A DEA-CCR Model Application in Clustered Stocks Portfolio with Technical Investment Strategies and Mean-Variance Model

The stock market is one of the investment options that offer the best potential returns. However, investors are aware of the risk associated with their investments, which is why stock portfolios are established to diversify investments or distribute investments. Managing a portfolio may be challenging because there are so many different stocks. One of the challenges is deciding how much money to invest and how to allocate it for the best results. This paper used the K-means Algorithm for cluster analysis in identifying Decision Making Unit (DMU), the stocks will be grouped according to their characteristics considering their risk and return. Asset allocation per clustered portfolio was performed and the Mean-Variance Model was used to optimize clustered portfolios. This paper aims to use Data Envelopment Analysis (DEA) to evaluate the efficiency of these clustered portfolios/DMUs. In addition, this paper introduces subjective choice in decision-making using the criterion's level of importance to help investors decide which among these efficient DMUs is a better option. The results show 4 Efficient DMUs for 2019 Data, and 3 Efficient DMUs in 2020 Data. All simulations were carried out using the MATLAB environment platform.