A Proposed Hybrid GA-TDDPL-CNN-LSTM Architecture for Stock Trend Prediction

Authors

  • Wei Chuan Loo School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia
  • Esraa Faisal Malik School of Management, Universiti Sains Malaysia, Penang, Malaysia
  • Xin Ying Chew School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia
  • Khai Wah Khaw School of Management, Universiti Sains Malaysia, Penang, Malaysia
  • Sajal Saha Department of Mathematics, IUBAT‒International University of Business Agriculture and Technology, Dhaka, Bangladesh
  • Ming Ha Lee Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350 Kuching, Sarawak, Malaysia
  • Mariam Al Akasheh Department of Analytics in the Digital Era, College of Business and Economics, United Arab Emirates University, Al Ain 15551, United Arab Emirates

Keywords:

CNN, expert rule, genetic algorithm, LSTM, RBM, stock market

Abstract

This study constructs a hybrid deep learning model to predict the price trend movement of Standard & Poor’s 500 index. Predicting stock market price trends is challenging because stock market data are non-linear and complex. Additionally, various factors, such as investor sentiment and news events, exert influence on stock price trends, leading to fluctuations in price trends. Researchers have implemented a variety of machine learning methods to predict stock price movements. The present study develops a hybrid deep learning network model consisting of a feature learning model, which is a long short-term memory model, and a feature selection model. Different types of data, including stock price, smoothing indicators, trend indicators, and oscillator indicators, are used as inputs to improve the model’s performance. Furthermore, to optimize the hyperparameter of each feature extraction model and feature selection model, a Genetic algorithm is utilized. An expert rule trend deterministic layer is also implemented to pre-process the data to further improve the model’s performance. The results indicate that the proposed model has superior testing performance compared to restricted Boltzmann machine, convolutional neural network, and autoencoder models. 

Downloads

Download data is not yet available.

References

C. J. Neely, D. Rapach, J. Tu, and G. Zhou, “Forecasting the Equity Risk Premium: The Role of Technical Indicators,” SSRN Electron. J., Jan. 2013, doi: 10.2139/SSRN.1787554.

H. Gunduz, Y. Yaslan, and Z. Cataltepe, “Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations,” Knowledge-Based Syst., vol. 137, pp. 138–148, Dec. 2017, doi: 10.1016/J.KNOSYS.2017.09.023.

J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques,” Expert Syst. Appl., vol. 42, no. 1, pp. 259–268, Ja[21n. 2015, doi: 10.1016/J.ESWA.2014.07.040.

M. Hiransha, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “NSE Stock Market Prediction Using Deep-Learning Models,” Procedia Comput. Sci., vol. 132, pp. 1351–1362, Jan. 2018, doi: 10.1016/J.PROCS.2018.05.050.

Q. Liang, W. Rong, J. Zhang, J. Liu, and Z. Xiong, “Restricted Boltzmann machine based stock market trend prediction,” Proc. Int. Jt. Conf. Neural Networks, vol. 2017-May, pp. 1380–1387, Jun. 2017, doi: 10.1109/IJCNN.2017.7966014.

W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A CNN-LSTM-based model to forecast stock prices,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/6622927.

Y. Hao and Q. Gao, “Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning,” Appl. Sci. 2020, Vol. 10, Page 3961, vol. 10, no. 11, p. 3961, Jun. 2020, doi: 10.3390/APP10113961.

Y. Huang, “Predicting home value in California, United States via machine learning modeling,” Stat. Optim. Inf. Comput., vol. 7, no. 1, pp. 66–74, 2019, doi: 10.19139/soic.v7i1.435.

S. Pourmand, A. Shabbak, and M. Ganjali, “Feature Selection Based on Divergence Functions: A Comparative Classification Study,” Stat. Optim. Inf. Comput., vol. 9, no. 3, pp. 587–606, 2021, doi: 10.19139/soic-2310-5070-1092.

M. Kheirkhahzadeh and M. Analoui, “Community detection in social networks using consensus clustering,” Stat. Optim. Inf. Comput., vol. 7, no. 4, pp. 864–884, 2019, doi: 10.19139/soic-2310-5070-801.

T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” Eur. J. Oper. Res., 2017, Accessed: Oct. 14, 2021. [Online]. Available: https://ideas.repec.org/p/zbw/iwqwdp/112017.html.

P. Zhong and Z. Gong, “A hybrid DBN and CRF model for spectral-spatial classification of hyperspectral images,” Stat. Optim. Inf. Comput., vol. 5, no. 2, pp. 75–98, 2017, doi: 10.19139/soic.v5i2.309.

F. Abdullayeva and Y. Imamverdiyev, “Development of oil production forecasting method based on deep learning,” Stat. Optim. Inf. Comput., vol. 7, no. 4, pp. 826–839, 2019, doi: 10.19139/soic-2310-5070-651.

M. Faraz, H. Khaloozadeh, and M. Abbasi, “Stock Market Prediction-by-Prediction Based on Autoencoder Long Short-Term Memory Networks,” 2020 28th Iran. Conf. Electr. Eng. ICEE 2020, Aug. 2020, doi: 10.1109/ICEE50131.2020.9261055.

W. Bao, J. Yue, and Y. Rao, “A deep learning framework for financial time series using stacked autoencoders and long-short term memory,” PLoS One, vol. 12, no. 7, p. e0180944, Jul. 2017, doi: 10.1371/JOURNAL.PONE.0180944.

S. Lv, Y. Hou, and H. Zhou, “Financial Market Directional Forecasting With Stacked Denoising Autoencoder,” Dec. 2019, Accessed: Oct. 14, 2021. [Online]. Available: https://arxiv.org/abs/1912.00712v1.

W. Chen, M. Jiang, W. G. Zhang, and Z. Chen, “A novel graph convolutional feature based convolutional neural network for stock trend prediction,” Inf. Sci. (Ny)., vol. 556, pp. 67–94, May 2021, doi: 10.1016/J.INS.2020.12.068.

J. Long, Z. Chen, W. He, T. Wu, and J. Ren, “An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market,” Appl. Soft Comput. J., vol. 91, Jun. 2020, doi: 10.1016/J.ASOC.2020.106205.

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/NECO.1997.9.8.1735.

S. JIN, L. SU, and A. ULLAH, “Robustify Financial Time Series Forecasting with Bagging,” Econom. Rev., vol. 33, no. 5–6, p. 575, Aug. 2014, doi: 10.1080/07474938.2013.825142.

H. M. Pandey, “Performance Evaluation of Selection Methods of Genetic Algorithm and Network Security Concerns,” Procedia Comput. Sci., vol. 78, pp. 13–18, Jan. 2016, doi: 10.1016/J.PROCS.2016.02.004

Dhabliya, D., & Dhabliya, R. (2019). Key characteristics and components of cloud computing. International Journal of Control and Automation,12(6 Special Issue), 12-18. Retrieved from www.scopus.com

Pekka Koskinen, Pieter van der Meer, Michael Steiner, Thomas Keller, Marco Bianchi. Automated Feedback Systems for Programming Assignments using Machine Learning. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/190

Carlos Silva, David Cohen, Takashi Yamamoto, Maria Petrova, Ana Costa. Ethical Considerations in Machine Learning Applications for Education. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/192

Downloads

Published

16.07.2023

How to Cite

Loo, W. C. ., Malik, E. F. ., Chew, X. Y. ., Khaw, K. W. ., Saha, S. ., Lee, M. H. ., & Akasheh, M. A. . (2023). A Proposed Hybrid GA-TDDPL-CNN-LSTM Architecture for Stock Trend Prediction. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 653–664. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3271

Issue

Section

Research Article