TITLE:
Robo-Advisor Performance and the Limits of Performance-Based Adoption Explanations
AUTHORS:
Boshuai Wang
KEYWORDS:
Robo-Advisors, Artificial Intelligence, Technology Adoption, Behavioral Finance, Retail Finance
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.11,
November
11,
2026
ABSTRACT: Robo-advisors offer low-cost automated investment services, but their reported investment outcomes do not by themselves explain who uses them. This paper compares ten selected U.S. robo-advisory accounts using a dated Condor Capital Robo Report extract. One-, three-, and five-year trailing results are summarized for total portfolio, equity-sleeve, fixed-income-sleeve, and risk-adjusted returns (Sharpe ratios). The accounts did not show uniformly weak reported outcomes. All ten fixed-income sleeves exceeded the displayed Bloomberg U.S. Aggregate Bond Index reference in the three- and five-year windows, whereas total and equity returns generally fell below the S&P 500 reference. These index comparisons are descriptive: portfolio allocations, risks, tax exposures, and fee conventions are not fully matched. Three-year Sharpe ratios were higher than five-year ratios in the supplied snapshot, but overlapping trailing windows do not establish a temporal decline. Most importantly, the data contain no investor-level adoption observations or direct performance-adoption relationship. The paper therefore does not claim to explain low adoption empirically. Instead, it positions reported performance alongside earlier work on investor trust, digital financial literacy, preferences for human advice, and provider incentives to outline propositions for future testing. The contribution is a bounded performance comparison and a clear separation of observed investment outcomes from conjectured adoption mechanisms.