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Corporate restructuring costs : a statistical view of legal and capital markets determinants

Author(s)
Curran, John P., Jr. (John Patrick)
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Alternative title
Statistical view of legal and capital markets determinants
Other Contributors
Sloan School of Management.
Advisor
Roy E. Welsch.
Terms of use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Prior authors have concluded that case-specific determinants, such as the number of professionals involved in a given case or the lead attorney's hourly rate, are the principal determinants of variance in professional fees and expenses in US corporate restructurings. The implication has been that less proximate variables, such as the availability of credit, the cost of capital or coincident economic conditions, are not relevant to predictive models. While we do not question the relevance of case-specific factors we question the practicality of models that rely exclusively on such factors in order to predict future fees and expenses. Our hypothesis is that both case-specific and less proximate variables are relevant to the determination of professional costs in bankruptcy, since we believe that variables belonging to each of these categories can act as frictions to the refinancing process or otherwise inform the court's determination as to the efficacy of refinancing, thereby impacting the complexity and duration of the restructuring process. Our results are less than conclusive, since our data is limited. However, the data that we have gives us reason to believe that the addition of non-case predictors may improve the accuracy of case-specific models; in some instances, these non-case-specific predictors even displace case specific predictors (via a screen for statistical significance). Our analysis indicates that this improvement is more pronounced when the data is sorted for Section 364 financing. Our goal is to complement the on-going analysis of professional fees and expenses so that it might better account for the potential impact of non-case factors and thereby yield a more accurate and practical predictive model.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2010.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references.
 
Date issued
2010
URI
http://hdl.handle.net/1721.1/59135
Department
Sloan School of Management
Publisher
Massachusetts Institute of Technology
Keywords
Sloan School of Management.

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