IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v12y2024i9p148-d1478550.html
   My bibliography  Save this article

Automated Machine Learning and Asset Pricing

Author

Listed:
  • Jerome V. Healy

    (Liverpool Business School, Liverpool John Moores University, Liverpool L3 5UG, UK)

  • Andros Gregoriou

    (Liverpool Business School, Liverpool John Moores University, Liverpool L3 5UG, UK)

  • Robert Hudson

    (Business School, University of Hull, Hull HU6 7RX, UK)

Abstract

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can identify features of the data-generating process undetected by standard methods and rank the best-performing algorithms. Our tests use 95 years of CRSP data, from 1926 to 2021, encompassing the price history of the broad US stock market. Our findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors may be more attractive when more appropriate account is taken of the non-linear properties of the underlying assets.

Suggested Citation

  • Jerome V. Healy & Andros Gregoriou & Robert Hudson, 2024. "Automated Machine Learning and Asset Pricing," Risks, MDPI, vol. 12(9), pages 1-12, September.
  • Handle: RePEc:gam:jrisks:v:12:y:2024:i:9:p:148-:d:1478550
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/12/9/148/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/12/9/148/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lintner, John, 1969. "The Aggregation of Investor's Diverse Judgments and Preferences in Purely Competitive Security Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 4(4), pages 347-400, December.
    2. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    3. Kolm, Petter N. & Tütüncü, Reha & Fabozzi, Frank J., 2014. "60 Years of portfolio optimization: Practical challenges and current trends," European Journal of Operational Research, Elsevier, vol. 234(2), pages 356-371.
    4. Black, Fischer, 1972. "Capital Market Equilibrium with Restricted Borrowing," The Journal of Business, University of Chicago Press, vol. 45(3), pages 444-455, 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. Sharpe, William F, 1991. "Capital Asset Prices with and without Negative Holdings," Journal of Finance, American Finance Association, vol. 46(2), pages 489-509, June.
    2. Nitzan Weiss, 1984. "Reply to a Paradigmatic Comment: Capital Markets, Output, and the Demand for Inputs under Uncertainty," Eastern Economic Journal, Eastern Economic Association, vol. 10(1), pages 79-85, Jan-Mar.
    3. Michele Costola & Bertrand Maillet & Zhining Yuan & Xiang Zhang, 2024. "Mean–variance efficient large portfolios: a simple machine learning heuristic technique based on the two-fund separation theorem," Annals of Operations Research, Springer, vol. 334(1), pages 133-155, March.
    4. Penaranda, Francisco, 2007. "Portfolio choice beyond the traditional approach," LSE Research Online Documents on Economics 24481, London School of Economics and Political Science, LSE Library.
    5. Thorsten Hens & Fatemeh Naebi, 2022. "Behavioral heterogeneity in the CAPM with evolutionary dynamics," Journal of Evolutionary Economics, Springer, vol. 32(5), pages 1499-1521, November.
    6. Chauveau, Thierry & Gatfaoui, Hayette, 2002. "Systematic risk and idiosyncratic risk: a useful distinction for valuing European options," Journal of Multinational Financial Management, Elsevier, vol. 12(4-5), pages 305-321.
    7. Erol Muzir & Cevdet Kizil & Burak Ceylan, 2021. "Role of International Trade Competitive Advantage and Corporate Governance Quality in Predicting Equity Returns: Static and Conditional Model Proposals for an Emerging Market," JRFM, MDPI, vol. 14(3), pages 1-31, March.
    8. Hayette Gatfaoui, 2010. "Capital Asset Pricing Model," Post-Print hal-00589904, HAL.
    9. Rostagno, Luciano Martin, 2005. "Empirical tests of parametric and non-parametric Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) measures for the Brazilian stock market index," ISU General Staff Papers 2005010108000021878, Iowa State University, Department of Economics.
    10. Turan G. Bali & Robert F. Engle & Yi Tang, 2017. "Dynamic Conditional Beta Is Alive and Well in the Cross Section of Daily Stock Returns," Management Science, INFORMS, vol. 63(11), pages 3760-3779, November.
    11. Zhong, Angel, 2018. "Idiosyncratic volatility in the Australian equity market," Pacific-Basin Finance Journal, Elsevier, vol. 50(C), pages 105-125.
    12. repec:dau:papers:123456789/2514 is not listed on IDEAS
    13. Zura Kakushadze, 2014. "4-Factor Model for Overnight Returns," Papers 1410.5513, arXiv.org, revised Jun 2015.
    14. Tai, Chu-Sheng, 2003. "Are Fama-French and momentum factors really priced?," Journal of Multinational Financial Management, Elsevier, vol. 13(4-5), pages 359-384, December.
    15. Campbell, John Y. & Giglio, Stefano & Polk, Christopher & Turley, Robert, 2018. "An intertemporal CAPM with stochastic volatility," Journal of Financial Economics, Elsevier, vol. 128(2), pages 207-233.
    16. D. L. Wilcox & T. J. Gebbie, 2013. "On pricing kernels, information and risk," Papers 1310.4067, arXiv.org, revised Oct 2013.
    17. Lawrence White, 2004. "Mortgage Backed Securities: Another Way to Finance Housing," Working Papers 04-14, New York University, Leonard N. Stern School of Business, Department of Economics.
    18. Stefan Nagel, 2013. "Empirical Cross-Sectional Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 5(1), pages 167-199, November.
    19. Raymond Kan & Guofu Zhou, 1999. "A Critique of the Stochastic Discount Factor Methodology," Journal of Finance, American Finance Association, vol. 54(4), pages 1221-1248, August.
    20. Attiya Yasmeen Javid, 2000. "Alternative Capital Asset Pricing Models: A Review of Theory and Evidence," PIDE Research Report 2000:3, Pakistan Institute of Development Economics.
    21. Drew, Michael E. & Naughton, Tony & Veeraraghavan, Madhu, 2004. "Is idiosyncratic volatility priced?: Evidence from the Shanghai Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 13(3), pages 349-366.

    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:gam:jrisks:v:12:y:2024:i:9:p:148-:d:1478550. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.