Advances in Economics, Management and Political Sciences

- The Open Access Proceedings Series for Conferences


Proceedings of the 7th International Conference on Economic Management and Green Development

Series Vol. 34 , 10 November 2023


Open Access | Article

A Literature Study of Asset Pricing Based on Machine Learning Method

Fengtian Zhao * 1
1 Faculty of engineering, architecture and information technology, University of Queens-land, Brisbane QLD 4072, Australia

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 34, 27-36
Published 10 November 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Fengtian Zhao. A Literature Study of Asset Pricing Based on Machine Learning Method. AEMPS (2023) Vol. 34: 27-36. DOI: 10.54254/2754-1169/34/20231669.

Abstract

By reviewing the relevant literature on machine learning in the field of asset pricing, this paper summarizes the application status, development trend, and existing problems of machine learning asset pricing methods, including commonly used algorithms, commonly used frameworks, and the advantages and disadvantages of different algorithms, comprehensively understanding the development status and trend of this field, and looking forward to the future possible research directions. In general, the machine learning asset pricing method has gradually shifted from manual feature extraction at the very beginning, relying on assumed models to build and solve model parameters, to end-to-end processing, increasing the diversity of data sources, especially with the development of deep reinforcement learning in recent years. This paper will focus on the methods and research progress of machine learning in the field of asset pricing and compares the applicability and limitations of the machine learning method according to the principle of a machine learning algorithm in different application scenarios.

Keywords

asset pricing, machine learning, applicability & limitations

References

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

2. Ross, S. L.: The arbitrage theory of capital asset pricing. Journal of Economic Theory 13(3), 341–360 (1976).

3. Gu, S., Kelly, B. T., & Xiu, D.: Empirical Asset Pricing via Machine Learning. Review of Financial Studies 33(5), 2223–2273 (2020).

4. Gu, S., Kelly, B. T., & Xiu, D.: Autoencoder asset pricing models. Journal of Econometrics 222(1), 429–450 (2021).

5. Weigand, A.: Machine learning in empirical asset pricing. Financial Markets and Portfolio Management 33(1), 93–104 (2019).

6. Giglio, S., Kelly, B. T., & Xiu, D.: Factor Models, Machine Learning, and Asset Pricing. Annual Review of Financial Economics 14(1), 337–368 (2022).

7. Lettau, M., & Pelger, M.: Factors that fit the time series and cross-section of stock returns. The Review of Financial Studies 33(5), 2274-2325 (2020).

8. Jiang, J., Kelly, B. T., & Xiu, D.: (Re-)Imag(in)ing Price Trends. Social Science Research Network (2020).

9. Bagnara, M.: Asset Pricing and Machine Learning: A critical review. Journal of Economic Surveys (2022).

10. Jaggi, M.: An Equivalence between the Lasso and Support Vector Machines. In Chapman and Hall/CRC eBooks, pp. 19–44 (2013).

11. Support Vector Machine(SVM): A Complete guide for beginners. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2021/10/support-vector-machinessvm-a-complete-guide-for-beginners/, last accessed 2023/4/20.

12. Bryzgalova, S., Pelger, M., & Zhu, J.: Forest through the trees: Building cross-sections of stock returns. SSRN (2020).

13. Coqueret, G., & Guida, T.: Machine Learning for Factor Investing : R version. In HAL (Le Centre pour la Communication Scientifique Directe). French National Centre for Scientific Research (2020).

14. Simonian, J., Wu, C., Itano, D., & Narayanam, V. R.: A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model. The Journal of Financial Data Science 1(1), 32–44 (2019).

15. Yao, J., Li, Y., & Tan, C. L.: Option price forecasting using neural networks. Omega 28(4), 455-466 (2000).

16. Feng, G., He, J., & Polson, N. G.: Deep learning for predicting asset returns. arXiv preprint (2018).

17. Kumaraswamy, B.: Neural networks for data classification. In Elsevier eBooks pp. 109–131 (2021).

18. Chen, L., Pelger, M., & Zhu, J.: Deep Learning in Asset Pricing. Social Science Research Network (2019).

19. Drobetz, W., & Otto, T.: Empirical asset pricing via machine learning: evidence from the European stock market. Journal of Asset Management 22(7), 507–538 (2021).

20. Huang, J., Chai, J., & Cho, S.: Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China 14(1), 1-24 (2020).

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 7th International Conference on Economic Management and Green Development
ISBN (Print)
978-1-83558-089-9
ISBN (Online)
978-1-83558-090-5
Published Date
10 November 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/34/20231669
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated