How Does Law Affect Finance? An Examination of Financial Tunneling in an Emerging Market
Bibliographic record
Abstract
How Does Law Affect Finance? An Examination of Financial Tunneling in an Emerging Market Vladimir Atanasov*, College of William and Mary Bernard Black, University of Texas at Austin Conrad S. Ciccotello, Georgia State University Stanley B. Gyoshev, Exeter University Current Draft: September 2007 University of Texas, McCombs School of Business, Research Paper No. FIN-04-06 University of Texas Law, Law and Economics Research Paper No. 80 European Corporate Governance Institute, Finance Working Paper No. 123/2006 Available on SSRN at: http://ssrn.com/abstract=902766 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. * Corresponding author: Mason School of Business, College of William and Mary, P.O. Box 8795, Williamsburg, VA 23187, vladimir.atanasov@mason.wm.edu, voice: 757-221-2954, fax: 7575-221-2937 We would like to thank Chris Muscarella, Clifford Holderness, Erik Berglof, Mike Burkart, Petko Dimitrov, John Edmunds, Vladimir Gatchev, Mariassunta Gianetti, Martin Grace, Greg Hebb, Mark Hershey, Laurie Krigman, Wendy Liu, Marina Martynova, David Mauer, Enrico Perotti, Jose Luis Peydro-Alcalde, Dimana Rankova, Andrei Shleifer, James Smith, David Stolin, Aris Stouraitis, Per Stromberg, Ajay Subramanian, and seminar participants at the Fourth Asian Corporate Governance Conference, the American Law and Economics Association Annual Meeting (2006), the Canadian Law and Economics Association Annual Meeting (2006), the European Finance Association Annual Meeting (2007), Financial System Modernization Conference organized by the European Central Bank, the Bulgarian Financial Supervision Commission, College of William and Mary, Georgia State University, Southern Methodist University, Stockholm School of Economics, the University of Amsterdam, the New Economic School (Moscow, Russia) and the University of Kansas for helpful comments. We are indebted to Apostol Apostolov, Roumen Nikolov, Vassil Golemanski, and Kamen Dikov for kindly providing the firm ownership, earnings, and Bulgarian Stock Exchange trade data. This project was funded in part through Grant Number S-LMAQM-00-H-0146 provided by the United State Department of State and administered by the William Davidson Institute. The opinions, findings, conclusions, and recommendations expressed herein are those of the authors and do not necessarily reflect those of the Department of State or the William Davidson Institute.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".