The Fairy Tale Decision of Holding SPAC Shares Past Midnight: A study on the Factors Affecting the Probability of a SPAC Being Good or Bad Prior to Approval Date
Bibliographic record
Abstract
This study further examines a two-portfolio theory by Sousa and Jenkinson 2011, where the two portfolios are separated as “Good” and “Bad” SPAC (Special Purpose Acquisition Company) deals. Our paper utilizes new generational SPAC IPO data for the period between 2011-2018, by analyzing the determinants, firm and market specific, that affect the probability of a deal being “good” or “bad”. We use hand collected data on the trust value and common shares outstanding for each quarter for 75 SPAC IPOs from their 10-Qs located in EDGAR and Fintel, to calculate Trust Value Per Share (TVPS), TVPS is defined as the pro-rata value of each common share outstanding (excluding sponsor shares) divided by the SPACs trust value. We categorize a SPAC deal as “Good” if the market price is greater than the TVPS, and a deal as “Bad” if the market price is below the TVPS, one day before the shareholder approval date, respectively. Our results indicate that 50 SPAC deals were “Good”, and 25 deals were “Bad”. These figures differ from those of Sousa and Jenkinson, which indicates a clear effect of the structural change of SPACs after 2011. Our logistic regression indicates a positive and statistically significant relationship between the dependent variable “Good” with the cumulative return of the SPAC and IPO Size, while a negative relationship was seen with the number of days between IPO and announcement, and Trust Value. We suggest investors redeem their shares prior to approval regardless of being classified as “Good” or “Bad”.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".