Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions

By Guizhu Shen, QingPing Tan, Haoyu Zhang, P. Zeng, Jianjun Xu. 2018

262 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

  1. Omer Berat Sezer
  2. A. Ozbayoglu
  3. M. Ugur Gudelek

In Finance

  1. M. Ugur Gudelek
  2. A. Ozbayoglu
  3. Omer Berat Sezer
  4. Weiwei Jiang
  5. Longbing Cao

In Management Science and Operations Research

  1. M. Ugur Gudelek
  2. Omer Berat Sezer
  3. A. Ozbayoglu
  4. Weiwei Jiang
  5. Longbing Cao

In Economics & Operations Research

  1. A. Ozbayoglu
  2. M. Ugur Gudelek
  3. Omer Berat Sezer

In Time-Series & Forecasting

  1. M. Ugur Gudelek
  2. Omer Berat Sezer
  3. A. Ozbayoglu
  4. Weiwei Jiang
  5. Longbing Cao

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