A Hard Look at SPAC Projections
Bibliographic record
Abstract
Firms’ use of special purpose acquisition companies (SPACs) to go public has increased dramatically, leading to market and regulatory debate about their use of projections. Examining SPAC mergers from 2004 through 2021, we find that 80% of firms provide projections for four years ahead on average, with approximately one-quarter of recent projections extending more than five years. For the sample of SPAC mergers with observable postmerger revenue, we find that only 35% of firms meet or beat their projections. This proportion declines for forecasts that are longer horizon, and nonserial SPAC sponsors miss forecasts by greater percentages. When we compare SPAC projected revenue growth with benchmark samples of firms completing an initial public offering (IPO) and matched firms, the SPAC projections are approximately three times larger on average than benchmark firms’ actual revenue growth, with even greater differences for long-term projections. After the merger, firms reduce their use of projections, providing them at statistically similar rates as benchmark firms. Overall, the evidence supports concerns that the SPAC merger includes highly optimistic projections. This paper was accepted by Suraj Srinivasan, accounting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".