An Empirical Model of Optimal Capital Structure
Jules H. Van Binsbergen
Stanford University - Graduate School of Business; National Bureau of Economic Research (NBER)
John R. Graham
Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER)
Georgetown University - McDonough School of Business
Journal of Applied Corporate Finance, Vol. 23, Issue 4, pp. 34-59, 2011
The authors provide a reasonably user‐friendly and intuitive model for arriving at a company's optimal, or value‐maximizing, leverage ratio that is based on the estimation of company‐specific cost and benefit functions for debt financing. The benefit functions are downward‐sloping, reflecting the drop in the incremental value of debt with increases in the amount used. The cost functions are upward‐sloping, reflecting the increase in costs associated with increases in leverage. The cost functions vary among companies in ways that reflect differences in corporate characteristics such as size, profitability, dividend policy, book‐to‐market ratio, and asset collateral and redeployability. The authors use these cost and benefit functions to produce an estimate of a company's optimal amount of debt. Just as equilibrium in economics textbooks occurs where supply equals demand, optimal capital structure occurs at the point where the marginal benefit of debt equals the marginal cost. The article illustrates optimal debt choices for companies such as Barnes & Noble, Coca‐Cola, Six Flags, and Performance Food Group. The authors also estimate the net benefit of debt usage (in terms of the increase in firm or enterprise value) for companies that are optimally levered, as well as the net cost of being underleveraged for companies with too little debt, and the cost of overleveraging for companies with too much. One critical insight of the model is that the costs associated with overleveraging appear to be significantly higher, at least for some companies, than the costs of being underleveraged.
Number of Pages in PDF File: 28Accepted Paper Series
Date posted: December 22, 2011
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