|Special Purpose Acquisition Companies ( acclaimed as a better alternative to theSPACs) —traditional IPO for taking a company public have been booming since 2020. This thesis—analyzes attributes affecting the post-merger performance of SPACs merging between 2020 and early 2022. Throughout this period, SPACs have massively underperformed their benchmarks, except on the first day of trading.
Multiple Linear Regression were used to investigate the relationship between independent variables and the dependent variables first-day and two-month return. We discover a significant negative relationship between performance and the redemption rates (investors withdrawal). Further, we find that the market favored young, profitable, and non-healthcare companies, which outperformed their peers in the short run. Contrary to our initial beliefs, the performance correlates similarly with the designated attributes –independent of the time horizon of interest. Substantiated by the “rise of retail investors” in 2020, we further reveal that two weeks of lagged “hype” has a significant positive relationship with post-merger initial performance.
Based on the obtained results, we suspected that the redemption rate absorbed the effect of the other predictors. This insight was further evaluated using a variety of machine learning models which concluded two things. First, redemption rates indeed absorb the effects in the OLS models. Second, in an Ordinal Logistic Regression, the other variables were able to predict the redemption rate with an accuracy close to 75%.