The combination of Multi-factor Models and Artificial Intelligence / Machines

Authors

  • Zihan Xuan

DOI:

https://doi.org/10.61173/4c2skc21

Keywords:

Multi-factor Models, Artificial Intelligence, Quantitative Finance, Risk Management, Portfolio

Abstract

With a focus on the rationality, risk, and return components of investment strategies, this study proposes to provide light on the growing importance of combining multifactor models and artificial intelligence (AI) in financial decisions intelligent decision making. The research aims to bring together traditional finance theories and modern data-driven approaches to enhance investment decision-making, risk management, and portfolio optimization. In this research I will mainly use this experimental scheme of data exploration as well as other experimental scheme such as using model training and testing to evaluate the possibility of AI-driven models to provide practical solutions and valuable knowledge for the financial industry.

References

Brown, 2019: Brown, J. (2019). The role of artificial intelligence in finance: A survey of recent developments and applications. Journal of Economic Surveys, 33(4), 881-908.

Carhart, M.M. (1997) ‘On persistence in mutual fund performance’, The Journal of finance, 52(1), pp.57-82.

Fama, E.F. and French, K.R. (2015) ‘A five-factor asset pricing model’, Journal of financial economics, 116(1), pp.1-22. Gao, J., Meng, B., Liang, T., Feng, Q., Ge, J., Yin, J., Wu, C.,

Cui, X., Hou, M., Liu, J. and Xie, H., (2019) Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China. ISPRS Journal of Photogrammetry and Remote Sensing, 147, pp.104-117.

Goodfellow, I., Bengio, Y. and Courville, A. (2016) Deep learning. MIT press.

Hu, B. and Wu, Y., (2023) Unlocking Causal Relationships in Commercial Banking Risk Management: An Examination of Explainable AI Integration with Multi-Factor Risk Models. Journal of Financial Risk Management, 12(3), pp.262-274.

Johnson, 2020: Johnson, R. (2020). Artificial intelligence in finance: How AI is transforming the way we invest. Wiley.

Jones, 2020: Jones, L. (2020). Machine learning in finance: From theory to practice. Routledge.

Kolari, J.W., Liu, W. and Pynnönen, S., (2024) The Future of Investment Practice, Artificial Intelligence, and Machine Learning. In Professional Investment Portfolio Management: Boosting Performance with Machine-Made Portfolios and Stock Market Evidence (pp. 237-247). Cham: Springer Nature Switzerland.

Loughran, T. and McDonald, B. (2011) ‘When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks’, The Journal of finance, 66(1), pp.35-65.

Markowitz, H. (1952) ‘The utility of wealth’, Journal of political Economy, 60(2), pp.151-158.

Nwogugu, M., (2005) Towards multi‐factor models of decision making and risk: A critique of Prospect Theory and related approaches, part II. The Journal of Risk Finance, 6(2), pp.163- 173.

Phiri, J., Zhao, T.J., Zhu, C.H. and Mbale, J., (2011) Using artificial intelligence techniques to implement a multifactor authentication system. International Journal of Computational Intelligence Systems, 4(4), pp.420-430.

Sharpe, W.F. (1964) ‘Journal of Finance’.

Smith, 2021: Smith, A. (2021). Multifactor models and artificial intelligence in finance: A review of the literature. Journal of International Financial Management, 12(3), 23-42.

Tang, X. and Huang, M., (2021) Inversion of chlorophyll-a concentration in Donghu Lake based on a machine learning algorithm. Water, 13(9), p.1179.

Zhang, C. and Tang, H., (2022), July. Empirical Research on Multifactor Quantitative Stock Selection Strategy Based on Machine Learning. In 2022 3rd International Conference on Pattern Recognition and Machine Learning (PRML) (pp. 380- 383). IEEE.

Downloads

Published

2024-04-16