IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v10y2022i4p84-d793303.html
   My bibliography  Save this article

A Review on Machine Learning for Asset Management

Author

Listed:
  • Pedro M. Mirete-Ferrer

    (Escuela Internacional de Doctorado Universidad de Murcia, Interuniversity Doctorate in Economics (DEcIDE), 30100 Murcia, Spain
    Faculty of Economics and Business Administration (ICADE), Universidad Pontificia Comillas, 28015 Madrid, Spain
    These authors contributed equally to this work.)

  • Alberto Garcia-Garcia

    (Departamento de Tecnología Informática y Computación, Universidad de Alicante, 03690 Alicante, Spain
    These authors contributed equally to this work.)

  • Juan Samuel Baixauli-Soler

    (Facultad de Economía y Empresa, Universidad de Murcia, 30100 Murcia, Spain)

  • Maria A. Prats

    (Facultad de Economía y Empresa, Universidad de Murcia, 30100 Murcia, Spain)

Abstract

This paper provides a review on machine learning methods applied to the asset management discipline. Firstly, we describe the theoretical background of both machine learning and finance that will be needed to understand the reviewed methods. Next, the main datasets and sources of data are exposed to help researchers decide which are the best ones to suit their targets. After that, the existing methods are reviewed, highlighting their contribution and significance in the analyzed financial disciplines. Furthermore, we also describe the most common performance criteria that are applied to compare such methods quantitatively. Finally, we carry out a critical analysis to discuss the current state-of-the-art and lay down a set of future research directions.

Suggested Citation

  • Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:4:p:84-:d:793303
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/10/4/84/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/10/4/84/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
    2. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    3. Weiwei Jiang, 2020. "Applications of deep learning in stock market prediction: recent progress," Papers 2003.01859, arXiv.org.
    4. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    5. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    6. Hyungjun Park & Min Kyu Sim & Dong Gu Choi, 2019. "An intelligent financial portfolio trading strategy using deep Q-learning," Papers 1907.03665, arXiv.org, revised Nov 2019.
    7. Eduard Krkoska & Klaus Reiner Schenk-Hoppé, 2019. "Herding in Smart-Beta Investment Products," JRFM, MDPI, vol. 12(1), pages 1-14, March.
    8. Shailesh Kumar, 2019. "Machine Learning (Supervised)," International Series in Operations Research & Management Science, in: Bhimasankaram Pochiraju & Sridhar Seshadri (ed.), Essentials of Business Analytics, chapter 0, pages 507-568, Springer.
    9. William N. Goetzmann & Alok Kumar, 2008. "Equity Portfolio Diversification," Review of Finance, European Finance Association, vol. 12(3), pages 433-463.
    10. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    11. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    12. Campbell R. Harvey, 2017. "Presidential Address: The Scientific Outlook in Financial Economics," Journal of Finance, American Finance Association, vol. 72(4), pages 1399-1440, August.
    13. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    14. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    15. Periklis Gogas & Theofilos Papadimitriou & Dimitrios Karagkiozis, 2018. "The Fama 3 and Fama 5 factor models under a machine learning framework," Working Paper series 18-05, Rimini Centre for Economic Analysis.
    16. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    17. R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
    18. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    19. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
    20. Jinho Lee & Jaewoo Kang, 2020. "Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-20, April.
    21. Nicolas Huck, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," Post-Print hal-02143971, HAL.
    22. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    23. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    24. Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
    25. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    26. Daniel, Kent & Titman, Sheridan, 1997. "Evidence on the Characteristics of Cross Sectional Variation in Stock Returns," Journal of Finance, American Finance Association, vol. 52(1), pages 1-33, March.
    27. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    28. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    29. Cooper, Ilan & Maio, Paulo, 2019. "New Evidence on Conditional Factor Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(5), pages 1975-2016, October.
    30. Justin Sirignano & Rama Cont, 2019. "Universal features of price formation in financial markets: perspectives from deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1449-1459, September.
    31. Jegadeesh, Narasimhan, 1990. "Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-898, July.
    32. He, Zhiguo & Kelly, Bryan & Manela, Asaf, 2017. "Intermediary asset pricing: New evidence from many asset classes," Journal of Financial Economics, Elsevier, vol. 126(1), pages 1-35.
    33. Gah-Yi Ban & Noureddine El Karoui & Andrew E. B. Lim, 2018. "Machine Learning and Portfolio Optimization," Management Science, INFORMS, vol. 64(3), pages 1136-1154, March.
    34. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    35. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    36. Prayut Jain & Shashi Jain, 2019. "Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification," Risks, MDPI, vol. 7(3), pages 1-27, July.
    37. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    38. Robert Novy-Marx & Mihail Velikov, 2016. "A Taxonomy of Anomalies and Their Trading Costs," The Review of Financial Studies, Society for Financial Studies, vol. 29(1), pages 104-147.
    39. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    40. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    41. Serhiy Kozak & Stefan Nagel & Shrihari Santosh, 2018. "Interpreting Factor Models," Journal of Finance, American Finance Association, vol. 73(3), pages 1183-1223, June.
    42. Pitera, Marcin & Schmidt, Thorsten, 2018. "Unbiased estimation of risk," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 133-145.
    43. Marcin Pitera & Thorsten Schmidt, 2016. "Unbiased estimation of risk," Papers 1603.02615, arXiv.org, revised Aug 2017.
    44. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brahmana, Rayenda Khresna, 2022. "Do Machine Learning Approaches Have the Same Accuracy in Forecasting Cryptocurrencies Volatilities?," MPRA Paper 119598, University Library of Munich, Germany.

    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. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    2. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    3. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    4. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    5. De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
    6. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    7. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    8. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    9. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
    10. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
    11. Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
    12. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
    13. Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    14. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, September.
    15. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
    16. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    17. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    18. Harvey, Campbell R. & Liu, Yan, 2021. "Lucky factors," Journal of Financial Economics, Elsevier, vol. 141(2), pages 413-435.
    19. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    20. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).

    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:10:y:2022:i:4:p:84-:d:793303. 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.