How Does Law Affect Finance? An Examination of Financial Tunneling in an Emerging Market
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,008 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».