The Value of Capital Market Regulation: IPOs Versus Reverse Mergers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We analyze the economic consequences of disclosure and regulation within a context of significant information asymmetry and lenient regulation. In Canada, firms can enter the stock market at a prerevenue stage by fulfilling each of the requirements of an initial public offering or using reverse mergers. This backdoor listing method implies a smoother oversight by the securities commission and a shorter process based on private placements. Controlling for several dimensions, including self‐selection, we find that the choice of the listing method and regulation strictness significantly influence the value and long‐run performance of newly listed firms. These results are consistent with theories suggesting that a commitment by a firm to a stricter regulatory oversight lowers the information asymmetry component of the cost of capital, reducing the heterogeneity of expectations and mispricing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it