IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v61y2024ics1544612324000448.html
   My bibliography  Save this article

Portfolio optimization based on the pre-selection of stocks by the Support Vector Machine model

Author

Listed:
  • Silva, Natan Felipe
  • de Andrade, Lélis Pedro
  • da Silva, Washington Santos
  • de Melo, Maísa Kely
  • Tonelli, Adriano Olímpio

Abstract

This study aims to analyze the performance of an investment portfolio using the Markowitz model, which maximizes the Sharpe ratio from a set of assets preselected through the Support Vector Machine (SVM) model using fundamental indicators in the Brazilian stock market. With an accuracy of 61% for the SVM model, the results indicate that preselecting assets based on fundamental indicators and subsequently optimizing them by maximizing the Sharpe ratio showed a superior return and faster recovery after drawdown periods compared to the benchmark or SVM (1/n) strategy. These results suggest the relevance of including the SVM in the optimization portfolio process.

Suggested Citation

  • Silva, Natan Felipe & de Andrade, Lélis Pedro & da Silva, Washington Santos & de Melo, Maísa Kely & Tonelli, Adriano Olímpio, 2024. "Portfolio optimization based on the pre-selection of stocks by the Support Vector Machine model," Finance Research Letters, Elsevier, vol. 61(C).
    • Handle: RePEc:eee:finlet:v:61:y:2024:i:c:s1544612324000448
    DOI: 10.1016/j.frl.2024.105014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324000448
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2024.105014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    2. Mir Seyed Mohammad Mohsen Emamat & Caroline Maria de Miranda Mota & Mohammad Reza Mehregan & Mohammad Reza Sadeghi Moghadam & Philippe Nemery, 2022. "Using ELECTRE-TRI and FlowSort methods in a stock portfolio selection context," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    3. Wang, Xianhe & Ouyang, Yuliang & Li, You & Liu, Shu & Teng, Long & Wang, Bo, 2023. "Multi-objective portfolio selection considering expected and total utility," Finance Research Letters, Elsevier, vol. 58(PD).
    4. Xin‐Ping Song & Zhi‐Hua Hu & Jian‐Guo Du & Zhao‐Han Sheng, 2014. "Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 611-626, December.
    5. Luis Lorenzo & Javier Arroyo, 2023. "Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    6. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    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. Marcos López de Prado & Joseph Simonian & Francesco A. Fabozzi & Frank J. Fabozzi, 2025. "Enhancing Markowitz's portfolio selection paradigm with machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 319-340, March.

    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. Hany Shawky & Ronald Forbes & Alan Frankle, 1983. "Liquidity Services and Capital Market Equilibrium: The Case for Money Market Mutual Funds," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 6(2), pages 141-152, June.
    2. Shaikh, Salman, 2013. "Investment Decisions by Analysts: A Case Study of KSE," MPRA Paper 53802, University Library of Munich, Germany.
    3. Ladislav Kristoufek & Paulo Ferreira, 2018. "Capital asset pricing model in Portugal: Evidence from fractal regressions," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 17(3), pages 173-183, November.
    4. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
    5. Sebastian Lobe & Christian Walkshäusl, 2016. "Vice versus virtue investing around the world," Review of Managerial Science, Springer, vol. 10(2), pages 303-344, March.
    6. repec:bdu:ojijfa:v:1:y:2016:i:1:p:108-123:id:122 is not listed on IDEAS
    7. Lars Hornuf & Gül Yüksel, 2022. "The Performance of Socially Responsible Investments: A Meta-Analysis," CESifo Working Paper Series 9724, CESifo.
    8. Maria Teresa Medeiros Garcia & Gonçalo Liberal, 2019. "The impact of hedge fund indices on portfolio performance," Working Papers REM 2019/85, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    9. Pat Wilson & John Okunev & Guy Ta, 1994. "Are Real Estate and Securities Markets Integrated? Some Australian Evidence," Working Paper Series 42, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    10. 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.
    11. Bosch-Badia, Maria Teresa & Montllor-Serrats, Joan & Tarrazon-Rodon, Maria-Antonia, 2014. "Unveiling the embedded coherence in divergent performance rankings," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 154-165.
    12. Walter Briec & Kristiaan Kerstens & Octave Jokung, 2007. "Mean-Variance-Skewness Portfolio Performance Gauging: A General Shortage Function and Dual Approach," Management Science, INFORMS, vol. 53(1), pages 135-149, January.
    13. Łukasz Dopierała & Magdalena Mosionek-Schweda & Daria Ilczuk, 2020. "Does the Asset Allocation Policy Affect the Performance of Climate-Themed Funds? Empirical Evidence from the Scandinavian Mutual Funds Market," Sustainability, MDPI, vol. 12(2), pages 1-23, January.
    14. Nisani, Doron & Shelef, Amit & Sonenshine, Ralph & David, Or, 2024. "The mutual funds puzzle and the elusive von Neumann-Morgenstern preference relation," International Review of Financial Analysis, Elsevier, vol. 92(C).
    15. Matallin-Saez Juan Carlos, 2008. "The Dynamics of Mutual Funds and Market Timing Measurement," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(4), pages 1-37, December.
    16. Vidal, Marta & Vidal-García, Javier & Bekiros, Stelios & Segovia, Juan Evangelista Trinidad, 2025. "A comparison of international mutual funds efficiency," Finance Research Letters, Elsevier, vol. 73(C).
    17. Gregory Price & Warren Whatley, 2021. "Did profitable slave trading enable the expansion of empire?: The Asiento de Negros, the South Sea Company and the financial revolution in Great Britain," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 15(3), pages 675-718, September.
    18. Lu, Chia-Wu & Wu, Hsueh-Ling & Su, Yu-Hsuan, 2024. "The icing on the cake: ESG effect on the quality factor portfolios," Finance Research Letters, Elsevier, vol. 70(C).
    19. Ikhlaas Gurrib & Firuz Kamalov & Elgilani Elshareif, 2021. "Can the Leading US Energy Stock Prices be Predicted using the Ichimoku Cloud?," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 41-51.
    20. Jan Bastin, 2015. "Volatility Effect: An Application on the German Stock Market [Efekt nízkého rizika: Aplikace na německý akciový trh]," Český finanční a účetní časopis, Prague University of Economics and Business, vol. 2015(1), pages 36-54.
    21. Fracasso, Laís Martins & Müller, Fernanda Maria & Ramos, Henrique Pinto & Righi, Marcelo Brutti, 2023. "Is there a risk premium? Evidence from thirteen measures," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 182-199.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:eee:finlet:v:61:y:2024:i:c:s1544612324000448. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.