Forecast Analysis of the Stock Market Based on Hidden Markov Model and Long Short-Term Memory Model- Taking the S and P500 Index as an Example
DOI:
https://doi.org/10.61173/mdmp6998Keywords:
Stock Market, Hidden Markov Model, HMM and LSTM models, prediction, S&P500 IndexAbstract
This paper proposes a hybrid method that combines the advantages of HMM and LSTM models to improve the accuracy of stock market predictions, using the S&P500 index as a case study. HMMs are used to identify and model potential market states, while LSTMs predict future stock prices based on these states. Through this integration, this paper hopes to take advantage of HMM’s ability to model market conditions and LSTM’s advantages in forecasting to provide a more robust forecasting framework. This article is mainly divided into five parts. The first part is an introduction to the research background and objectives. The second part is the review and arrangement of relevant literature. The third part is the elaboration of the theoretical basis and the collection and analysis of data. The fourth part is an explanation of the data analysis results. The fifth part is the conclusion and suggestions of this study.
References
R., & Khatri, R. K. (2022). Predicting stock market index using LSTM. Machine Learning with Applications, 9, 100320.
Frino, A., & Gallagher, D. (2001). Tracking S&P 500 index funds. Journal of portfolio Management, 28(1). Gupta, A., & Dhingra, B. (2012, March). Stock market prediction using hidden markov models. In 2012 Students Conference on Engineering and Systems (pp. 1-4). IEEE. Hashish, I. A., Forni, F., Andreotti, G., Facchinetti, T., & Darjani, S. (2019, September). A hybrid model for bitcoin prices prediction using hidden Markov models and optimized LSTM networks. In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 721-728). IEEE. Hassan, M. R., & Nath, B. (2005, September). Stock market forecasting using hidden Markov model: a new approach. In 5th international conference on intelligent systems design and applications (ISDA’05) (pp. 192-196). IEEE.
Islam, M. R., & Nguyen, N. (2020). Comparison of financial models for stock price prediction. Journal of Risk and Financial Management, 13(8), 181.
Liu, M., Huo, J., Wu, Y., & Wu, J. (2021). Stock market trend analysis using hidden Markov model and long short term memory. arXiv preprint arXiv:2104.09700.
Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for stock price prediction. In E3S Web of Conferences (Vol. 218, p. 01026). EDP Sciences.
Rao, P. S., Srinivas, K., & Mohan, A. K. (2020). A survey on stock market prediction using machine learning techniques. In
ICDSMLA 2019: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications (pp. 923- 931). Springer Singapore. Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017, September). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics (icacci) (pp. 1643-1647). IEEE.
Soni, P., Tewari, Y., & Krishnan, D. (2022). Machine learning approaches in stock price prediction: a systematic review. In Journal of Physics: Conference Series (Vol. 2161, No. 1, p. 012065). IOP Publishing.
Su, Z., & Yi, B. (2022). Research on HMM‐Based Efficient Stock Price Prediction. Mobile Information Systems, 2022(1), 8124149.
Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606.
Wang, Q., Kang, K., Zhang, Z., & Cao, D. (2021). Application of LSTM and conv1d LSTM network in stock forecasting model. Artificial Intelligence Advances, 3(1), 36-43.
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