Research on Stock Market Investment Model Based on Time Series Forecasting and Dynamic Programming

DOI

10.2991/978-94-6463-270-5_46How to use a DOI?

Keywords

time series forecasting; dynamic programming; stock prediction; data mining

Abstract

Forecasting the value of a stock population has always been attractive and challenging for shareholders due to its inherent dynamics, nonlinearity and complexity. In this paper, we propose a stock investment model based on time series forecasting and dynamic programming. The time series forecasting model is utilized for next day high- and low-price prediction, combined with the dynamic programming model to formulate stock trading strategies. The study conducted simulated back tests on 200 stocks randomly selected from the Chinese Shanghai and Shenzhen stock markets, and the results show that the scheme proposed in this study can achieve a return of more than 12% after more than a 3-month investment cycle.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - XuDong An
AU  - Xuan Yi
AU  - BeiQi Zhou
AU  - QinJuan Zhang
PY  - 2023
DA  - 2023/10/29
TI  - Research on Stock Market Investment Model Based on Time Series Forecasting and Dynamic Programming
BT  - Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023)
PB  - Atlantis Press
SP  - 412
EP  - 420
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-270-5_46
DO  - 10.2991/978-94-6463-270-5_46
ID  - An2023
ER  -