IDX30 Stocks Clustering with K-Means Algorithm based on Expected Return and Value at Risk

Ahmad Fawaid Ridwan; Subiyanto Subiyanto; Sudradjat Supian

doi:10.46336/ijqrm.v2i4.157

IDX30 Stocks Clustering with K-Means Algorithm based on Expected Return and Value at Risk

Ahmad Fawaid Ridwan, Subiyanto Subiyanto, Sudradjat Supian

Abstract

Stocks are one of the investment instruments available in the capital market. Several indices show the characteristics of stocks listed on the Indonesia Stock Exchange. IDX30 is one of several indications that show the combined stocks are stocks with large market capitalization, high liquidity, and good fundamentals. The selection of assets to be allocated in the portfolio is an important factor in investing where the purpose of investing is to maximize returns and minimize risk. This study aims to classify stocks that have certain characteristics based on the expected return and value at risk of the stocks incorporated in IDX30 with a clustering algorithm. The clustering algorithm used is the K-Means algorithm. K-Means is a non-hierarchical clustering algorithm by groups each object based on its proximity to the cluster center. The method used in this research is a clustering simulation study using the K-Means algorithm on IDX30 stock data. By identifying the characteristics of the stock based on the characteristics of the cluster formed, it is hoped that it can be considered in choosing the assets to be used in the formation of an optimal portfolio.

Keywords

expected return, IDX30, K-Means, stock, value at risk

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DOI: https://doi.org/10.46336/ijqrm.v2i4.157