Overconfidence vs. Herding: Analyzing Investor Behavior in the Moroccan Retail Trade Sector

This study looks at overconfidence and herding in the Moroccan stock market, focusing on the retail trade sector. We use daily data from four major stocks; Autohall, Ennakl, Label Vie, and Total Maroc, covering March 2015 to January 2025. Investor behavior is analyzed with regression and rolling-window methods. Overconfidence is tested by seeing how volatility and past returns affect trading volume, while herding is measured using CSAD metrics for both trading volume and returns. The results provide strong evidence of overconfidence, particularly in Label Vie and Total Maroc, where volatility coefficients are significantly positive (β = 1.52e+06 and β = 6.17e+05, respectively). These findings confirm that higher volatility encourages excessive trading. Rolling regressions further show that overconfidence is time-varying and becomes more pronounced during volatile market conditions. By contrast, herding behavior appears largely absent. The squared market volume coefficient (β₂ = -1.512e-12, p = 0.747) and squared return coefficient (β₂ = 4.46, p < 0.01) indicate that dispersion increases rather than contracts during periods of elevated market activity, contradicting the expectations of herding models. Time-varying t-statistics also remain consistently above -2, confirming the lack of persistent herding across the sample period. Overall, the findings show that Moroccan retail investors are more influenced by their own confidence than by following the crowd.

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Publication Details

Journal
RMd Economics Management & Social Sciences
Published
2025-11-01
DOI
https://doi.org/10.23882/emss25217
Primary Topic
Financial Markets and Investment Strategies
Type
article
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article

Overconfidence vs. Herding: Analyzing Investor Behavior in the Moroccan Retail Trade Sector

Adda Benslimane, Faouzi BOUSSEDRA, Júlio Lob�ão, Mohamed Amine Chafik
RMd Economics Management & Social Sciences
Financial Markets and Investment Strategies
article

Overconfidence vs. Herding: Analyzing Investor Behavior in the Moroccan Retail Trade Sector

Adda Benslimane, Faouzi BOUSSEDRA, Júlio Lob�ão, Mohamed Amine Chafik
article en

Abstract

This study looks at overconfidence and herding in the Moroccan stock market, focusing on the retail trade sector. We use daily data from four major stocks; Autohall, Ennakl, Label Vie, and Total Maroc, covering March 2015 to January 2025. Investor behavior is analyzed with regression and rolling-window methods. Overconfidence is tested by seeing how volatility and past returns affect trading volume, while herding is measured using CSAD metrics for both trading volume and returns. The results provide strong evidence of overconfidence, particularly in Label Vie and Total Maroc, where volatility coefficients are significantly positive (β = 1.52e+06 and β = 6.17e+05, respectively). These findings confirm that higher volatility encourages excessive trading. Rolling regressions further show that overconfidence is time-varying and becomes more pronounced during volatile market conditions. By contrast, herding behavior appears largely absent. The squared market volume coefficient (β₂ = -1.512e-12, p = 0.747) and squared return coefficient (β₂ = 4.46, p < 0.01) indicate that dispersion increases rather than contracts during periods of elevated market activity, contradicting the expectations of herding models. Time-varying t-statistics also remain consistently above -2, confirming the lack of persistent herding across the sample period. Overall, the findings show that Moroccan retail investors are more influenced by their own confidence than by following the crowd.

RMd Economics Management & Social SciencesVol. 2(3)
Universidade do Porto (PT), Université Paul-Valéry Montpellier (FR), Chouaib Doukkali University (MA)
Openalex Percentile: Top 57%
Financial Markets and Investment Strategies
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Overconfidence vs. Herding: Analyzing Investor Behavior in the Moroccan Retail Trade Sector — Adda Benslimane, Faouzi BOUSSEDRA, et al. · RMd Economics Management & Social Sciences (2025) | TGRS Research Map | TGRS