Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions

Guizhu Shen · Qingping Tan · Haoyu Zhang · Jianjun Xu

304 citations0/0 files storedProcedia Computer Science

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Stock Market Forecasting Methods Neural Networks and Applications Time Series Analysis and Forecasting

Abstract

Gated recurrent unit (GRU) networks perform well in sequence learning tasks and overcome the problems of vanishing and explosion of gradients in traditional recurrent neural networks (RNNs) when learning long-term dependencies. Although they apply essentially to financial time series predictions, they are seldom used in the field. To fill this void, we propose GRU networks and its improved version for predicting trading signals for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index from 1991 to 2017, and compare the GRU-based models with the traditional deep net and the benchmark classifier support vector machine (SVM). Experimental results show that the two GRU models proposed in this paper both obtain higher prediction accuracy on these data sets, and the improved version can effectively improve the learning ability of the model.

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References

1970EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*1970Efficient Capital Markets: A Review of Theory and Empirical Work 2016Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 2005The use of data mining and neural networks for forecasting stock market returns

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