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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Time-Series and Cross-Sectional Determinants of the Performance of Special Purpose Acquisition Companies |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 64 |
| Date | 2022 |
| Zusammenfassung | This thesis examines the post-merger performance of the special purpose acquisition company (SPAC). The analysis is divided into two parts. In the first part, the relationship between the performance of individual SPACs in their first-year post-merger period and a set of cross-sectional characteristics is examined. The second part investigates a set of time-series predictors and how these relate to the monthly performance of SPACs in general. The objective is to identify cross-sectional predictors of post-merger SPAC performance which can be used by SPAC investors to aid in their redemption decision. To accomplish this, the characteristics used as the predictors need to be observable prior to the redemption deadline and the merger vote. Supplemented with the results from the time-series analysis, the ultimate goal is to develop actionable investment strategies in regards to SPACs that manage to outperform the Russell 2000. The existing scientific literature finds SPACs at large to underperform in the long term. When researching SPACs, the underperformance is observed in virtually every scientific paper on SPAC performance. The research into cross-sectional predictors and determinants is fairly limited. Even more scarce is the research into time-series predictors: It seems only a single paper has included a time-series analysis of SPAC returns up to date. In addition to research on SPACs being rather sparse, some of the variables examined in earlier works are not observable ex ante and therefore, investment strategies based on those predictors are not actionable. SPACs are constantly changing, which is why a number of characteristics examined in the previous literature are outdated at present. Thus, if one wants to develop investment and trading strategies for the fast moving SPAC market, new, up to date research is required. The cross-sectional analysis successfully identifies a set of predictors which can be used to segregate SPACs based on their expected performance. The time-series predictors are uniformly identified to not be related to the abnormal returns of SPACs. Therefore, the investment and trading strategies developed based on the analysis of SPAC returns and presented in chapter seven focus exclusively on the selection of SPACs. The strategies presented in the thesis demonstrate the real possibilities for profit with regard to the SPAC market, though back-testing and calibrating is needed before they can be put into practice. |
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