Performance analysis of healthcare-focused special purpose acquisition companies

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The Covid-19 pandemic has accelerated some structural changes in the healthcare industry, and several health-tech start-ups thrived by providing innovative solutions to the challenges imposed by the pandemic. To finance their growth, many of these companies went through mergers with Special Purpose Acquisition Companies (SPACs). The paper investigates the market performance of healthcare-focused US-listed SPACs. The study aims to analyze the returns that healthcare SPACs offer to their investors and ascertain the determinants that drive these returns over a sample of 33 SPACs that merged with a healthcare firm between 2018 and 2021. Linear regression is employed to identify the drivers of SPACs’ market performance. Portfolio analysis is also performed and compared against the Russell 2000 and the S&P500 Healthcare Indexes.
The first outcome accomplished by the analysis is that a portfolio made of healthcare-SPACs underperforms small-cap firms by 2.14% and the healthcare industry by 6.72% over a two-year period, even if the difference in the returns of the healthcare SPACs portfolio and the two benchmarks is not statistically significant. Moreover, a high level of redemptions, the presence of serial SPAC sponsors, cross-border deals, private equity and venture capital funds as sellers, and a high percentage of boutique investment banks among the sell-side advisors seem to negatively affect the returns of healthcare-focused SPACs with a significance level of at least 10%. Instead, a larger number of buy-side advisors appears to be beneficial for healthcare-focused SPACs’ market performance.

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    • Figure 1. Healthcare SPAC portfolio performance versus benchmarks
    • Table 1. Overview of SPAC characteristics
    • Table 2. Healthcare SPAC portfolio returns versus benchmarks
    • Table 3. T-tests outcomes
    • Table 4. Multiple linear regression output
    • Table A1. Regressors’ correlation matrix
    • Table A2. Variance inflation factors to detect multicollinearity
    • Conceptualization
      Gimede Gigante, Daniele Notarnicola
    • Formal Analysis
      Gimede Gigante, Daniele Notarnicola
    • Data curation
      Gimede Gigante, Daniele Notarnicola
    • Funding acquisition
      Gimede Gigante, Daniele Notarnicola
    • Investigation
      Gimede Gigante, Daniele Notarnicola
    • Methodology
      Gimede Gigante, Daniele Notarnicola
    • Project administration
      Gimede Gigante, Daniele Notarnicola
    • Resources
      Gimede Gigante, Daniele Notarnicola
    • Software
      Gimede Gigante, Daniele Notarnicola
    • Supervision
      Gimede Gigante, Daniele Notarnicola
    • Validation
      Gimede Gigante, Daniele Notarnicola
    • Visualization
      Gimede Gigante, Daniele Notarnicola
    • Writing – original draft
      Gimede Gigante, Daniele Notarnicola
    • Writing – review & editing
      Gimede Gigante, Daniele Notarnicola