Determination of Optimal Portfolio by Calculating Transaction Costs using Genetic Algorithms on the Jakarta Islamic Index

Sinta Oktavia Nur Fadhila, Agus Maman Abadi, Ezra Putranda Setiawan
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Abstract

The optimal portfolio is a portfolio that can provide maximum returns at the same level of risk. In investing, the term "high return, high risk" is known, meaning that the higher the return, the higher the risk. Therefore, investors need to develop an optimal portfolio to obtain the maximum return on investment at the same level of risk. This study aims to determine the optimal formation of a stock portfolio by calculating transaction costs using the genetic algorithm method on stocks that are members of the Jakarta Islamic Index. This research uses data of daily return on stocks included in Jakarta Islamic Index from 1 August 2020-1 August 2022. The dataset consists of two variables: the date of observation and daily stock returns. The method used in this study is the minimum variance method and the genetic algorithm. Data analysis was divided into two stages: model formulation and model testing through case studies. The analysis of optimal portfolio formation using genetic algorithms shows that in terms of performance, the minimum variance portfolio is superior to the genetic algorithm portfolio, as indicated by the Sharpe ratio value. Meanwhile, the genetic algorithm portfolio is superior to the minimum variance portfolio regarding transaction costs. The genetic algorithm portfolio can provide a fairly high total return, small transaction costs, and good performance compared to the minimum portfolio. Hence, the genetic algorithm portfolio is worthy of recommendation to investors.
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利用遗传算法计算雅加达伊斯兰指数的交易成本,确定最佳投资组合
最佳投资组合是指能够在相同风险水平下提供最大回报的投资组合。在投资领域,众所周知 "高回报、高风险",即回报越高,风险越大。因此,投资者需要制定一个最优的投资组合,以获得相同风险水平下的最大投资回报。本研究旨在通过使用遗传算法计算雅加达伊斯兰指数成员股票的交易成本,确定股票投资组合的最佳形成。本研究使用的是 2020 年 8 月 1 日至 2022 年 8 月 1 日雅加达伊斯兰指数股票的每日收益数据。数据集由两个变量组成:观察日期和每日股票收益率。本研究采用的方法是最小方差法和遗传算法。数据分析分为两个阶段:建立模型和通过案例研究检验模型。对使用遗传算法形成的最优投资组合的分析表明,就绩效而言,从夏普比率值来看,最小方差投资组合优于遗传算法投资组合。同时,在交易成本方面,遗传算法组合优于最小方差组合。与最小方差组合相比,遗传算法组合可以提供相当高的总回报率、较小的交易成本和良好的绩效。因此,遗传算法投资组合值得向投资者推荐。
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