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Activity Number: 65 - Building a New and Essential Statistics Toolbox for Challenges in Finance and Business Analytics
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #324516
Title: A Statistical Approach to Corporate Debt Structure
Author(s): Kai-Sheng Song *
Companies: Department of Mathematics, University of North Texas
Keywords: Corporate Debt Structure ; Multiple Continuous Proportions ; Implied Zeros

One of the core issues in corporate finance is to understand why firms finance themselves as they do. This issue has become increasingly important because how firms are financed influences their performance and value. Since the 1950s the capital structure literature has addressed this fundamental issue by focusing on a firm's mix of debt and equity. However, firms often use more than one type of debt claim. Furthermore, some firms use certain types of debt claim that others do not use. The financing choices of various forms of debt claim and different amounts of debt issued lead to financial data with multiple continuous proportions and many zeros implied by debt structures. We propose a novel statistical method for addressing such choice-implied statistical issues. Our method is based on choice probability-driven submodels and is empirically implementable even in large dimensions. Its performance is demonstrated by simulations. Its application to the analysis of debt structures of U.S. corporations reveals new insights into the corporate capital structure.

Authors who are presenting talks have a * after their name.

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