Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 49 , 01 December 2023
Open
Access | Article
Forecasting Sector Rotation of A-share Market Using LSTM and Random Forest
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Abstract
To improve the efficacy of stock prediction strategies, researching sector rotation is essential. This study addresses the sector rotation problem in the A-share market and proposes an approach that leverages LSTM and random forest models to forecast sector rotation trends. Extensive evaluations are conducted to assess the models' prediction accuracy, comparing different evaluation indicators. The random search algorithm is employed to optimize model parameters, while the adaptive learning rate Adam algorithm is utilized to enhance convergence performance. The final experimental results demonstrate the remarkable accuracy of the LSTM model, achieving an impressive 88% accuracy in predicting sector rotation in the A-share market. Meanwhile, the random forest model achieves an accuracy of 86%. Furthermore, a combination of the bagging algorithm based on LSTM and random forest (LSTM-RF Bagging model) is employed for in-depth research, which exhibits even better performance with an accuracy of approximately 89%. The predictability of A-share market sector rotation is evident, and both LSTM and random forest models, along with new combination, prove to be suitable for forecasting. The findings in this paper serve as a valuable reference for investors, aiding them in making informed decisions regarding sector selection and asset allocation.
Keywords
sector rotation, A-share market, random forest, LSTM, forecasting
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-145-2
- ISBN (Online)
- 978-1-83558-146-9
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/49/20230493
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited