The Determinants Of Capital Structure: Some UK Evidence

Bennett, Mark W.; Donnelly, Ray

doi:10.1006/bare.1993.1005

Cited by 98 publications

(97 citation statements)

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“…In the light of past researches (Bennett and Donnelly (1993), Rajan and Zingales (1995), Ozkan (2001), Lööf (2004), Gaud et al (2005) and Ramlall (2009) …”

Section: Dependent Variablementioning

confidence: 96%

“…According to past studies done by Ozkan (2001), Wald (1999) and Bennett and Donnelly (1993), evidences have been found that there is negative relationship between non-debt tax shield (NDTS) and leverage. To examine this relationship, the proportion of annual depreciation to total assets is used as a proxy for NDTS.…”

Section: Non-debt Tax Shieldmentioning

confidence: 99%

“…Model C is replication of Bevan and Danbolt (2004) and Bennett and Donnelly (1993) with some adjustments. These adjustments are including the data for both independent variable and dependent variables are averaged to isolate distortions as a result of shortterm variations and to reduce possible reverse causality between the independent and dependant variables.…”

Section: Model Cmentioning

confidence: 99%

“…These adjustments are including the data for both independent variable and dependent variables are averaged to isolate distortions as a result of shortterm variations and to reduce possible reverse causality between the independent and dependant variables. As an application of Bevan and Danbolt (2004) and Bennett and Donnelly (1993) The leverage proxies are calculated at both market value and book value. Total debt ratio at book value (TDBV), long-time debt ratio at book value (LDBV) and short-term debt ratio at book value (SDBV), total debt ratio at market value (TDMV), long-time debt ratio at market value (LDBV) and short-term debt ratio at market value (SDMV) are taken as dependant variables for Model D.…”

Section: Model Cmentioning

confidence: 99%

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How Do Firm Characteristics Affect Capital Structure? Some UK Evidence

Akdal

2011

SSRN Journal

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This study aims to determine the influence of various firm level characteristics such as, profitability, size, growth opportunities, asset tangibility, non-debt tax shield, volatility and liquidity on capital structure. Employing the cross-sectional data methodology, the researcher examines the capital structure determinants of 202 companies from FTSE 250 for the time period of 2002 -2009. Seven variables multiple regression models are used to estimate the influence of firm level attributes on capital structure and capital structure is measured simultaneously by the ratios of total debt, long-term debt and short-term debt at both book value and market value of equity. The results obtained from four different regression models show that profitability and liquidity are negatively and significantly related to leverage. Also asset tangibility has a positive relationship with leverage, which is significant. Moreover the researcher finds that total debt ratio at market value of equity is the most important dependent variable as a proxy of capital structure, followed by long-term debt ratio at market value of equity. 2 DECLARATION I declare that this dissertation is all my own work and the sources of information and material I have used (including the Internet) have been fully identified and properly acknowledged as required. I have kept all materials used in this research, including samples, research data, preliminary analysis, notes and drafts, and I can produce them on request.3 Acknowledgement I would like to show my gratitude to everybody who has helped me throughout the research period.

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“…In the light of past researches (Bennett and Donnelly (1993), Rajan and Zingales (1995), Ozkan (2001), Lööf (2004), Gaud et al (2005) and Ramlall (2009) …”

Section: Dependent Variablementioning

confidence: 96%

Section: Non-debt Tax Shieldmentioning

confidence: 99%

Section: Model Cmentioning

confidence: 99%

Section: Model Cmentioning

confidence: 99%

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How Do Firm Characteristics Affect Capital Structure? Some UK Evidence

Akdal

2011

SSRN Journal

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“…Similarly, the closer the firm gets to bankruptcy, lenders will demand to be compensated more for the risk of default. Some lenders will also demand extra 11 Bennett and Donnelly 1993;Lasfer, 1995;Ozkan, 2001;Panno, 2003. Bunn andYoung, 2003 estimate a basic model in which firms' desired capital structure is systematically related only to the tax shield properties of debt.…”

Section: Data and Empirical Specificationmentioning

confidence: 99%

Non-Linearity in the Determinants of Capital Structure: Evidence from UK Firms

2005

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We develop a model of the firm's maximization programme i n which the firm's capital structure is a non linear function of a vector of costs including asymmetric information costs and is subject to a debt ceiling. Using conditional quantile regression methods, we test for the existence of such a non-linearity in a heterogeneous sample of UK firms and demonstrate that, by exploiting more fully the distribution of leverage, this technique yields new insights into the choice of leverage ratio. Not only is the estimated effect of the explanatory variables different at different quantiles of the distribution, we find evidence that the effect of a variable changes sign between low leveraged and high leveraged firms.

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Determinants of Capital Structure in the Uk Retail Industry: A Comparison of Multiple Regression and Generalized Regression Neural Network

Abdou

Kuzmic

Pointon

et al. 2012

Intell. Sys. Acc. Fin. Mgmt.

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SUMMARY 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.

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The Determinants Of Capital Structure: Some UK Evidence

Cited by 98 publications

References 0 publications

How Do Firm Characteristics Affect Capital Structure? Some UK Evidence

How Do Firm Characteristics Affect Capital Structure? Some UK Evidence

Non-Linearity in the Determinants of Capital Structure: Evidence from UK Firms

Determinants of Capital Structure in the Uk Retail Industry: A Comparison of Multiple Regression and Generalized Regression Neural Network

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