The performance analysis of stock predication based on recurrent neural network

Research Article
Open access

The performance analysis of stock predication based on recurrent neural network

Shiying Wang 1* , Xinyu Yao 2
  • 1 East China Jiaotong University    
  • 2 Capital Normal University    
  • *corresponding author 2020041011000105@ecjtu.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230696
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

The stock exchange is unpredictable, and the stock price seems unpredictable. However, with the continuous development of the deep learning model's ability to deal with massive data, forecasting stock prices has become feasible and has reference value for investors. Many factors affect the stock price, and it is a great challenge to define these factors' influence on the price clearly. This paper selects multi-features stock price data sets of different companies. Because of the superiority of recurrent neural networks in dealing with time series problems, this paper compares and analyzes the experimental results of four models, namely Long Short Term Memory, Bi-directional Long Short-Term Memory, Gate Recurrent Unit, and Bi-directional Gate Recurrent Unit, and concludes that the BiLSTM model is the most outstanding one. At the same time, the prediction accuracy under different feature numbers is compared. The experimental results show that the stock price forecasting model with multi-features shows good performance, but the noise brought by it can't be ignored.

Keywords:

recurrent neural network, stock prediction, data analysis, LSTM, GRU

Wang,S.;Yao,X. (2023). The performance analysis of stock predication based on recurrent neural network. Applied and Computational Engineering,6,1268-1274.
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References

[1]. H.White. Economic prediction using neural networks: the case of IBM daily stock returns. Neural Networks, IEEE International Conference on. 1988.2(6). 451-458

[2]. Gen Cay. R.Non-linear prediction of security returns with moving average rules.Journal of Forecasting,1996,15(3).43-46

[3]. Rodriguez. F, Martel.C. On the profitability of technical trading rules based on artificial neural networks: Evidence form the madrid stock market. Economics Letters.2000.69(1).35-37

[4]. G. Peter Zhang. Time series forecasting using a hybrid ARIMA and neural network model|J]. Neurocomputing.2003(50). 159-175

[5]. A.Murat. Bahadir. Comparison of bayesian estimation and neural network model in stock market trading.Intelligent Engineering Systems through Artificial Neural Networks.2011.20. 74-81

[6]. A.Muratozbayoglu. Neural based techical analysis in stock market forecasting.Intelligent Engineering Systems through Artificial Neural Networks.2008(18). 261-265

[7]. Lapedes, A., & Farber, R. (1987). Nonlinear signal processing using neural networks: Prediction and system modelling (No. LA-UR-87-2662; CONF-8706130-4).

[8]. Fu, R., Zhang, Z., & Li, L. (2016, November). Using LSTM and GRU neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) (pp. 324-328). IEEE.

[9]. Dey, R., & Salem, F. M. (2017, August). Gate-variants of gated recurrent unit (GRU) neural networks. In 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS) (pp. 1597-1600). IEEE.

[10]. Bansal, T., Belanger, D., & McCallum, A. (2016, September). Ask the gru: Multi-task learning for deep text recommendations. In proceedings of the 10th ACM Conference on Recommender Systems (pp. 107-114).

Cite this article

Wang,S.;Yao,X. (2023). The performance analysis of stock predication based on recurrent neural network. Applied and Computational Engineering,6,1268-1274.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. H.White. Economic prediction using neural networks: the case of IBM daily stock returns. Neural Networks, IEEE International Conference on. 1988.2(6). 451-458

[2]. Gen Cay. R.Non-linear prediction of security returns with moving average rules.Journal of Forecasting,1996,15(3).43-46

[3]. Rodriguez. F, Martel.C. On the profitability of technical trading rules based on artificial neural networks: Evidence form the madrid stock market. Economics Letters.2000.69(1).35-37

[4]. G. Peter Zhang. Time series forecasting using a hybrid ARIMA and neural network model|J]. Neurocomputing.2003(50). 159-175

[5]. A.Murat. Bahadir. Comparison of bayesian estimation and neural network model in stock market trading.Intelligent Engineering Systems through Artificial Neural Networks.2011.20. 74-81

[6]. A.Muratozbayoglu. Neural based techical analysis in stock market forecasting.Intelligent Engineering Systems through Artificial Neural Networks.2008(18). 261-265

[7]. Lapedes, A., & Farber, R. (1987). Nonlinear signal processing using neural networks: Prediction and system modelling (No. LA-UR-87-2662; CONF-8706130-4).

[8]. Fu, R., Zhang, Z., & Li, L. (2016, November). Using LSTM and GRU neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) (pp. 324-328). IEEE.

[9]. Dey, R., & Salem, F. M. (2017, August). Gate-variants of gated recurrent unit (GRU) neural networks. In 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS) (pp. 1597-1600). IEEE.

[10]. Bansal, T., Belanger, D., & McCallum, A. (2016, September). Ask the gru: Multi-task learning for deep text recommendations. In proceedings of the 10th ACM Conference on Recommender Systems (pp. 107-114).

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