Diffusion-Based Parameters for Stock Clustering: Sector Separation and Out-of-Sample Evidence

Piyarat Promsuwan, Paisit Khanarsa () and Kittisak Chumpong ()
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Piyarat Promsuwan: Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, Thailand
Paisit Khanarsa: Institute of Field Robotics, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Kittisak Chumpong: Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, Thailand

JRFM, 2025, vol. 18, issue 11, 1-22

Abstract: Clustering techniques are widely applied to equity markets to uncover sectoral structures and regime shifts, yet most studies rely solely on empirical returns. This paper introduces a novel perspective by using diffusion-based parameters from the Black–Scholes model, namely monthly drift and diffusion, as clustering features. Using SET100 stocks in 2020, we applied k -means clustering and evaluated performances with silhouette scores, the Adjusted Rand Index, Wilcoxon tests, and an out-of-sample portfolio exercise. The results showed that diffusion-based features achieved higher silhouette scores in turbulent months, where they revealed sectoral divergence that log-returns failed to capture. The partition for November 2020 provided clearer sector separation and smaller portfolio losses, demonstrating predictive value beyond in-sample fit. Practically, the findings indicate that diffusion-based parameters can signal early signs of market stress, guide sector rotation decisions during volatile regimes, and enhance portfolio risk management by isolating persistent volatility structures across sectors. Theoretically, this model-based framework bridges equity clustering with stochastic diffusion representations used in derivatives valuation, offering a unified and interpretable tool for data-driven market monitoring.

Keywords: stock clustering; diffusion process; Black–Scholes model; log-return; silhouette coefficient; adjusted rand index; SET100 index (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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