Structural behavioral effects on the Moscow Stock Exchange: from industry analysis to cluster verification

Objective: to develop and test a comprehensive methodology for identifying and analyzing behavioral distortions in the modern Russian stock market. The methodology aims to identify structural patterns in investor behavior and assess the impact of industry affiliation, volatility and asset liquidity on them.

Methods: the study is based on a quantitative analysis of daily trading data from the Moscow Stock Exchange (01.10.2023–30.09.2025) for a stratified sample of 34 stocks. To quantify behavioral distortions (the effect of disposition and loss aversion), Odean coefficient was calculated. K-means cluster analysis method followed by PCA visualization was used to identify implicit behavioral patterns and verify the results.

Results: it was established that the Russian market is not dominated by a single behavioral effect, but by a structural polarization: loss aversion prevails in the “protective” sectors (oil and gas, finance), while the disposition effect prevails in the “growth” and speculative sectors (IT, gold mining). A direct relationship between volatility and the strength of the disposition effect was confirmed. The clustering method identified eight stable behavioral profiles that highly accurately correspond to the industry affiliation of securities. This proves the systemic rather than random nature of the identified anomalies.

Scientific novelty: it consists in a comprehensive methodological approach combining the traditional calculation of Odean coefficient with modern machine learning methods (K-means cluster analysis). This allows not only testing hypotheses about the sectoral nature of behavioral distortions, but also identifying intersectoral heterogeneity. For the first time, intra-industry heterogeneity was identified for the financial sector due to differences in asset liquidity.

Practical significance: the proposed approach allows investors to adapt trading strategies to the behavioral profile of a particular sector and asset. The results can be used by regulators to develop measures to protect retail investors, as well as by issuers and brokers to better understand their audience and build effective communications.

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