ANALYSING MARKET DYNAMICS: REVEALING OBSCURED PATTERNS IN LQ45 STOCKS (2021-2023) USING WARD’S HIERARCHICAL CLUSTERING - Theotista, Giovanny; Febe, Margareta; Ryan, Michael Sannova (PATTIMURA UNIVERSITY, 2025)
This study aimed to address the instability of the Indonesian stock market from 2021 to 2023 by analyzing the LQ45 index, a critical indicator of economic robustness and corporate performance. Hierarchical Ward clustering was employed to categorize LQ45 stocks based on fundamental metrics such as Return, Volume, Price, Price-Earnings Ratio (PER), Earnings Per Share (EPS), and Dividends. Data preprocessing involved feature creation, Max-Abs scaling for normalization, and binary encoding of categorical variables. The optimal number of clusters was identified using dendrograms, revealing two primary clusters: one focusing on core materials and the other on financial services, alongside other industry-specific clusters. This method, characterized by its ability to minimize variance within clusters and determine natural groupings without predefined assumptions, provided valuable insights for financial advisors, policymakers, and investors. The findings offer practical guidance for optimizing decision-making, minimizing risks, and leveraging opportunities within the Indonesian stock market during a period of significant economic uncertainty. By employing this strategy, investors and traders can gain a comprehensive understanding of the current condition of the stock market, offering a thorough comprehension of the connections between equities and the operational and financial issues currently under scrutiny.