Machine learning-driven factor fitting model for stock data and its future trend prediction

DOI

10.2991/978-2-38476-257-6_45How to use a DOI?

Keywords

Factor analysis; Stock picking strategy; Machine learning

Abstract

Stock data factor model fitting and forecasting has been a hot topic in fintech and quantitative investment research. As a representative technology of artificial intelligence, machine learning can greatly improve the effect of forecasting research in economics and management. This paper aims to analyze stock data and predict fu-ture trends by using a variety of factor fitting models. First, we ex-tract multiple related factors from the data, such as volatility factor, growth factor, momentum factor, size factor, value factor, liquidity factor, profit factor, etc. Then, we use these factors to build fitting models, in this paper, eight machine learning algorithms, including linear model, Lasso regression, Ridge regression, decision tree model, random forest model, GBDT model and XGBT model, are used to build stock return prediction model and investment portfolio. Through these models, we can make pre-dictions about the stock data and come up with future trends. Final-ly, through empirical analysis, we verify that the forecast of these factor fitting models is better than that of CSI 300, and the annual-ized return rate and Sharpe ratio are both higher than that of CSI 300. Among them, the GDBT model has the best forecast results, with Sharpe ratio reaching 1.07 years and annualized return rate reaching 0.92, far higher than the 0.15 annualized return rate of CSI 300.

Copyright

© 2024 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.

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TY  - CONF
AU  - Binghui Wang
PY  - 2024
DA  - 2024/05/27
TI  - Machine learning-driven factor fitting model for stock data and its future trend prediction
BT  - Proceedings of the 2024 International Conference on Applied Economics, Management Science and Social Development (AEMSS 2024)
PB  - Atlantis Press
SP  - 382
EP  - 389
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-257-6_45
DO  - 10.2991/978-2-38476-257-6_45
ID  - Wang2024
ER  -