The time series clustering of stock price in LQ45 index and its financial performance analysis

Abstract

Abstract Stock price in time series data can be analyzed with the clustering method by using the autocorrelation function distance measurement method. The purpose of this study is to cluster stock prices with the same characteristics and analyze companies’ financial performance in each cluster and provide a reference to investors in making choices to develop their investments. This study uses time-series data from stock prices in the LQ45 index, which is continuously available and registered from January 2010 to December 2019, as many as 32 companies. The results of this study are obtained 3 clusters, where the first cluster contains 17 stocks, the second cluster contains six stocks, and the third cluster contains nine stocks. After clustering, the financial performance of each cluster is analyzed in 2019. The companies’ financial performance in the first cluster shows that the company has proper inventories, total assets, profit for the period, and can get great benefits. The third cluster shows that the company has a relatively good current ratio and demonstrates its ability to generate profits from the high assets and equity used. Meanwhile, the second cluster has quite high receivables.