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Investment analytics using association rule mining (Finassociations)

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  • Elif Kartal
  • M. Erdal Balaban
  • Zeki Özen

Abstract

This study aims to discover financial associations (relations) in (foreign) exchange rates, cryptocurrencies, and stocks using association rule mining (ARM). It demonstrates the applicability and success of ARM on alternative investment instruments over desired periods. A dynamic web application called 'Finassociations' was developed in this scope, allowing investors to use and discover ARM. They can use the desired filters to make investment decisions by generating rules for which investment instruments rise or fall together. The application dynamically retrieves current data from Yahoo Finance. This study is a dynamic and expanded update on the existing ones. The exemplary analyses utilised data spanning various periods, up to two years preceding October 9, 2022. According to the study results, significant and strong financial associations in three different investment groups can be obtained. Also, the results show that short-term financial data can be preferred over long-term financial data when examining associations between investment instruments.

Suggested Citation

  • Elif Kartal & M. Erdal Balaban & Zeki Özen, 2026. "Investment analytics using association rule mining (Finassociations)," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 16(1/2), pages 3-22.
    • Handle: RePEc:ids:ijcome:v:16:y:2026:i:1/2:p:3-22
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