How Does Law Affect Finance? An Examination of Financial Tunneling in an Emerging Market
Bibliographic record
Abstract
We establish that one channel through which law affects financial markets is by control of financial tunneling.We first develop a model of how legal rules affect two common forms of financial tunneling: dilutive equity offerings and below-market freezeouts, and how these forms affect equity valuations.We then report evidence from Bulgaria, which goes through mass privatization in 1998, followed by extensive post-privatization tunneling.In 2002, Bulgaria adopts securities law changes which rescue a collapsing market by limiting both forms of tunneling, and provide a natural experiment which allows us to test the model predictions.Following the legal changes, minority shareholders participate equally in secondary equity offers, where before they rarely participated and suffered severe dilution; and freezeout prices quadruple (measured as offer price/sales).After the law is adopted, valuation measures (price/earnings, price/sales, and Tobin's q) more than double for firms at high risk of tunneling, relative to lower risk firms.We thus present evidence from an emerging market on (i) the importance of legal rules that limit financial tunneling, and (ii) the importance of financial tunneling risk as a factor in determining equity prices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".