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

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  • 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
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