9卷 第6期
Faculty of Science, Kunming University of Science and Technology, Kunming, Yunnan. 650031, China
纸质出版日期：2025-06-30
Scan QR Code
(2025). A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction. 发展经济学, 9(6), 37-43.
Fu Yinfei.A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction[J].Development Economics of China,2025,09(06):37-43.
(2025). A Review of Machine Learning-Driven Multifactor Modeling in Stock Prediction. 发展经济学, 9(6), 37-43. DOI： 10.47297/wspdecWSP2515-797307.20250906.
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.
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.
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 .
Related Articles
Related Author
Related Institution
京公网安备11010802024621