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Persistence in Stock Returns: Robotics and AI ETFs Versus Other Assets

Author

Listed:
  • Fekria Belhouichet
  • Guglielmo Maria Caporale
  • Luis Alberiko Gil-Alana

Abstract

This paper examines the long-memory properties of returns of exchange-traded funds (ETFs) specializing in robotics and artificial intelligence (AI) listed on the US market, as well as those of other assets such as the WTI crude oil price (West Texas Intermediate), Bitcoin, the S&P 500 index, 10-year US Treasury bonds, and the VIX volatility index. The frequency is daily and the sample period goes from 1 January 2023 to 23 June 2025. The adopted fractional integration framework is more general and flexible than those previously used in related studies, and sheds light on the degree of persistence of returns. The evidence suggests that all returns series examined are highly persistent, regardless of the error structure assumed, and that in general a linear model is appropriate to capture their evolution over time. The implications are that that the newly developed assets do not offer to investors additional hedging and diversification opportunities compared to more traditional ones, and that the creation of these additional financial instruments does not pose fresh challenges to policy makers tasked with financial stability.

Suggested Citation

  • Fekria Belhouichet & Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2025. "Persistence in Stock Returns: Robotics and AI ETFs Versus Other Assets," CESifo Working Paper Series 12171, CESifo.
    • Handle: RePEc:ces:ceswps:_12171
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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