投资组合优化:马科维茨模型和单一指数模型在印度尼西亚证券交易所 LQ45 股票上的应用与比较

Budi Oktavianus Yusan, Selamet Riyadi
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摘要

本文采用马科维茨模型(均值-方差模型)和单一指数模型这两种模型研究了印度尼西亚股票投资组合的优化问题。数据包括 LQ 45 种股票从 2016 年 1 月到 2021 年 12 月的历史回报。重点是根据两种模型为投资组合选择股票并确定其权重。研究使用夏普(Sharpe)和特雷诺(Treynor)测量法比较了两个优化组合与 LQ45 指数基准、IHSG 市场以及彼此的表现。本文检验了两种模型优化组合的股票组成在未来(2022 年 1 月 1 日 - 2022 年 12 月 31 日)和(2023 年 1 月 1 日 - 2023 年 12 月 31 日)是否能成功且持续地产生优于 LQ45 和 IHSG 的表现。结果显示,2022 年和 2023 年的投资组合表现形成了明显的对比。2022 年,马科维茨投资组合和单一指数投资组合的回报都非常可观,超过了 LQ45 和 IHSG。然而,在 2023 年,这两个投资组合的表现都大幅落后,出现了负回报和不利的风险调整指标。这些发现强调了金融市场的动态性质,以及对投资组合进行持续监控和调整的必要性。我们鼓励投资者根据不断变化的市场条件重新评估其投资组合策略。这项研究为了解优化投资组合的时变性及其对不断变化的市场动态的敏感性提供了宝贵的见解。
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Portfolio Optimization: Application and Comparison of Markowitz Model and Single Index Model on LQ45 Stocks in Indonesia Stock Exchange
This paper examines the optimization of an Indonesian stock portfolio using two models: the Markowitz Model (Mean-Variance Model) and the Single Index Model. The data comprises historical returns of LQ 45 stocks from January 2016 to December 2021. The focus is on selecting stocks for the portfolio and determining their weights based on the two models. The study compares the performance of both optimized portfolios with the LQ45 Index benchmark, IHSG market, and each other using Sharpe and Treynor measurements. The paper tests whether the stock composition of the optimized portfolio from both models successfully and consistently generates a better performance in the future (1 January 2022 – 31 December 2022) and (1 January 2023 – 31 December 2023) compared to both LQ45 and IHSG. The results reveal a notable contrast in portfolio performance between 2022 and 2023. In 2022, both the Markowitz and Single Index portfolios exhibited remarkable returns, surpassing LQ 45 and IHSG. However, in 2023, both portfolios experienced substantial underperformance, with negative returns and unfavorable risk-adjusted metrics. These findings underscore the dynamic nature of financial markets and the need for continuous portfolio monitoring and adaptation. Investors are encouraged to reevaluate their portfolio strategies in response to changing market conditions. The study contributes valuable insights into the temporal variability of optimized portfolios and their sensitivity to evolving market dynamics.
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