Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Analysing capital structure of spanish chemical companies using self-organizing maps

Analysing capital structure of spanish chemical companies using self-organizing maps PurposeThis paper aims to analyses the capital structure of the Spanish chemical industry during the period between 1999 and 2013, with a twofold objective. First, to determine whether the assumptions of pecking order theory are fulfilled throughout the study's timeframe. Second, by using data covering the years before the crisis and the worst years thereof, this study shows how the crisis has affected the capital structure of the companies included in this sample.Design/methodology/approachA particular kind of unsupervised neural network, self-organizing maps, is applied. This methodology allows to cluster firms avoiding the need to establish relationships between the different variables involved in the problem beforehand.FindingsCompanies are clustered into groups with different degrees of accomplishment of the pecking order theory. The hypothesis about risk is the one that experience a greater variation in the period before and after the crisis. Moreover, companies' capital structure has been lightly disrupted by the crisis.Originality/valueThe originality of this paper lies in applying an unprecedented methodology to the problem of capital structure. Therefore, the capital structure problem can be approached without setting any function relationship previously. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

Analysing capital structure of spanish chemical companies using self-organizing maps

Loading next page...
 
/lp/emerald-publishing/analysing-capital-structure-of-spanish-chemical-companies-using-self-anb1EBNe4m
Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0368-492X
DOI
10.1108/K-05-2016-0112
Publisher site
See Article on Publisher Site

Abstract

PurposeThis paper aims to analyses the capital structure of the Spanish chemical industry during the period between 1999 and 2013, with a twofold objective. First, to determine whether the assumptions of pecking order theory are fulfilled throughout the study's timeframe. Second, by using data covering the years before the crisis and the worst years thereof, this study shows how the crisis has affected the capital structure of the companies included in this sample.Design/methodology/approachA particular kind of unsupervised neural network, self-organizing maps, is applied. This methodology allows to cluster firms avoiding the need to establish relationships between the different variables involved in the problem beforehand.FindingsCompanies are clustered into groups with different degrees of accomplishment of the pecking order theory. The hypothesis about risk is the one that experience a greater variation in the period before and after the crisis. Moreover, companies' capital structure has been lightly disrupted by the crisis.Originality/valueThe originality of this paper lies in applying an unprecedented methodology to the problem of capital structure. Therefore, the capital structure problem can be approached without setting any function relationship previously.

Journal

KybernetesEmerald Publishing

Published: Jun 5, 2017

References