Modeling short-term post-offering price-volume relationships using Bayesian change-point panel quantile regression

Abstract

We propose a special panel quantile regression model with multiple stochastic change-points to analyze latent structural breaks in the short-term post-offering price-volume relationships in China's growth enterprise market where the piecewise quantile equations are defined by change point indication functions. We also develop a new Bayesian inference and Markov chain Monte Carlo simulation approach to estimate the parameters, including the locations of change points, and put forth simulation-based posterior Bayesian factor tests to find the best number of change points. Our empirical evidence suggests that the single change point effect is significant on quantile-based price-volume relationships in China's growth enterprise market. The lagged initial public offering (IPO) return and the IPO volume rate of change have positive impacts on the current IPO return before and after the change point. Along with investors' gradually declining hot sentiment toward a new IPO, the market index volume rate of change induces the abnormal short-term post-offering IPO return to move back to the equilibrium.