Optimasi Portofolio Saham BUMN20 Menggunakan Particle Swarm Optimization dan Genetic Algorithm dengan Pendekatan Maximizing Sharpe Ratio
DOI:
https://doi.org/10.55587/jla.v6i2.290Keywords:
Particle Swarm optimization, Genetic Algorithm, Maximizing Sharpe Ratio, BUMN20Abstract
Purpose: This study aims to construct and optimize a stock portfolio within the BUMN20 index by maximizing the Sharpe Ratio using the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) approaches under volatile market conditions.
Method: This study uses Adjusted Close Price data from 20 BUMN20 member stocks for the period from August 1, 2024, to August 1, 2025, obtained from Yahoo Finance, with stages including pre-processing to calculate returns and excess returns, portfolio optimization using PSO and GA, and performance evaluation based on returns, risk, and the Sharpe Ratio.
Findings: The results show that the PSO method yields a higher Sharpe Ratio of 0.112 compared to GA’s 0.101, with respective returns of 0.00322 and 0.00248 and risks of 0.02880 and 0.02440. The PSO portfolio tends to be concentrated in two main stocks, namely ANTM and PGEO, while GA produces a more diversified portfolio but with a lower risk-adjusted return.
Novelty: This study provides a comparative analysis between PSO and GA in stock portfolio optimization on the BUMN20 index using the Maximizing Sharpe Ratio (MSR) approach, thereby contributing to the development of portfolio optimization methods for indices that are sensitive to policy dynamics and political conditions.
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