Research on stock trend prediction methods - Taking CSI 300 and CSI 500 as examples
DOI:
https://doi.org/10.54097/hbem.v4i.3535Keywords:
ARIMA, ARCH Stock market, trend predictionAbstract
As an important part of the capital market, the stock market can not only reflect the quality of the macro economy in time, but also reflect the policy changes in the change of the stock price.In addition, the stock market not only provides a public financing platform with strong liquidity for enterprises, but also plays a crucial role in the redistribution of social resources.This paper takes China's CSI 300 index CSI 500 index from 2007 to 2022 as the research object, and uses ARIMA, ARCH family and other models to implement it, which provides a better solution for the prediction of stock trends.
Downloads
References
Jiang, Z., Xu, D., & Liang, J. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. ArXiv, Abs / 1706.10059.
Wang, J., Zhang, Y., Tang, K., Wu, J., & Xiong, Z. (2019). AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
Wang, Z., Huang, B., Tu, S., Zhang, K., & Xu, L. (2021). DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. AAAI.
Cong, L.W., Tang, K., Wang, J., & Zhang, Y. (2020). AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI. Capital Markets: Asset Pricing & Valuation eJournal.
Liu, X., Yang, H., Chen, Q., Zhang, R., Yang, L., Xiao, B., & Wang, C. (2020). FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. Capital Markets: Market Microstructure eJournal.
Yang, H., Liu, X., Zhong, S., & Walid, A. (2020). Deep reinforcement learning for automated stock trading: an ensemble strategy. Proceedings of the First ACM International Conference on AI in Finance.
Yang, M., Zheng, X., Liang, Q., Han, B., & Zhu, M. (2022). A Smart Trader for Portfolio Management based on Normalizing Flows. IJCAI.
Wang, H., Wang, T., Li, S., Zheng, J., Guan, S., & Chen, W. (2022). Adaptive Long-Short Pattern Transformer for Stock Investment Selection. IJCAI.
Mguni, D.H., Sootla, A., Ziomek, J., Slumbers, O., Dai, Z., Shao, K., & Wang, J. (2022). Timing is Everything: Learning to Act Selectively with Costly Actions and, between, regional Constraints. ArXiv, ABS /2205.15953.
Pretorius, R., & van Zyl, T.L. (2022). Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management. ArXiv, Abs / 2203.11318.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
