Abstract: Though recurrent neural networks (RNN) outperform traditional machine learning algorithms in the detection of long-term dependencies among the training instances, such as in term sequences in sentences or among values in time series, surprisingly few studies so far have deployed concrete solutions with RNNs for the stock market trading. Presumably the current difficulties of training RNNs have contributed to discourage their wide adoption.This work presents a simple but effective solution, based on a deep RNN, whose gains in trading with Dow Jones Industrial Average (DJIA) outperform the state-of-the-art, moreover the gain is 50% higher than that produced by similar feed forward deep neural networks. The trading actions are driven by the predictions of the price movements of DJIA, using simply its publicly available historical series. To improve the reliability of results with respect to the literature, we have experimented the approach on a long consecutive period of 18 years of historical DJIA series, from 2000 to 2017. In 8 years of trading in the test set period from 2009 to 2017, the solution has quintupled the initial capital, moreover since DJIA has on average an increasing trend, we also tested the approach with a decreasing averagely trend by simply inverting the same historical series of DJIA. In this extreme case, in which hardly any investor would risk money, the approach has more than doubled the initial capital.(More)

Though recurrent neural networks (RNN) outperform traditional machine learning algorithms in the detection of long-term dependencies among the training instances, such as in term sequences in sentences or among values in time series, surprisingly few studies so far have deployed concrete solutions with RNNs for the stock market trading. Presumably the current difficulties of training RNNs have contributed to discourage their wide adoption.This work presents a simple but effective solution, based on a deep RNN, whose gains in trading with Dow Jones Industrial Average (DJIA) outperform the state-of-the-art, moreover the gain is 50% higher than that produced by similar feed forward deep neural networks. The trading actions are driven by the predictions of the price movements of DJIA, using simply its publicly available historical series. To improve the reliability of results with respect to the literature, we have experimented the approach on a long consecutive period of 18 years of historical DJIA series, from 2000 to 2017. In 8 years of trading in the test set period from 2009 to 2017, the solution has quintupled the initial capital, moreover since DJIA has on average an increasing trend, we also tested the approach with a decreasing averagely trend by simply inverting the same historical series of DJIA. In this extreme case, in which hardly any investor would risk money, the approach has more than doubled the initial capital.

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Fabbri, M. and Moro, G. (2018). Dow Jones Trading with Deep Learning: The Unreasonable Effectiveness of Recurrent Neural Networks.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 142-153. DOI: 10.5220/0006922101420153

@conference{data18, author={Mirco Fabbri. and Gianluca Moro.}, title={Dow Jones Trading with Deep Learning: The Unreasonable Effectiveness of Recurrent Neural Networks}, booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,}, year={2018}, pages={142-153}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0006922101420153}, isbn={978-989-758-318-6}, }

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, TI - Dow Jones Trading with Deep Learning: The Unreasonable Effectiveness of Recurrent Neural Networks SN - 978-989-758-318-6 AU - Fabbri, M. AU - Moro, G. PY - 2018 SP - 142 EP - 153 DO - 10.5220/0006922101420153