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Abstract
This study examines the performance of robo-advisors within the
broader digital transformation of financial services. Robo-advisors automate
portfolio construction and maintenance through algorithmic frameworks that
apply established investment principles and low-cost ETFs, thereby extending
professional investment management to individuals lacking the time, resources,
or expertise traditionally required. Focusing on Wealthfront’s Classic
Portfolio from 2013 to 2023, the analysis evaluates absolute and risk-adjusted
returns, volatility and drawdown dynamics, and factor exposures to distinguish
systematic risks from potential investment skill. Results show that passive
indexing outperformed all examined robo-advisor portfolios on both absolute and
risk-adjusted bases during a decade dominated by strong U.S. equity
performance. Although robo-advisors successfully delivered calibrated risk
exposure, their diversified multi-asset allocations incurred notable
opportunity costs in a growth-driven market. The platforms offer the greatest
value to conservative investors, while more aggressive investors may pay
advisory fees without receiving proportional benefits.
JEL classification numbers: G11, G51.
Keywords: Robo-advisor, Artificial intelligence, Performance
evaluation, Risk-adjusted performance.