IDEAS home Printed from https://ideas.repec.org/a/ibn/ibrjnl/v9y2016i12p13-22.html
   My bibliography  Save this article

Style Investing with Machine Learning

Author

Listed:
  • Philipp Kallerhoff

Abstract

This paper applies machine learning techniques to style investing. Support Vector Regression is applied to multi-factor investing based on momentum, dividend, quality, volatility and growth. The results show that Support Vector Regression selects stocks consistently with a higher efficiency ratio than a broad market investment and outperforms linear regression methods. The methods are applied to global stocks in the MSCI World index between 1996 and 2016. The behavior of both models is analyzed for economic sectors and over time. Interestingly, factors like low-volatility and momentum contribute both positively and negatively in some economic sectors and certain time periods.

Suggested Citation

  • Philipp Kallerhoff, 2016. "Style Investing with Machine Learning," International Business Research, Canadian Center of Science and Education, vol. 9(12), pages 13-22, December.
    • Handle: RePEc:ibn:ibrjnl:v:9:y:2016:i:12:p:13-22
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ibr/article/view/63910/34346
    Download Restriction: no
    File URL: http://www.ccsenet.org/journal/index.php/ibr/article/view/63910
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    3. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    4. 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.
    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. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    2. Ciciretti, Rocco & Dalò, Ambrogio & Dam, Lammertjan, 2023. "The contributions of betas versus characteristics to the ESG premium," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 104-124.
    3. Jeong, Giho & Kang, Jangkoo & Kwon, Kyung Yoon, 2018. "Liquidity skewness premium," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 130-150.
    4. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2020. "Firm profitability and expected stock returns: Evidence from Latin America," Research in International Business and Finance, Elsevier, vol. 51(C).
    5. Lin, Chaonan & Chen, Hong-Yi & Ko, Kuan-Cheng & Yang, Nien-Tzu, 2021. "Time-dependent lottery preference and the cross-section of stock returns," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 272-294.
    6. Cong, Lin William & George, Nathan Darden & Wang, Guojun, 2023. "RIM-based value premium and factor pricing using value-price divergence," Journal of Banking & Finance, Elsevier, vol. 149(C).
    7. Andrew Detzel, 2017. "Monetary Policy Surprises, Investment Opportunities, And Asset Prices," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 40(3), pages 315-348, September.
    8. Lou, Dong & Polk, Christopher & Skouras, Spyros, 2019. "A tug of war: Overnight versus intraday expected returns," Journal of Financial Economics, Elsevier, vol. 134(1), pages 192-213.
    9. Jansen, Maarten & Swinkels, Laurens & Zhou, Weili, 2021. "Anomalies in the China A-share market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    10. Becker, Janis & Hollstein, Fabian & Prokopczuk, Marcel & Sibbertsen, Philipp, 2019. "The Memory of Beta Factors," Hannover Economic Papers (HEP) dp-661, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    11. Sudipta Das, 2019. "Asset Pricing Test Using Alternative Sets of Portfolios: Evidence from India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(3), pages 339-354, September.
    12. Calvet, Laurent E. & Betermier, Sebastien & Jo, Evan, 2019. "A Supply and Demand Approach to Equity Pricing," CEPR Discussion Papers 13974, C.E.P.R. Discussion Papers.
    13. Cakici, Nusret & Zaremba, Adam, 2021. "Liquidity and the cross-section of international stock returns," Journal of Banking & Finance, Elsevier, vol. 127(C).
    14. Cao, Zhiqi & Lv, Dayong & Sun, Zhenzhen, 2021. "Stock price manipulation, short-sale constraints, and breadth-return relationship," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    15. de Oliveira Souza, Thiago, 2016. "The size premium and intertemporal risk," Discussion Papers on Economics 3/2016, University of Southern Denmark, Department of Economics.
    16. Chiah, Mardy & Long, Huaigang & Zaremba, Adam & Umar, Zaghum, 2023. "Trade competitiveness and the aggregate returns in global stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    17. Doron Avramov & Tarun Chordia & Gergana Jostova & Alexander Philipov, 2022. "The Distress Anomaly is Deeper than You Think: Evidence from Stocks and Bonds [The prediction of corporate bankruptcy: a discriminant analysis]," Review of Finance, European Finance Association, vol. 26(2), pages 355-405.
    18. David Hirshleifer & Po-Hsuan Hsu & Dongmei Li, 2018. "Innovative Originality, Profitability, and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2553-2605.
    19. Sun, Kaisi & Wang, Hui & Zhu, Yifeng, 2022. "How is the change in left-tail risk priced in China?," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    20. EOM, Cheoljun & EOM, Yunsung & PARK, Jong Won, 2024. "Intermediate cross-sectional prospect theory value in stock markets: A novel method," International Review of Financial Analysis, Elsevier, vol. 93(C).

    More about this item

    Keywords

    hedge funds; machine learning; factor models; style investing;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:ibn:ibrjnl:v:9:y:2016:i:12:p:13-22. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.