both model architectures and reveal portfolio performance characteristics in terms of return and risk metrics for different portfolio sizes, trying to derive common patterns within the top and flop stocks. Thus, we unveil sources of long-term profitability and demonstrate, that both LSTM and CNN networks are able to extract meaningful information from such noisy financial time series.