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Application of Generative Al in Investment Information Analysis: Effects on Investment Decision Quality and Investor Confidence
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Abstract
En 中文
This study aims to examine the impact of generative Al investment information on investment decision quality and investor confidence, while also testing the mediating role of decision quality in this process. With the rapid development of artificial intelligence in the financial sector, generative AI tools can provide real-time, structured, and personalized investment information, potentially influencing investors' decision-making behavior and psychological confidence. However, empirical research on how AI information improves investment decision quality and subsequently affects investor confidence remains limited. Focusing on investors as the research population, this study collected data through a questionnaire survey. The empirical results indicate that generative AI investment information has a significant positive effect on investment decision quality. In turn, decision quality significantly positively affects investor confidence. At the same time, generative AI investment information also exerts a direct impact on investor confidence and produces an indirect effect through decision quality, highlighting the key mediating role of decision quality between AI information and confidence. Overall, the model demonstrates good fit, and the research hypotheses are supported. Based on the fmdings, this study provides practical recommendations. Overall, the research not only confirms the value of generative AI investment information in behavioral finance but also offers operationalized scales and application guidelines, contributing significant theoretical and practical insights for fmtech development, improved investment decision-making, and the construction of investor psychological confidence.
Keywords:
Generative Al
Investment Information
Investment Decision Quality
Investor Confidence
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