International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 12, No. 6, 8 Dec. 2020
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Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks
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Author(s)
Muhammad Zulqarnain
1,*
Rozaida Ghazali
1
Muhammad Ghulam Ghouse
1
Yana Mazwin Mohmad Hassim
1
Irfan Javid
1
Index Terms
Deep learning, RNN, GRU, CNN, financial time series prediction
Abstract
Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index range 2008 to 2016, and associate the GRU-CNN based approaches with the existing deep learning models. Experimental results present that the proposed GRU-CNN model obtained the best prediction accuracy 56.2% on HIS dataset, 56.1% on DAX dataset and 56.3% on S&P500 dataset respectively.
Cite This Paper
Muhammad Zulqarnain, Rozaida Ghazali, Muhammad Ghulam Ghouse, Yana Mazwin Mohmad Hassim, Irfan Javid, "Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks ", International Journal of Intelligent Systems and Applications(IJISA), Vol.12, No.6, pp.21-32, 2020. DOI:10.5815/ijisa.2020.06.02
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