Research Article
A Study of Investment Strategies Based on Artificial Intelligence and Machine Learning Forecasting
@INPROCEEDINGS{10.4108/eai.2-6-2023.2334655, author={Cheng Peng and Yiming Fang and Yinzhe Chang}, title={A Study of Investment Strategies Based on Artificial Intelligence and Machine Learning Forecasting}, proceedings={Proceedings of the 2nd International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2023, June 2--4, 2023, Nanchang, China}, publisher={EAI}, proceedings_a={ICIDC}, year={2023}, month={8}, keywords={machine learning artificial intelligence deep neural networks decision tree models}, doi={10.4108/eai.2-6-2023.2334655} }- Cheng Peng
Yiming Fang
Yinzhe Chang
Year: 2023
A Study of Investment Strategies Based on Artificial Intelligence and Machine Learning Forecasting
ICIDC
EAI
DOI: 10.4108/eai.2-6-2023.2334655
Abstract
Artificial intelligence has developed rapidly since the 21st century and its integration with various fields has contributed to the rapid development of each field. This paper focuses on the quantitative application of artificial intelligence in China's financial market, by introducing 20 indicators covering value, technical, momentum and sentiment reversal and 8 machine learning algorithms to forecast stock returns in Shanghai and Shenzhen markets. In terms of the degree of contribution of each indicator to the model, this paper finds that momentum, reversal and technical indicators have the highest degree of influence on the future stock returns. The paper then ranks these stocks according to their predicted returns and develops a trading strategy. Comparing the results of each model, it is found that the trading strategies formed by the predicted returns can achieve significant excess returns in the Chinese market, with deep neural networks being the best predictors and regularised linear machine learning models the second best. Deep machine learning is used to explore the impact of each factor indicator on the Chinese stock market, providing some implications for policy makers, as well as a better understanding of the irrational factors in Chinese market trading.
