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Determinants Of Capital Structure In The Uk Retail Industry: A Comparison Of Multiple Regression And Generalized Regression Neural Network

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  • Hussein A. Abdou
  • Andzelika Kuzmic
  • John Pointon
  • Roger J. Lister

Abstract

Firms need to rely on different financing sources, but the question is how capital structure is determined for a particular industry. Our aim is to undertake an investigation into the factors which determine capital structure in the UK retail industry. Our initial sample consists of 163 (final sample: 100) UK retail companies, using data from 2000 in order to analyse capital structure from 2002 to 2006. Nonlinear models tend to be unduly neglected in capital structure research, and so we apply generalized regression neural networks (GRNNs), which are compared with conventional multiple regressions. We utilize a hold‐out sample for the multiple regressions to make them comparable with the GRNNs. Stability of the data is also confirmed. Our main findings are: net profitability and the depreciation‐to‐sales ratio are key determinants of capital structure based on GRNNs, while two more variables are added in the multiple regressions, namely size and quick ratio; there is strong support for the pecking‐order theory; both root‐mean‐square errors and mean absolute errors are much lower for the GRNNs than those for the multiple regressions for overall, training and testing datasets. The potential benefit of this research to financial managers and investors in the UK retail sector is the identification of the overriding role of net profitability in reducing the financial risk from high levels of gearing. Copyright © 2012 John Wiley & Sons, Ltd.

Suggested Citation

  • Hussein A. Abdou & Andzelika Kuzmic & John Pointon & Roger J. Lister, 2012. "Determinants Of Capital Structure In The Uk Retail Industry: A Comparison Of Multiple Regression And Generalized Regression Neural Network," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(3), pages 151-169, July.
    • Handle: RePEc:wly:isacfm:v:19:y:2012:i:3:p:151-169
    DOI: 10.1002/isaf.1330
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    2. Daniel E. O'Leary, 2015. "The 50 Most Downloaded Papers in Intelligent Systems in Accounting, Finance and Management in 2014," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(4), pages 283-288, October.

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