Articles

Portfolio Optimization Using Markowitz Model on Sri-Kehati Index

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This thesis investigates the portfolio optimization process using the Markowitz model on the SRI-KEHATI index, an esteemed sustainable investment index. The study aims to explore the potential advantages of incorporating environmental, social, and governance (ESG) factors into portfolio construction. By leveraging historical financial data and reliable ESG metrics, this research develops optimized portfolios that strike a balance between risk and return while adhering to the sustainability criteria of the SRIKEHATI index.

The methodology encompasses the collection of credible ESG data and financial information for the constituents of the SRIKEHATI index. The Markowitz model is subsequently employed to analyze the risk and return characteristics of each asset within the portfolio. Through the application of optimization algorithms, the study seeks to identify the optimal asset allocation that maximizes risk-adjusted returns, taking into account the ESG criteria outlined by the SRI-KEHATI index.

The outcomes of this research provide valuable insights into the effectiveness of portfolio optimization techniques within the realm of sustainable investing. By considering both financial metrics and ESG factors, investors can construct portfolios that align with their sustainability objectives while optimizing risk and return. The findings shed light on the performance of the optimized portfolios and compare them with conventional approaches, thereby demonstrating the potential benefits of integrating ESG considerations into portfolio decision-making.

Additionally, this study examines the practical implications associated with implementing sustainable portfolio strategies based on the SRI-KEHATI index.

Overall, this thesis contributes to the expanding body of knowledge on sustainable investing and portfolio optimization, specifically focusing on the SRI-KEHATI index. It provides valuable insights for investors, asset managers, and policymakers interested in sustainable investment strategies. Furthermore, it offers a framework for incorporating ESG considerations into the portfolio construction process using the Markowitz model, thereby aiding in the development of more robust and sustainable investment portfolios.

Optimal Portfolio Construction Using Bitcoin, Gold, LQ45 Index, and Indonesia Bond Index

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Cryptocurrencies are significant improvements in the digital age that have changed the way we think about money. The first cryptocurrency was Bitcoin, introduced in 2009 and was created by Nakamoto. Due to their potential ups and downs, many people now think that cryptocurrencies are appropriate for use as an investment instrument, especially millennials who are attracted to higher-risk investment alternatives. A number of different investing options such as cryptocurrencies, gold, and other conventional assets like equities and bonds have unique characteristics and advantages. It’s essential for investors to understand the similarities and differences between cryptocurrencies and other assets in order to create diversified portfolios.

In this study, the optimum portfolio will be constructed using Bitcoin, Gold, LQ45 Index, and ABF IBI as the representative of Indonesia Bond Index. Mean-Variance Optimization will be used as an asset allocation method, and will be compared to the other methods such as Risk Parity, 60/40 Portfolio, and Equally Weighted to find a better risk-adjusted return. The Sharpe ratio analysis is used to evaluate the portfolio performance resulting from every method. The investment strategy will be simulated to know which strategy will result the best total return in the end of simulation period.

According to risk, return, and the Sharpe ratio, Bitcoin could perform better than gold, LQ45, and ABF IBI. Furthermore, the Mean-Variance Optimization resulted the highest Sharpe ratio compared to the other methods. The optimal weight from the portfolio construction using Mean-Variance Optimization allocated 53% to ABFI index, 40% to Bitcoin, and 7% to gold, which resulted 48.2% portfolio return, 40.44% portfolio risk, and 1077.8% Sharpe ratio. From the investment strategy simulation, the quarterly rebalancing strategy was found to be the best strategy with the total return 223.36%.

Design and Evaluation of Robo-Advisors Using Index Fund and Alternative Assets of Cryptocurrency and Gold: Case of Indonesian Capital Market

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Robo-advisor is one of the most prominent innovation in the wealth management industry, and its success in Indonesia has been evident in the case of Bibit. Therefore, wealth management companies need to employ Robo-Advisor to overcome their competition. This research aims to give recommendation on asset allocation method and asset class selection for Robo-Advisors in Indonesia using Sharpe Ratio Analysis. Then, the author will analyze the robo-advisor’s performance during equity market downturn. Finally, The Robo-Advisor’s actual performance will be tested in 2018, 2019, and 2020. The Sharpe ratio analysis result showed that Robo-Advisors seeking higher risk-adjusted return should choose mean-variance optimization over risk parity for asset allocation method, and the inclusion of gold and bitcoin in a portfolio of stock mutual fund and bond mutual fund increases the risk-adjusted return of the portfolio. The proposed robo-advisor’s portfolio protected investors from equity market downturn in 2011-2010 in 83,3% of the case. Finally, the proposed robo-advisor’s portfolio generated better return for the conservative, moderate and aggressive investor during 2018, 2019, and 2020 when compared to LQ45.