Stock Clustering Framework using Financial Ratios: A Case Study in the Stock Exchange of Thailand

Kietikul Jearanaitanakij; Natdanai Poonpon; Chanidapa Wongtep; Kittaporn Buriyameathakul; Artitaya Pimsupaporn

doi:10.55003/ETH.420407

Authors

Kietikul Jearanaitanakij School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
Natdanai Poonpon School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
Chanidapa Wongtep School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
Kittaporn Buriyameathakul School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
Artitaya Pimsupaporn School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand

DOI:

https://doi.org/10.55003/ETH.420407

Keywords:

Stock clustering, Composite index, Financial ratios, Stock exchange of Thailand, Investment

Abstract

Value investors typically seek undervalued stocks that align with specific financial criteria to maximize their margin of safety. However, manually analyzing the financial data of all listed stocks is a time-intensive process. Furthermore, the market price of a target stock may exceed its intrinsic value, introducing potential investment risks. To address these challenges, this study proposes a stock clustering framework that groups equities based on financial ratio similarity. The proposed framework is designed to streamline the investment decision-making process by recommending stocks with comparable financial profiles as alternatives to those currently attracting investor interest but that may already be overvalued. Multiple clustering algorithms are evaluated to determine the most effective grouping strategy. Empirical back testing using four years of data from the Stock Exchange of Thailand reveals that the Gaussian Mixture Model (GMM) achieves the highest composite performance metric among the tested methods. Additionally, the HDBSCAN algorithm is employed to detect and exclude outlier stocks, thereby enhancing the reliability of the clustering results.

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