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| Behavior-Based Typology: Classifying Investors by Stock Holding Preferences |
| XIONG Xiong, CHEN Ruoxin, MENG Yongqiang, GAO Ya, LIN Shen
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College of Management and Economics/Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University;
School of Economics and Management, Dalian University of Technology |
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Abstract Recognizing, distinguishing, and characterizing the heterogeneous features of micro-level agents constitutes the foundation for advancing micro-level regulatory technologies and constructing a capital market theory grounded in heterogeneous agent behavior. From the practical perspective of financial risk management, the focus of regulatory issues has shifted from “how to regulate” to “the efficiency of regulatory input and output.” Real-time monitoring of all micro-market participants would result in significant waste of computational resources and diminishing marginal returns from regulatory efforts. From the theoretical perspective of financial risk management, the behavioral characteristics of micro-agents, their heterogeneity, and their interactions represent the underlying drivers of systemic risk emergence within the macro-level capital market as a complex system. Meanwhile, advances in information technology have continuously enhanced the acquisition and analytical capacity for massive micro-level data. The joint forces of technological progress and theoretical demand necessitate deeper academic inquiry into micro-level heterogeneity.
Although prior studies have made certain progress, the simple reliance on “intuitive” classification criteria such as wealth or age has left numerous issues unresolved. The most salient problems include: (i) insignificant differences across categories, (ii) failure to identify critical categories, and (iii) the inability to justify the selection of segmentation boundaries. The first two issues substantially diminish the applicability of classification methods in managerial contexts, whereas the subjectivity in boundary selection severely undermines their generalizability for research applications. Consequently, existing studies primarily examine the impact of predefined labels on investor behavioral heterogeneity rather than exploring how labels can be cut to effectively distinguish investor behaviors, leaving a notable research gap.
Building on the premise that investor types are fundamentally shaped by their behavior, this study employs data from 96,072 individual investor accounts spanning 2016 to 2021, along with 109 stock factors, to classify and analyze investors based on their holding preferences. The results reveal that stock price, as a singular characteristic, emerges as the most significant determinant of internal preference divergence among individual investors, thereby distinguishing distinct investor types in terms of their holdings.
Based on the relative importance of preference divergence and the intercorrelation among indicators, this study ultimately focuses on nine firm characteristics. Employing Principal Component Analysis (PCA) for dimension reduction, we summarize the major sources of heterogeneity in investor preferences into two composite dimensions: price-attention factor and shell-value factor, which collectively explain over 50% of the overall heterogeneity across the nine indicators. This outcome not only aligns with existing asset pricing and individual investor studies in the Chinese market but also corroborates the widely cited retail investor adage: “Bet on restructuring (shell value) mid-year and on earnings (low price and low attention) at year-end.”
Subsequently, using each investor's positions along these two composite dimensions, we apply the K-means clustering algorithm to categorize investors and examine the relationship between preference-based clusters and investors' demographic and behavioral characteristics. The empirical findings indicate that investor classification derived from stock-holding preferences is not driven by a single demographic label but rather by a combination of multiple attributes and behavioral patterns. For example, two investor clusters may both display characteristics such as a small portfolio size, older age, longer investment experience, lower education, and a lower turnover ratio, making differentiation based on a single demographic label infeasible. However, segmentation based on holding preferences reveals that investors with a strong shell-value orientation warrant closer regulatory attention compared to relatively mature and conservative investors under a tiered supervision framework.
Based on the findings, three policy recommendations are proposed:
First, enhance tiered supervision and risk warning systems. In light of the challenges posed by financial innovation and digitalization, strengthening the regulatory framework requires robust support from supervisory technology (RegTech). Considering the efficiency constraints in comprehensive micro-level financial regulation, we recommend adopting an approach similar to that presented in this study—identifying core investors based on stock-holding preferences to accurately target risk-asset holders and potential spillover agents, and implementing dynamic, tiered regulation.
Second, deliver investor education tailored to heterogeneity. In a market dominated by retail investors—with over 200 million small and medium-sized participants—we recommend using stock-holding preferences as an entry point for investor education. Through regular review and monitoring, regulators can correct behavioral biases, such as excessive risk-seeking and gambling tendencies.
Third, employ shell-value preference as a quantitative indicator to advance the registration-based system. At present, China is implementing institutional reforms on both the asset and investor fronts. To address the difficulty of quantifying policy feedback on the investor side, we suggest leveraging the technical approach outlined in this study to measure shell-value preferences among different investor groups, thereby providing an empirical basis for tracking policy responses and evaluating the effectiveness of reforms.
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Received: 30 December 2024
Published: 02 December 2025
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