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Deregulation and the Predictability of U.S. Stock Market Bubbles: State-Dependent Evidence from Multivariate Quantile-on-Quantile Approach

Author

Listed:
  • Khder Alakkari

    (Department of Statistics and Programming, Faculty of Economics, Latakia University, P.O. Box 2230, Syria; Faculty of Dentistry, Al Andalus University for Medical Sciences, Tartus, Syria; Department of Financial and Banking Sciences, Faculty of Economics, Tartous University, Syria)

  • Bushra Ali

    (Department of Financial and Banking Sciences, Faculty of Economics, Tartous University, Syria)

  • Mariem Brahim

    (Paris School of Business, Paris 75005, France)

  • Rangan Gupta

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

Abstract

In this paper, it is analyzed whether deregulation can be used as a positive or negative state dependent predictor of U.S. stock market bubbles. We use monthly data between February 1973 and May 2025 to calculate the dynamics of these bubbles using 6 Multi-Scale Log-Periodic Power Law Singularity Confidence Indicators, separated by positive and negative bubbles in short-, medium- and long-term horizons. We then use a multivariate quantile-on-quantile regression model to analyse the joint changing nature of the prediction power of deregulation as the distribution of deregulation intensity and the distribution of bubble confidence are controlled for macroeconomic activity and monetary conditions. The results show the best and most consistent results for medium and long horizon positive bubbles. The long-horizon positive bubble confidence is directly associated with deregulation with larger effects in the upper tail of the bubble distribution. Regulation produces broadly opposite patterns, while proposed deregulation displays considerably stronger predictive content than enacted deregulation, supporting an expectations-based regulatory channel. The findings are robust to alternative controls and bandwidth choices. International evidence for the remaining G7 and BRICS markets is more heterogeneous, reinforcing the U.S.-centered nature of the main results. Overall, deregulation contains state-dependent information about the evolution of equity-market imbalances and may complement conventional indicators used to monitor emerging risks to financial stability.

Suggested Citation

  • Khder Alakkari & Bushra Ali & Mariem Brahim & Rangan Gupta, 2026. "Deregulation and the Predictability of U.S. Stock Market Bubbles: State-Dependent Evidence from Multivariate Quantile-on-Quantile Approach," Working Papers 202627, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202627
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    References listed on IDEAS

    as
    1. Bouri, Elie & Can, Ufuk & Cepni, Oguzhan & Gupta, Rangan, 2026. "Oil price shocks and stock market bubble-risk indicators," The Quarterly Review of Economics and Finance, Elsevier, vol. 108(C).
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    3. Khder Alakkari, 2026. "Geopolitical risk and gold return predictability across quantile states: quantile on quantile regression with block bootstrap and scenario forecasts," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 53(3), pages 837-866, August.
    4. Krippner, Leo, 2013. "Measuring the stance of monetary policy in zero lower bound environments," Economics Letters, Elsevier, vol. 118(1), pages 135-138.
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
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    Keywords

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    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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