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Employing deep learning for stock return prediction on the Oslo Stock Exchange : a comprehensive test of deep learning models for predictive purposes and applicability to trading

Lund, Henrik; Løvås, Jonas
Master thesis
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URI
http://hdl.handle.net/11250/2586263
Date
2018
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  • Master Thesis [3749]
Abstract
We predict daily out-of sample directional movements of the constituent stocks of the Oslo

Stock Exchange Benchmark Index (OSEBX) using Long Short-Term Memory (LSTM) net

works, benchmarked against other machine learning and econometric techniques. Our results

unambiguously show that the LSTM model outperforms all benchmark models in terms of pre

dictive performance. When testing simple long trading strategies utilizing the predictions, we

find that the LSTM model outperform all other methods with a Sharpe ratio of 3.25 prior to

transaction costs from 1999 - 2017. In comparison, the OSEBX had a Sharpe ratio of 0.30 over

the same period. We find that the LSTM model seems to follow a short-term mean-reversion

strategy. While seeing somewhat diminishing excess returns in the last years, the excess returns

are still present in the three latest years, which differs from similar studies on other indexes

where the excess returns have been found to be absent in recent years. When total transaction

costs are implemented we see that the excess returns are lost in the bid-ask spread. Training the

model on spread-adjusted returns and imposing advanced strategies leads to a modest Sharpe

ratio of 0.37 over the whole period. Even though the trading performance after total transaction

costs is not statistically significant better than the OSEBX, we see that LSTM networks have

predictive properties that can make it a great tool and complement to other trading strategies.

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