Clustering and Financial Performance Analysis of Indonesian Coal Mining Industry Stock Prices

Achmad Naufal, Muhammad Fadhil, Wisudanto
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Abstract

In the comprehensive study of the Indonesian coal mining industry, a rigorous exploration and clustering approach was applied to historical stock price data and financial metrics from coal companies listed for over a decade. Data sourced from Yahoo Finance underwent an automated download process, ensuring consistency and efficiency. The research utilized robust clustering techniques, including K-means, Hierarchical, and Correlation Clustering, to discern the stock price movements during the dynamic market conditions of 2022 and 2023. Financial performance analysis, focusing on key metrics such as ROA, NPM, and EPS, highlighted the unique financial dynamics of companies like PTIS, IATA, and AIMS. The study's results provide a multifaceted understanding of the coal industry's financial trends, emphasizing varied company responses to market conditions and revealing significant financial performance divergences among key players. This research not only offers invaluable insights into the coal industry's financial landscape but also presents an innovative methodology poised to transform financial analytics in the sector.
印度尼西亚煤矿行业股票价格的聚类和财务绩效分析
在对印尼煤炭开采业的综合研究中,对上市十多年的煤炭公司的历史股价数据和财务指标采用了严格的探索和聚类方法。数据来源于雅虎财经(Yahoo Finance),经过了自动下载过程,确保了数据的一致性和效率。研究采用了强大的聚类技术,包括 K-均值聚类、层次聚类和相关聚类,以辨别 2022 年和 2023 年动态市场条件下的股价走势。财务业绩分析侧重于 ROA、NPM 和 EPS 等关键指标,突出了 PTIS、IATA 和 AIMS 等公司独特的财务动态。研究结果提供了对煤炭行业财务趋势的多方面理解,强调了各公司对市场条件的不同反应,并揭示了主要公司之间财务业绩的显著差异。这项研究不仅为煤炭行业的财务状况提供了宝贵的见解,还提出了一种创新方法,有望改变该行业的财务分析方法。
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