Algorithmic Overconfidence, Financial Literacy, and Decision Quality: Comparative Evidence from Morocco and France

Abstract

Abstract:

The rapid diffusion of algorithmic financial technologies such as robo advisors, automated trading platforms and artificial intelligence applications raises a central question regarding their actual impact on investor rationality. While these tools are often perceived as enhancing objectivity and improving decision making, they may also introduce new behavioral vulnerabilities. This study examines the impact of algorithmic overconfidence, defined as excessive reliance on automated systems driven by an overestimation of their reliability and superiority, on the quality of financial decisions. It also investigates the explanatory and moderating role of financial literacy. A comparative research design is adopted, contrasting Morocco as an emerging market characterized by lower financial literacy and a developing regulatory framework, with France as a developed market marked by higher institutional and digital maturity. The empirical analysis is based on a questionnaire administered to 312 individual investors, including 158 Moroccan and 154 French participants. The relationships are tested using partial least squares structural equation modeling. The findings indicate that algorithmic overconfidence significantly reduces financial decision quality, while financial literacy exerts a positive direct effect and a protective moderating role. More precisely, the positive interaction effect shows that higher levels of financial literacy weaken the negative impact of algorithmic overconfidence on decision quality. The comparative analysis further reveals that the adverse effect of algorithmic overconfidence is more pronounced in Morocco, whereas the protective role of financial literacy is stronger in France. These results demonstrate that technological tools do not eliminate behavioral biases but rather reshape their intensity depending on the institutional context. The study contributes to the behavioral finance literature by integrating the technological dimension into the analysis of cognitive biases and by highlighting the central role of financial literacy as both an explanatory and protective mechanism. From a practical perspective, the findings emphasize the need to strengthen financial education in emerging markets and to promote greater transparency and critical awareness in the use of algorithmic financial systems in developed economies.

Keywords:  Algorithmic Overconfidence; Financial Literacy; Behavioral Finance; Financial Decision Quality; Robo-Advisors; FinTech Adoption; Emerging vs. Developed Markets; Morocco-France Comparison.