Detecting hot and cold cycles using a Markov regime switching model—Evidence from the Chinese A-share IPO market

Abstract

This paper focuses on detecting hot and cold IPO cycles in the Chinese A-share market using a Markov regime switching model. We introduce a set of observations to measure IPO activities, which include numbers of IPOs issued, levels of underpricing, market conditions and duration time from prospectus and listing, and thus establish a model to estimate these activities' average performance in hot and cold periods respectively. It is found that a hot period is related with an abundant supply of IPOs, high levels of underpricing, positive market conditions and short waiting time to listing after prospectus issue. Further, this paper depicts the turning points of hot and cold periods across the period from 1994 to 2005 for each observation. The cycles detected by the number of IPOs per month are the benchmark and then these cycles' robustness is tested by the other observations.