Agency Problems In Special Purpose Acquisition Companies
Bibliographic record
Abstract
Special Purpose Acquisition Companies (SPACs) are publicly held\nshell companies with no operations, formed with the sole purpose\nof acquiring a single private company. With a sample of 342 SPAC\nmergers between July 2016 and February 2022, we find that the\naverage 3-month buy-and-hold abnormal return (BHAR) is -16.7\npercent, and only a quarter of SPAC’s produce positive returns. We\nattribute these results to agency problems such as conflicting interests\nbetween the participants and adverse selection problems for\nthe target shareholders. By analyzing SPAC data with the state-ofthe-\nart tabular machine learning algorithm, XGBoost, we identify\npreviously undiscussed features that can help investors predict the\nperformance of SPACs and understand the conflicting interest. Our\nresults may indicate that outside investors are unaware of the true\ndeterminants of SPAC performance. Furthermore, our evidence\nsuggests that the SPAC structure leads to optimistic valuations of\ntarget companies and primarily benefits the parties that sell or redeem\ntheir shares before the merger. We suggest new regulations\nthat align the interest of the parties involved in the SPAC transaction\nwith the publicly traded target firm.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".