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Robo-Advisors: Artificial Intelligence-Driven Services for Retail Investors’ Asset Allocation

Author

Listed:
  • Guido Abate
  • Pierpaolo Ferrari
  • Bianca Pisino

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.

Suggested Citation

  • Guido Abate & Pierpaolo Ferrari & Bianca Pisino, 2026. "Robo-Advisors: Artificial Intelligence-Driven Services for Retail Investors’ Asset Allocation," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 15(1), pages 1-3.
    • Handle: RePEc:spt:fininv:v:15:y:2026:i:1:f:15_1_3
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

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