Exploiting Long Short-term Memory Neural Network for Stock Price Prediction

Research Article
Open access

Exploiting Long Short-term Memory Neural Network for Stock Price Prediction

Zihui Chen 1*
  • 1 Ocean University of China    
  • *corresponding author 921018234@qq.com
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230277
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

Stock as a high yield, high risk investment has been favored by the public. In order to increase the return on investing in stocks, investors need to predict stock prices. In the past, investors used traditional mathematical methods to make predictions. Now, neural networks are used by investors to predict stocks, which can improve the accuracy of stock forecasting. To further verify the effectiveness of these methods, this work discusses the effects of different network structures and hyperparameters on stock prediction models using short-term memory (LSTM) neural networks. The results show that deeper network layer can get better training effect, but it needs more training time, resulting in a lot of time waste. In addition, this experiment tests the prediction effect under different dropout parameters. The results show that the dropout function should not be too large or too small. Multiple experiments are needed to find an appropriate dropout value.

Keywords:

stock price prediction, neural network, deep learning, LSTM

Chen,Z. (2023). Exploiting Long Short-term Memory Neural Network for Stock Price Prediction. Applied and Computational Engineering,8,829-834.
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References

[1]. Yuniningsih, Y., Widodo, S., & Wajdi, M. B. N. (2017). An analysis of decision making in the stock investment. Economic: Journal of Economic and Islamic Law, 8(2), 122-128.

[2]. Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The journal of Finance, 55(2), 773-806.

[3]. Nikou, M., Mansourfar, G., & Bagherzadeh, J. (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4), 164-174.

[4]. Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606.

[5]. Ghosh, A., Bose, S., Maji, G., Debnath, N., & Sen, S. (2019). Stock price prediction using LSTM on Indian share market. In Proceedings of 32nd international conference on, 63, 101-110.

[6]. Ding, G., & Qin, L. (2020). Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics, 11, 1307-1317.

[7]. Mehtab, S., & Sen, J. (2020). Stock price prediction using CNN and LSTM-based deep learning models. In 2020 International Conference on Decision Aid Sciences and Application (DASA), 447-453.

[8]. Feldman, J., Muthukrishnan, S., Sidiropoulos, A., Stein, C., & Svitkina, Z. (2010). On distributing symmetric streaming computations. ACM Transactions on Algorithms (TALG), 6(4), 1-19.

[9]. Hsu, H. C. (2010). Using MSN money to perform financial ratio analysis. Journal of College Teaching & Learning (TLC), 7(9), 25-36.

[10]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

Cite this article

Chen,Z. (2023). Exploiting Long Short-term Memory Neural Network for Stock Price Prediction. Applied and Computational Engineering,8,829-834.

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 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Yuniningsih, Y., Widodo, S., & Wajdi, M. B. N. (2017). An analysis of decision making in the stock investment. Economic: Journal of Economic and Islamic Law, 8(2), 122-128.

[2]. Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The journal of Finance, 55(2), 773-806.

[3]. Nikou, M., Mansourfar, G., & Bagherzadeh, J. (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4), 164-174.

[4]. Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606.

[5]. Ghosh, A., Bose, S., Maji, G., Debnath, N., & Sen, S. (2019). Stock price prediction using LSTM on Indian share market. In Proceedings of 32nd international conference on, 63, 101-110.

[6]. Ding, G., & Qin, L. (2020). Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics, 11, 1307-1317.

[7]. Mehtab, S., & Sen, J. (2020). Stock price prediction using CNN and LSTM-based deep learning models. In 2020 International Conference on Decision Aid Sciences and Application (DASA), 447-453.

[8]. Feldman, J., Muthukrishnan, S., Sidiropoulos, A., Stein, C., & Svitkina, Z. (2010). On distributing symmetric streaming computations. ACM Transactions on Algorithms (TALG), 6(4), 1-19.

[9]. Hsu, H. C. (2010). Using MSN money to perform financial ratio analysis. Journal of College Teaching & Learning (TLC), 7(9), 25-36.

[10]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

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