UNDERSTANDING LQ45 STOCKS (2021-2023) WITH K-MEANS CLUSTERING - Febe, Margareta; Theotista, Giovanny; Winson, Winson (BAREKENG: Jurnal Ilmu Matematika dan Terapan, 2025)
The primary aim of this study is to examine the use of K-Means clustering in analyzing LQ45 stocks from 2021 to 2023, utilizing data obtained from the Yahoo Finance platform. The analysis delves into key performance measures such as the price-to-earnings ratio (PER), earnings per share (EPS), dividends, trading volume, and historical return on investment. This technique categorizes stocks with similar characteristics, providing financial analysts, money managers, and investors with valuable insights. The objective of the clustering analysis is to gain a deeper understanding of the relationship between intrinsic stock features and the inherent price volatility of companies. This is accomplished by using historical datasets to conduct stock feature analysis. Mathematics plays a crucial role in the K-Means model by providing the foundational algorithms and statistical methods used to categorize and analyze the data. The study contributes to the field of financial market analysis by demonstrating how understanding group-to-group dynamics can affect investment decisions and offering a more precise representation of large datasets in financial contexts. These findings provide significant insights for individuals involved in financial matters in the stock market, helping to identify potential investment opportunities and reduce risk more effectively.