Nalici, Mehmet ErenSoylemez, IsmetUnlu, Ramazan01. Abdullah Gül University02.02. Endüstri Mühendisliği02. Mühendislik Fakültesi07. Fen Bilimleri Enstitüsü07.03. Endüstri Mühendisliği Anabilim Dalı2025-09-252025-09-2520251072-47611943-670Xhttps://doi.org/10.23055/ijietap.2025.32.2.10273https://hdl.handle.net/20.500.12573/4690Nalici, Mehmet Eren/0000-0002-7954-6916This study employs the Symbolic Aggregate Approximation (SAX) clustering method to enhance investor decision-making on the Borsa Istanbul (BIST100) by identifying companies exhibiting analogous stock movements. The data from 81 BIST100 companies over a three-year period has been analyzed, with a focus on risk minimization and strategic investment. The SAX method, integrated with a dendrogram, categorizes stocks into sector-based and non-sector-based clusters, providing insights for portfolio optimization. The results demonstrate the effectiveness of the method in identifying relevant stock patterns across sectors, aiding in more informed investment decisions. This approach highlights the need for considering multiple factors in investment strategies, offering a new perspective on stock market analysis with advanced clustering techniques.eninfo:eu-repo/semantics/closedAccessBist100Financial AccessMachine LearningStock MarketSymbolic Aggregate Approximation (SAX)Article10.23055/ijietap.2025.32.2.102732-s2.0-105001969547