Artificial Intelligence and Investor Psychology: Evidence from Retail Markets

Abstract

Artificial Intelligence (AI) has significantly reshaped the landscape of the financial services sector by enabling advanced investment platforms that offer automated portfolio management, customized financial advice, and continuous market monitoring. These AI-driven platforms have become increasingly popular among retail investors as they simplify complex investment processes, lower operational costs, and enhance decision-making efficiency. Despite these technological benefits, investors often remain influenced by psychological biases that affect their judgment and overall portfolio outcomes. This study aims to examine how AI-enabled investment platforms impact the quality of investment decisions made by retail investors, while also analyzing the role of behavioral biases such as overconfidence, herd behavior, anchoring effect, and loss aversion. Additionally, the study considers investor trust as a key mediating factor linking the adoption of AI platforms with improved decision-making quality. The research framework is based on the integration of the Technology Acceptance Model (TAM), Behavioral Finance principles, and Trust Theory to better understand investor behavior in a technology-driven environment. Data collection is proposed through a structured questionnaire using established measurement scales targeting retail investors. For analysis, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4 software. The measurement model focuses on assessing reliability and validity through indicators such as Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE), along with discriminant validity tests like the Fornell–Larcker criterion and HTMT ratio. The structural model evaluates relationships using path coefficients, coefficient of determination (R²), predictive relevance (Q²), effect size (f²), and bootstrapping techniques.