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How do AI-based tokens react to interest rate shocks? Evidence from quantile regression

Francisco Jareño et al.

Studies in Economics and Finance2026https://doi.org/10.1108/sef-08-2025-0614article
AJG 1ABDC B
Weight
0.50

What the paper says

Purpose This study aims to investigate how artificial intelligence (AI)-based tokens respond to changes in nominal and real interest rates, particularly in different market conditions, and to evaluate their potential role in portfolio diversification. Design/methodology/approach This study uses quantile regression to analyse the effect of changes in the nominal interest rate on the returns of five AI-based tokens. Two models are used to evaluate the performance of the tokens in relation to the S&P 500 and interest rates, taking into account inflation expectations and sub-period analysis. Findings AI-based tokens are sensitive to shocks in the US stock market, particularly during periods of market decline. Negative changes in the real interest rate affect tokens negatively in bear markets, while changes in the nominal rate are more harmful during market peaks. The behaviour of tokens varies across sub-periods. Originality/value This research provides valuable insights into the dynamics of AI-based token markets and how they interact with macroeconomic variables. It offers guidance for investors and portfolio managers.

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https://doi.org/https://doi.org/10.1108/sef-08-2025-0614

Or copy a formatted citation

@article{francisco2026,
  title        = {{How do AI-based tokens react to interest rate shocks? Evidence from quantile regression}},
  author       = {Francisco Jareño et al.},
  journal      = {Studies in Economics and Finance},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/sef-08-2025-0614},
}

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How do AI-based tokens react to interest rate shocks? Evidence from quantile regression

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.