IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2507.00332.html
   My bibliography  Save this paper

Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control

Author

Listed:
  • Ruisi Li
  • Xinhui Gu

Abstract

Propose a deep learning driven multi factor investment model optimization method for risk control. By constructing a deep learning model based on Long Short Term Memory (LSTM) and combining it with a multi factor investment model, we optimize factor selection and weight determination to enhance the model's adaptability and robustness to market changes. Empirical analysis shows that the LSTM model is significantly superior to the benchmark model in risk control indicators such as maximum retracement, Sharp ratio and value at risk (VaR), and shows strong adaptability and robustness in different market environments. Furthermore, the model is applied to the actual portfolio to optimize the asset allocation, which significantly improves the performance of the portfolio, provides investors with more scientific and accurate investment decision-making basis, and effectively balances the benefits and risks.

Suggested Citation

  • Ruisi Li & Xinhui Gu, 2025. "Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control," Papers 2507.00332, arXiv.org.
    • Handle: RePEc:arx:papers:2507.00332
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2507.00332
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nhi N.Y.Vo & Xue-Zhong He & Shaowu Liu & Guandong Xu, 2019. "Deep Learning for Decision Making and the Optimization of Socially Responsible Investments and Portfolio," Published Paper Series 2019-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    3. Philipp J. Kremer & Andreea Talmaciu & Sandra Paterlini, 2018. "Risk minimization in multi-factor portfolios: What is the best strategy?," Annals of Operations Research, Springer, vol. 266(1), pages 255-291, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hongcheng Ding & Xuanze Zhao & Ruiting Deng & Shamsul Nahar Abdullah & Deshinta Arrova Dewi, 2024. "EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods," Papers 2408.13214, arXiv.org, revised Jun 2025.
    2. Bouyaddou, Youssef & Jebabli, Ikram, 2025. "Integration of investor behavioral perspective and climate change in reinforcement learning for portfolio optimization," Research in International Business and Finance, Elsevier, vol. 73(PB).
    3. Pedro Reis & Ana Paula Serra & Jo~ao Gama, 2025. "The Role of Deep Learning in Financial Asset Management: A Systematic Review," Papers 2503.01591, arXiv.org.
    4. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," FrenXiv e75gc_v1, Center for Open Science.
    5. repec:osf:thesis:auyvc_v1 is not listed on IDEAS
    6. Jian Guo & Saizhuo Wang & Lionel M. Ni & Heung-Yeung Shum, 2022. "Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence," Papers 2301.04020, arXiv.org.
    7. Fereydooni, Ali & Barak, Sasan & Asaad Sajadi, Seyed Mehrzad, 2024. "A novel online portfolio selection approach based on pattern matching and ESG factors," Omega, Elsevier, vol. 123(C).
    8. Massimo Guidolin & Manuela Pedio & Dimos Andronoudis, 2019. "How Smart is the Real Estate Smart Beta? Evidence from Optimal Style Factor Strategies for REITs," BAFFI CAREFIN Working Papers 19117, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    9. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    10. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
    11. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    12. repec:osf:metaar:haf2v_v1 is not listed on IDEAS
    13. Xuecheng Tian & Yanxia Guan & Shuaian Wang, 2023. "Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty," Mathematics, MDPI, vol. 11(17), pages 1-12, September.
    14. Sadefo Kamdem, Jules & Bandolo Essomba, Rose & Njong Berinyuy, James, 2020. "Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    15. Schlütter, Sebastian & Fianu, Emmanuel Senyo & Gründl, Helmut, 2022. "Responsible investments in life insurers' optimal portfolios under solvency constraints," ICIR Working Paper Series 45/22, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    16. Bonaccolto, Giovanni & Caporin, Massimiliano & Maillet, Bertrand B., 2022. "Dynamic large financial networks via conditional expected shortfalls," European Journal of Operational Research, Elsevier, vol. 298(1), pages 322-336.
    17. Taeisha Nundlall & Terence L Van Zyl, 2023. "Machine Learning for Socially Responsible Portfolio Optimisation," Papers 2305.12364, arXiv.org.
    18. Davoodi, Shayan & Fereydooni, Ali & Rastegar, Mohammad Ali, 2024. "Can portfolio construction considering ESG still gain high profits?," Research in International Business and Finance, Elsevier, vol. 67(PA).
    19. Jiawei Wang & Zhen Chen, 2024. "Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-26, June.
    20. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawRxiv kczj5, Center for Open Science.
    21. Shima Nabiee & Nader Bagherzadeh, 2023. "Stock Trend Prediction: A Semantic Segmentation Approach," Papers 2303.09323, arXiv.org.
    22. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2507.00332. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.