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

Fekria Belhouichet, Guglielmo Maria Caporale and Luis Alberiko Gil-Alana

No 12171, CESifo Working Paper Series from CESifo

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.

Keywords: persistence; fractional integration; long memory; trends; robotics ETFs; AI ETFs (search for similar items in EconPapers)
JEL-codes: C22 G11 G12 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_12171

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