The relationship between asset and capital structure: a compositional approach with panel vector autoregressive models

Miquel Carreras-Simó,Germà Coenders

doi:10.3934/qfe.2021025

Abstract

<abstract> <p>The companies' investment and financing policies are dynamically interrelated and there is no general consensus about the direction of this relationship. There are theoretical arguments and empirical evidence supporting both possible directions, which makes panel vector autoregressive models an appropriate tool. However, the financial ratios normally used to assess this relationship empirically tend to be asymmetric, and to have extreme outliers and non-linear relationships. The aim of this article is to propose a methodological approach to address these issues by complementing panel vector autoregressive models with compositional data analysis. The usefulness of the proposed methodology is illustrated with real data of Spanish retail companies, while a reanalysis with standard financial ratios is inconclusive.</p> </abstract>

Highlights

The study of the relationship between asset structure and capital structure has been carried out within the scope of research on theories of capital structure (Frank and Goyal, 2003; Harris and Raviv, 1991; or Kumar et al, 2017)
Distributional problems are invariably reported in financial ratios, which are used as variables to study this relationship, including skewness, non-linearity and outliers (Linares-Mustarós et al, 2018)
In this article we put forward a method aimed at solving both problems based by combining compositional data (CoDa) methods with panel Vector autoregressive (VAR) models

Summary

Introduction

The study of the relationship between asset structure (investment structure) and capital structure (financial structure) has been carried out within the scope of research on theories of capital structure (Frank and Goyal, 2003; Harris and Raviv, 1991; or Kumar et al, 2017). This type of study faces several major challenges. The relationship is dynamic and potentially bidirectional, requiring panel models in which both variables are potentially dependent, such as panel vector autoregressive (panel VAR) models. In this article we put forward a method aimed at solving both problems based by combining compositional data (CoDa) methods with panel VAR models

Objectives

Methods

Results

Conclusion