Clustering Stock Prices of Industrial and Consumer Sector Companies in Indonesia Using Fuzzy C-Means and Fuzzy C-Medoids Involving ACF and PACF

Fundamental and technical analysis that investors generally use to select the best stocks cannot provide information regarding the similarity of stock price characteristics of companies in one sector. Even though companies are in the same sector, each company has a different ability to earn profits and overcome financial difficulties. So, clustering is done to find the stock prices of companies with the same characteristics in one sector. This research uses data on industrial and consumer sector companies’ stock prices because the industrial and consumer sectors are one of the largest sectors in Indonesia. The variables used in this research are open, close, and HML (High Minus Low) stock prices. Several clustering methods that can be used to cluster time series data are Fuzzy C-Means and Fuzzy C-Medoids. In addition, this research also uses several approaches with ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function), which can handle data with high dimensions and allow the comparison of time series data with different lengths. Based on the highest FS (Fuzzy Silhouette) value, the empirical results show that the two best methods for clustering open, close, and HML stock prices are Fuzzy C-Means and Fuzzy C-Medoids. The clustering results using Fuzzy C-Means are the same for open stock prices and close stock prices data. Meanwhile, there are different clustering results for HML stock price data.