STRATEGIC INVESTMENT IN BIST100: A MACHINE LEARNING APPROACH USING SYMBOLIC AGGREGATE APPROXIMATION CLUSTERING

Nalici M. E., Söylemez İ., Ünlü R.

International Journal of Industrial Engineering : Theory Applications and Practice, vol.32, no.2, pp.382-395, 2025 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 2
  • Publication Date: 2025
  • Doi Number: 10.23055/ijietap.2025.32.2.10273
  • Journal Name: International Journal of Industrial Engineering : Theory Applications and Practice
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Compendex
  • Page Numbers: pp.382-395
  • Keywords: BIST100, Financial Access, Machine Learning, Stock Market, Symbolic Aggregate Approximation (SAX)
  • Abdullah Gül University Affiliated: Yes

Abstract

This 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.