The Impact of Robo-Advisors and Risk Tolerance on Retail Investor Portfolio Performance

Abstract

The increasing adoption of financial technology has transformed investment landscapes, with robo-advisors emerging as pivotal tools for retail investors. This study explores the impact of robo-advisors and individual risk tolerance on the performance of retail investor portfolios. By employing a qualitative approach, utilizing literature review and library research methods, this paper investigates how robo-advisors facilitate portfolio diversification, optimize asset allocation, and mitigate biases that often affect traditional investment decisions. The analysis highlights the significance of aligning investment strategies with investors' risk tolerance to enhance long-term portfolio performance. Through comprehensive examination of existing academic and industry literature, the study identifies key benefits of robo-advisors, such as cost efficiency, accessibility, and data-driven insights, while also addressing potential limitations, including algorithmic rigidity and lack of personalized advisory services. Findings suggest that retail investors with varying degrees of risk tolerance can significantly improve portfolio outcomes by leveraging robo-advisors, provided they maintain a balanced approach to automated and manual investment strategies. The study concludes that the synergy between robo-advisors and personalized risk assessment serves as a crucial factor in driving optimal portfolio performance for retail investors. Future research may further investigate the dynamic relationship between evolving AI-based advisory platforms and behavioral finance principles.