Picking Winners: Identifying Features of High-Performing Special Purpose Acquisition Companies (SPACs) with Machine Learning

Caleb J. Williams ()
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Caleb J. Williams: Data Science Division, Quilty Analytics, 33 6th St. S, Suite 204, St. Petersburg, FL 33701, USA

JRFM, 2023, vol. 16, issue 4, 1-21

Abstract: Special Purpose Acquisition Companies (SPACs) are publicly listed “blank check” firms with a sole purpose: to merge with a private company and take it public. Selecting a target to take public via SPACs is a complex affair led by SPAC sponsors who seek to deliver investor value by effectively “picking winners” from the private sector. A key question for all sponsors is what they should be searching for. This paper aims to identify the characteristics of SPACs and their target companies that are relevant to market performance at sponsor lock-up windows. To achieve this goal, the study breaks market performance into a binary classification problem and uses a machine learning approach comprised of decision trees, logistic regression, and LASSO regression to identify features that exhibit a distinct relationship with market performance. The obtained results demonstrate that corporate or private equity backing in target firms greatly improves the odds of market outperformance one-year post-merger. This finding is novel in indicating that characteristics of target firms may also be deterministic of SPAC performance, in addition to SPACs, transaction, and the market features identified in the prior literature. It further suggests that a viable sponsor strategy could be constructed for generating outsized market returns at share lock-up windows by simply “following the money” and choosing target firms with prior involvement from corporate or private equity investors.

Keywords: Special Purpose Acquisition Company (SPAC); private equity; venture capital; Initial Public Offering (IPO); feature engineering; machine learning; artificial intelligence; classification and regression tree; logistic regression; LASSO regression; out-of-sample performance; econometrics; predictive analytics (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
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