The Distributional Properties of the Debt to Equity Ratio: Some Implications for Empirical Research

Ali Ataullah · Andrew W. Higson · Mark J. Tippett

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Corporate Finance and Governance Financial Analysis and Corporate Governance Financial Reporting and Valuation Research

Abstract

The purpose here is to assess empirically the quasi‐supply side model of the firm developed in the paper by Ashton et al. (2004 ) by testing the prediction of the model that the evolution of a firm's debt to equity ratio will be compatible with a non‐linear (target adjustment) process whose underlying probability density function possesses no convergent moments. Using a thirty‐two‐year history of the debt to equity ratio for each of ninety ‘mature’ United Kingdom firms, a non‐parametric estimation procedure shows that the debt to equity ratio evolves in terms of a process which is largely consistent with the predictions of this model. In particular, the evolution of the debt to equity ratio is compatible with a ‘long (fat) tailed’ density function with no convergent moments. This has the important implication, supported by our empirical analysis, that the linear dynamic models which characterize empirical work in this area will be mis‐specified and will return inconsistent and temporally unstable estimates of the target adjustment process as a consequence.

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1979Distribution of the Estimators for Autoregressive Time Series With a Unit Root 1984Corporate financing and investment decisions when firms have information that investors do not have 1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1958The Cost of Capital, Corporation Finance and the Theory of Investment 2008The cost of capital, corporation finance and the theory of investment 1963CORPORATE INCOME TAXES AND THE COST OF CAPITAL: A CORRECTION 2001Capital Structures in Developing Countries 1999Testing static tradeoff against pecking order models of capital structure1This paper has benefited from comments by seminar participants at Boston College, Boston Unsiversity, Dartmouth College, Massachusetts Institute of Technology, University of Massachusetts, Ohio State University, University of California at Los Angeles and the NBER, especially Eugene Fama and Robert Gertner. The usual disclaimers apply. Funding from MIT and the Tuck School at Dartmouth College is gratefuly acknowledged. We also thank two reviewers, Richard S. Ruback and Clifford W. Smith, Jr., for helpful comments.1

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