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AI Revolution and Crash Risks in Technology Stocks

Author

Listed:
  • Onur Polat

    (Institute of Informatics, Hacettepe University, Ankara, Turkiye)

  • Oguzhan Cepni

    (Department of Economics, Copenhagen Business School, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

This study extends the literature on the impact of technological shocks on stock market dynamics from a novel perspective in the context of the emerging AI revolution. Utilizing the recently developed AI indexes that capture general public attention towards AI-related developments through the newspaper coverage frequency of artificial intelligence and related topics like machine learning and high-frequency (5-minute interval intraday) data on technology stocks over the period from January 2015 to March 2026, we examine the predictive effect of AI sentiment and uncertainty proxies on crash risks in technology stocks that are directly associated with the emerging AI boom. Employing nonparametric causality-in-quantiles tests, we find that all three AI-related indexes significantly predict future crash risk in technology stocks, primarily during ``normal" and high-risk market states. Sign analysis via average derivatives reveals that general and economic AI shocks positively impact crash risk, while explicit uncertainty exhibits state-dependent characteristics at higher quantiles. These findings suggest that AI uncertainty acts as a behavioural amplifier of market tail risk, driven by investor attention and ``fear of missing out" (FOMO) dynamics.

Suggested Citation

  • Onur Polat & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2026. "AI Revolution and Crash Risks in Technology Stocks," Working Papers 202617, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202617
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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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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