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Portfolio Rebalancing Model Utilizing Support Vector Machine for Optimal Asset Allocation

  • Research Article-Systems Engineering
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Abstract

Achieving an optimal asset allocation strategy is critical for investors aiming to maximize returns while managing risk in a dynamic financial market. This research presents a portfolio rebalancing model that leverages the power of Support Vector Machine (SVM) algorithms to optimize asset allocation decisions and enhance portfolio performance. For this purpose, initially, the assets are classified based on the SVM technique to identify the most favorable assets. Secondly, a widely accepted mean–variance optimization technique is employed by this method to balance risk and return objectives, accommodating diverse transaction costs, time horizons, and other realistic conditions, thus facilitating the selection of assets and their respective weights for both initial investment and the rebalancing of the existing portfolio. Further, to assess the effectiveness of the portfolio rebalancing model incorporating SVM, comprehensive historical testing and performance analyses are conducted using historical data from the Indian Stock Market. This evaluation is contrasted with outcomes derived from other prevalent machine learning techniques such as K-Nearest Neighbors, Naive Bayes, Random Forest, and Decision Tree. By comparing the results from different methodologies, the study highlights the superior efficacy of the SVM-based portfolio rebalancing model in achieving optimal asset allocation in a dynamic financial landscape.

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Acknowledgements

The authors express their sincere appreciation to the referee for their rigorous review and valuable insights, which significantly improved the quality and clarity of the manuscript. The referee’s expertise and thoughtful feedback have been instrumental in enhancing the overall scholarly impact of our work.

Funding

This research has no funding by any organization or individual.

Author information

Authors and Affiliations

  1. Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    B. R. B. Sahu & P. Kumar

  2. DoMSC, National Institute of Technology Hamirpur, Hamirpur, HP, 177005, India

    P. Kumar

Authors
  1. B. R. B. Sahu
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  2. P. Kumar
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Corresponding author

Correspondence to P. Kumar.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Appendix

Appendix

See Tables 8, 9, 10, 11, 12, 13, and 14.

Table 8 Return and variance of 50 assets
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Table 9 Parameters for Portfolio rebalancing optimization problem
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Table 10 SVM degree 2 \(+1\) class selected assets for different time horizons
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Table 11 DT \(+1\) class selected assets for different time horizons
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Table 12 KNN \(+1\) class selected assets for different time horizons
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Table 13 NB \(+1\) class selected assets for different time horizons
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Table 14 RF \(+1\) class selected assets for different time horizons
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Sahu, B.R.B., Kumar, P. Portfolio Rebalancing Model Utilizing Support Vector Machine for Optimal Asset Allocation. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08850-9

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