Robo-Advisors and Investor Behavior: An Empirical Examination of Trust, Adoption, And Portfolio Outcomes

Abstract

Robo-advisors (RAs) are a new and powerful force in the field of wealth management. They promise to save money, be fair, and be easy to use. However, psychological factors, including as trust and algorithm aversion, persist in shaping investor behaviour on these digital platforms. This research empirically investigates the precursors of trust in robo-advisors, their influence on adoption decisions, and the resultant effects of robo-advisor utilization on portfolio performance. Using a mixed-methods approach, we first provide surveys and an experimental vignette study to retail investors (N≈1,000) to find out how transparency, human-in-the-loop models, and financial literacy affect trust and the desire to adopt. We augment this work by using panel data from a cohort of RA adopters and their non-adopters, applying a difference-in-differences methodology to assess variations in diversification, turnover, and risk-adjusted performance. We anticipate that our findings will demonstrate that increased transparency and hybrid advisory models substantially bolster trust, hence facilitating adoption. Also, it is suggested that using RA would make portfolios more diverse and cut down on too much trading, which would contribute to small improvements in risk-adjusted returns. This study enhances the literature on behavioural finance and financial technology by correlating psychological factors influencing adoption with tangible portfolio outcomes, providing valuable insights for investors, regulators, and financial service providers.