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

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.