Implementation of K-means Clustering Algorithm for the Indonesian Stock Exchange

Abstract

In the dynamic field of financial markets, effective analysis and understanding of stock market behavior are very crucial for investors, analysts, and policymakers. This study investigates the implementation of the K-means algorithm for clustering stocks listed on the Indonesian Stock Exchange (IDX). The main objectives of this research include exploring IDX's clustering patterns, identifying groups based on their trading characteristics, and evaluating algorithm performance. Some challenging parts have been addressed, such as data quality, feature selection, determining the optimal number of clusters, scalability, interpretability, and evaluation. Precise data preprocessing, feature engineering, and algorithm optimization provide insight into the clustering structure of the Indonesian stock market, helping investors in portfolio diversification, risk management, and strategic decision-making. The results show the potential of the K-means algorithm in thoroughly uncovering important patterns on the IDX, thereby contributing to the advancement of market analysis methodologies adapted to the Indonesian financial environment.