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<p>Understanding the impact of cognitive biases on retail investor performance remains a critical challenge in behavioral finance. This study introduces the Behavioral Performance Attribution framework, a novel methodology for decomposing portfolio returns based on investor biases, providing an alternative to traditional return attribution models such as the Brinson, Hood, and Beebower (BHB) model. Using a large real-world trading dataset, we apply ordinary least squares (OLS) regression to quantify the explanatory power of behavioral biases across different investor groups. The empirical results demonstrate that biases such as Action Bias and Portfolio Concentration Bias significantly correlate with returns, with the Model Explainability Ratio (MER) ranging between 43.44% and 63.54%, confirming the framework’s practical applicability. Notably, we estimate separate models for different investor subgroups, revealing that bias effects vary between investors outperforming and underperforming a benchmark. While excessive trading and portfolio concentration enhance outperformance among extreme outperformers, these same behaviors amplify losses among underperforming investors. Residual analysis highlights systematic over- and underestimation patterns, suggesting the need for further refinements incorporating additional behavioral and market-driven factors. These findings contribute to the growing literature on behavioral return attribution and offer a structured approach for integrating psychological factors into investment performance analysis.</p>

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Behavioral performance attribution of retail investors’ portfolio returns

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月11日

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  • David Gorzon,
  • Rüdiger von Nitzsch

摘要

Understanding the impact of cognitive biases on retail investor performance remains a critical challenge in behavioral finance. This study introduces the Behavioral Performance Attribution framework, a novel methodology for decomposing portfolio returns based on investor biases, providing an alternative to traditional return attribution models such as the Brinson, Hood, and Beebower (BHB) model. Using a large real-world trading dataset, we apply ordinary least squares (OLS) regression to quantify the explanatory power of behavioral biases across different investor groups. The empirical results demonstrate that biases such as Action Bias and Portfolio Concentration Bias significantly correlate with returns, with the Model Explainability Ratio (MER) ranging between 43.44% and 63.54%, confirming the framework’s practical applicability. Notably, we estimate separate models for different investor subgroups, revealing that bias effects vary between investors outperforming and underperforming a benchmark. While excessive trading and portfolio concentration enhance outperformance among extreme outperformers, these same behaviors amplify losses among underperforming investors. Residual analysis highlights systematic over- and underestimation patterns, suggesting the need for further refinements incorporating additional behavioral and market-driven factors. These findings contribute to the growing literature on behavioral return attribution and offer a structured approach for integrating psychological factors into investment performance analysis.