Changfeng Xiao
ABSTRACT
The popularity of information technology has led to an explosive growth in the amount of information in financial markets. As a result, quantitative trading, which combines computer technology, has gained favor among domestic and foreign investors. Nowadays, there are various quantitative trading strategies available to address various investment challenges. Among them, Long Short-Term Memory (LSTM) networks perform exceptionally well in many problems and are now widely used. This paper proposes a quantitative trading model based on the LSTM network, utilizing Python for model learning and prediction. By adjusting the internal parameters of the model, the accuracy of stock prediction can be improved. Historical data of any stock can be used for learning and predicting future stock trends. This provides a new strategy tool for market investment.
- Shi, L. 2023. Quantitative Trading Strategy Design Based on XGBoost Stock Selection Model. DOI: 10.26916/d.cnki.gahcc.2023.000333.Google Scholar
Cross Ref
- Bai, Y., Zhang, D. 2023. Research on Stamp Tax Reform in High-Frequency Quantitative Trading. Tax Research, 2023(01):92-99. DOI: 10.19376/j.cnki.cn11-1011/f.2023.01.022.Google ScholarCross Ref
- Guo, X. 2019. Research on Quantitative Investment Trading Strategies. Financial World, (03):16-17. DOI: 10.16266/j.cnki.cn11-4098/f.2019.02.012.Google ScholarCross Ref
- Wang, J., Zhuang, Z., Gao, D., Li, Y. & Feng, L. 2023. Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression. Studies in Nonlinear Dynamics & Econometrics, 27(3), 397-421. https://doi.org/10.1515/snde-2021-0096.Google ScholarCross Ref
- Lthelaya K A, El-alfy E M, Mohammed S. Evaluation of bidirectional LSTM for short-and long-term stock market prediction[C]//2018 9th International Conference on Information and Communication Systems (ICICS). 2018, 151-6.Google Scholar
- Qian F, Chen X. Stock Prediction Based on LSTM under Different Stability[C]//2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). 2019, 483-6.Google Scholar
- Li B, Li L-F. Stock Prediction Based on Adaptive Gradient Descent Deep Learning[M]. Springer International Publishing. 2021, 51-62.Google ScholarCross Ref
- Nguyen T T, Yoon S. A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning [J]. Applied Sciences, 2019, 9(22): 4745.Google ScholarCross Ref
- Wang, S. 2023. Research on Quantitative Trading Strategy of Stock Trends Based on Value Discovery. University of Electronic Science and Technology of China, 2023. DOI: 10.27005/d.cnki.gdzku.2022.000971.Google ScholarCross Ref
- Cheng, M., Gao, (2022) S. Multi-scale Stock Prediction Based on Deep Transfer Learning. Computer Engineering and Applications, 2022, 58(12): 249-259.Google Scholar
- Deng, F., Cen, S., Zhong, F., (2021) Short-term price trend prediction based on LSTM neural network. Computer Systems and Applications, 2021, 30(04): 187-192. DOI: 10.15888/j.cnki.csa.007855.Google ScholarCross Ref
- Zhang, Q., Yan, D., Han, J. (2021) Research on Stock Price Prediction Combined with Deep Learning and Decomposition Algorithm. Computer Engineering and Applications, 2021, 57(05): 56-64.Google Scholar
- Ding, W. (2021) Comparison of ARIMA Model and LSTM Model Based on Stock Forecast. Industrial Control Computer, 2021, 34(07): 109-112 + 116.Google Scholar
- Goodfellow I J, Pouget-Abadie J, Mirza M, (2014) Generative Adversarial Networks. Advances in Neural Information Processing Systems, 2014, 3:2672-2680.Google Scholar
Index Terms
- Quantitative trading prediction model based on Long Short-Term Memory
Recommendations
Using Long Short-Term Memory to Predict Cash Dividend
ICEBT '21: Proceedings of the 2021 5th International Conference on E-Education, E-Business and E-TechnologyInvesting in stocks has been very popular in recent years. Investors hope to make a profit by investing in stocks. However, stock prices are highly volatile. Many investors judge whether to invest in stocks based on historical stock prices, technical ...
Computer Intelligent Value Evaluation Model through ARMA and Long Short-Term Memory Neural Network
AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureWith the rapid development of the economy, stocks have become a widely accepted investment option, and professional analysts need to get the latest information to develop investment strategies. According to the efficient market assumption, the incentive ...
Short-Term Price Overreactions: Identification, Testing, Exploitation
This paper examines short-term price reactions after one-day abnormal price changes and whether they create exploitable profit opportunities in various financial markets. Statistical tests confirm the presence of overreactions and also suggest that ...
Comments