A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction

Yinfei Fu

doi:10.47297/wspdecWSP2515-797307.20250906

PDF
Export
Share
Collection
Album

A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction

Volume 9, Issue 6

Views: 0 Downloads: 0

A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction
A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction
Faculty of Science, Kunming University of Science and Technology, Kunming, Yunnan. 650031, China
Vol. 9, Issue 6, Pages: 37-43(2025)
Published：30 June 2025
DOI：10.47297/wspdecWSP2515-797307.20250906
Accepted：

Scan QR Code

Fu Yinfei.A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction[J].Development Economics of China,2025,09(06):37-43.
DOI：

Fu Yinfei.A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction[J].Development Economics of China,2025,09(06):37-43. DOI： 10.47297/wspdecWSP2515-797307.20250906.

摘要

Abstract

With the rapid development of financial markets and the continuous progress of data acquisition technology

machine learning algorithms have been increasingly applied in the field of stock return prediction. This paper systematically reviews the research progress of machine learning-driven multifactor models in stock forecasting in recent years

focusing on analyzing the research results of different machine learning algorithms (e.g.

linear regression

neural networks

random forest

deep learning

etc.) combined with multifactor models. By combing and classifying the existing literature

this paper summarizes the differences in the performance of each method in terms of prediction accuracy

model complexity

interpretability

etc.

and explores what feature engineering is applicable under different models. Finally

this paper points out the deficiencies in current research

such as the lack of cross-market generalization capability and the high-frequency and low-frequency data fusion challenges

and proposes potential directions for future research

including the combination of real-time decision-making and reinforcement learning

interpretability and regulatory compliance. The research results in this paper can provide references and lessons for academics and financial practice areas.

关键词

Keywords

references

Sharpe W . F . ( 1964 ). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance , * 19 *( 3 ), 425 - 442 .

Ross S . A . ( 1976 ). The arbitrage theory of capital asset pricing. Journal of Economic Theory , * 13 *( 3 ), 341 - 360 .

Fama E. F. , & French K . R . ( 1993 ). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics , * 33 *( 1 ), 3 - 56 .

Carhart M . M . ( 1997 ). On persistence in mutual fund performance. Journal of Finance , * 52 *( 1 ), 57 - 82 .

Fama E. F. , & French K . R . ( 2015 ). A five-factor asset pricing model. Journal of Financial Economics , * 116 *( 1 ), 1 - 22 .

Li Z. , Liu H. , & Dong X . ( 2017 ). Zhongguo A gu shichang de wu yinzi moxing yanjiu [A five-factor model in China's A-share market] . Journal of Financial Research , * 12 *, 191 - 206 .

Xu C . C . ( 2020 ). Gupiao jiage yuce fangfa zongshu [A review of stock price prediction methods ] . Zhongguo Shichang [China Market] , ( 9 ), 42 - 43+68 .

Vapnik V . N . ( 1998 ). Statistical learning theory. Wiley.(For SRM principle specifically, cite this book as it contains the definitive formulation)

Breiman L . ( 2001 ). Random forests . Machine Learning , 45 ( 1 ), 5 - 32 .

Krauss C. , Do X. A. , & Huck N . ( 2017 ). Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S&P 500 . European Journal of Operational Research . 259 ( 2 ), 689 - 702 .

Amin M . ( 2000 , June ). Stock market prediction via support vector machines: A NASDAQ case study. Paper presented at the IEEE/IAFE/INFORMS Conference on Computational Paper presented at the IEEE/IAFE/INFORMS Conference on Computational Intelligence for Financial Engineering (CIFEr), New York, NY, USA .

Patel J. , Shah S. , Thakkar P. , & Kotecha K . ( 2015 ). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques . expert Systems with Applications , 42 ( 1 ), 259 - 268 .

Li B. , Wang X. , & Chen Y . ( 2019 ). Jiqixuexi qudong de jibenmian lianghua touzi yanjiu [Machine learning-driven fundamental quantitative investment research]. Zhongguo Gongye Jingji [China Industrial Economics], *2019 * ( 8 ), 45 - 62 .

Cao C . -F., Luo Z . -N. , Xie J.-X., & Li L. ( 2022 ). MDT-CNN-LSTM moxing de gujia yuce yanjiu [Stock price prediction using the MDT-CNN-LSTM model ] . Jisuanji Gongcheng yu Yingyong [Computer Engineering and Applications ] , * 58 *( 5 ), 280 - 286 .

Bao W. , Yue J. , & Rao Y . ( 2017 ). A deep learning framework for financial time series using stacked autoencoders and long-short term memory . PLOS ONE , *12 *( 7 ), e0180944 .

Gyamerah S. A. , Ngare P. , & Ikpe D . ( 2021 ). Ensemble machine learning for stock market prediction: a case study of the Nairobi Securities Exchange . expert Systems with Applications , *185 *,. 115591 .

Chen L. , & Wei Y . ( 2020 ). Heterogeneous ensemble learning for stock selection in China's A-share market . Expert Systems with Applications , * 158 *, 113516 .

Kim S. , Ku S. , Chang W. , & Song J . W . ( 2020 ). Predicting the direction of US stock prices using effective transfer entropy and machine learning techniques. IEEE Access , 8 , 111660 - 111682 .

Song L . ( 2022 ). Integrated learning model and system implementation for multi-factor mining of stocks [Master's thesis, University of Electronic Science and Technology of China] . China National Knowledge Infrastructure .

Xiang X . ( 2022 ). Research on multi-factor quantitative stock selection strategy based on machine learning [Master's thesis, Zhongnan University of Economics and Law] . .

Li R. , & Ye Z . ( 2023 ). Machine learning-based prediction of fund returns . Statistics & Decision , *39 *( 11 ), 156 - 161 .

Alert me when the article has been cited

Submit

No data

Related Author

No data

Related Institution

No data

⁰