Author(s)

He Kexin ¹, Zhang Zhijin ¹

Affiliation(s)

¹ The School of Business, Wenzhou-kean University, Li'ao Street, Wenzhou, China

Corresponding Author

ABSTRACT

To compare the ability of efficiently prediction stock price based on CNN and LSTM method, we take the data from Tata Consultancy Services as our object to study. We run 35 times for their training and test loss and errors. Afterwards we made a comparison on the data, and getting the result that in contract to LSTM method, CNN method has a better ability on prediction accuracy in short time.

KEYWORDS

CITE THIS PAPER

He Kexin, Zhang Zhijin, Prediction stock price based on CNN and LSTM models. Financial Engineering and Risk Management (2022) Vol. 5: 14-21. DOI: http://dx.doi.org/10.23977/ferm.2022.050703.

REFERENCES

[1] Huang, W., Nakamori, Y., & Wang, S.-Y. (2004). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513–2522.
[2] Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941. 
[3] Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-Based Model to Forecast Stock Prices. Complexity, 2020, 1–10. 
[4] Sismanoglu, G., Onde, M. A., Kocer, F., & Sahingoz, O. K. (2019). Deep Learning Based Forecasting in Stock Market with Big Data Analytics. 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT).
[5] Vui, C. S., Soon, G. K., On, C. K., Alfred, R., & Anthony, P. (2013). A review of stock market prediction with Artificial neural network (ANN). 2013 IEEE International Conference on Control System, Computing and Engineering. 
[6] Nelson, D. M. Q., Pereira, A. C. M., & de Oliveira, R. A. (2017). Stock market's price movement prediction with LSTM neural networks. 2017 International Joint Conference on Neural Networks (IJCNN).
[7] Y. Sun, Y. Liang, and W. Zhang, "Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting," Neural Computing and Applications, vol. 14, pp. 1441–1449, 2005.
[8] Y. Hu, "Stock market timing model based on convolutional neural network – a case study of Shanghai composite index," Finance& Economy, vol. 4, pp. 71–74, 2018.
[9] Rasheed, J., Jamil, A., Hameed, A. A., Ilyas, M., Özyavaş, A., & Ajlouni, N. (2020, October). Improving stock prediction accuracy using cnn and lstm. In 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) (pp. 1-5). IEEE.
[10] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
[11] B. S. Kim and T. G. Kim, "Cooperation of simulation and data model for performance analysis of complex systems," International Journal of Simulation Modelling, vol. 18, no. 4, pp. 608–619, 2019.
[12] L. Qin, N. Yu, and D. Zhao, "Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video," Tehnicki Vjesnik-Technical Gazette, vol. 25, no. 2, pp. 528–535, 2018.

Sponsors, Associates, and Links

• Information Systems and Economics
  
  
• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Tourism Management and Technology Economy
  
  
• Journal of Computational and Financial Econometrics
  
  
• Accounting and Corporate Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Agricultural & Forestry Economics and Management
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics