Human vs. Algorithmic Judgment: A Behavioral Experiment on how Retail Investors Respond to AI-Generated Portfolio Recommendations

Abstract This research examines the quantitative behavioral experiment on retail investors' reactions to the AI-generated recommendations on retail investors' portfolios compared to human-generated recommendations. In a between-subjects experimental design, the participants were randomly divided into two groups, with the same portfolio recommendations presented as either AI-generated or human-generated. This study investigates how the perceived credibility, perceived usefulness, financial literacy, familiarity with AI, and risk-taking propensity influence trust, intent to adopt, and investment decision-making. The results show that perceived usefulness, perceived credibility and financial literacy have a significant positive impact on investors' willingness to use the AI-generated recommendations, while AI familiarity and risk tolerance moderate the investor trust and decision-making process. The study also finds that clear, user-friendly and trustworthy AI systems, alongside better investor education, could be key factors in driving more retail investors to adopt AI-powered investment advice.