Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/157808
Authors: 
Fischer, Thomas
Krauss, Christopher
Year of Publication: 
2017
Series/Report no.: 
FAU Discussion Papers in Economics 11/2017
Abstract: 
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 constituent stocks of the S&P 500 from 1992 until 2015. With daily returns of 0.46 percent and a Sharpe Ratio of 5.8 prior to transaction costs, we find LSTM networks to outperform memory-free classification methods, i.e., a random forest (RAF), a deep neural net (DNN), and a logistic regression classifier (LOG). We unveil sources of profitability, thereby shedding light into the black box of artificial neural networks. Specifically, we find one common pattern among the stocks selected for trading - they exhibit high volatility and a short-term reversal return profile. Leveraging these findings, we are able to formalize a rules-based short-term reversal strategy that is able to explain a portion of the returns of the LSTM.
Subjects: 
finance
statistical arbitrage
LSTM
machine learning
deep learning
Document Type: 
Working Paper

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