Investment Portfolio Optimization using Mean-Semi Standard Deviation Model (Case Study: BBNI, BBCA, BMRI, TLKM, and ANTM)

https://doi.org/10.47194/orics.v5i4.353

Authors

  • Moch Panji Agung Saputra Department of Mathematics, Universitas padjadjaran
  • Rizki Apriva Hidayana Department of Mathematics, Faculty of Mathematics and Natural Sciences, National University of the Republic of Indonesia, Bandung, Indonesia
  • Grida Saktian Laksito Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia

Abstract

This study aims to determine the optimal stock portfolio allocation using the Mean-Semi Standard Deviation optimization model as an alternative to the more commonly used Mean-Variance model. The Mean-Semi Standard Deviation model considers downside risk, which is more relevant to investors' preferences in minimizing potential losses. The data used in this study consists of daily closing prices of five stocks listed on the Indonesia Stock Exchange (BBNI, BBCA, BMRI, TLKM, and ANTM) from December 7, 2023, to December 6, 2024. The optimization process was conducted using the Lagrange method to maximize the portfolio's expected return with controlled risk, incorporating a risk aversion parameter (ro) to adjust for investor preferences. The results show that the portfolio with a risk aversion value of ro=0.1 provides the highest return-to-risk ratio of 0.058556, with the largest portfolio weight allocated to BBCA stock. The findings suggest that the Mean-Semi Standard Deviation model can serve as a more effective approach to portfolio management in the Indonesian stock market, particularly in reducing downside risk amid high market volatility.

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Published

2025-01-06

How to Cite

Saputra, M. P. A., Hidayana, R. A., & Laksito, G. S. (2025). Investment Portfolio Optimization using Mean-Semi Standard Deviation Model (Case Study: BBNI, BBCA, BMRI, TLKM, and ANTM). Operations Research: International Conference Series, 5(4), 146–154. https://doi.org/10.47194/orics.v5i4.353