Three essays in corporate governance and corporate finance : international evidence

Hernandez Perdomo, Elvis Alexander

May 2017

Thesis or dissertation

© 2017 Elvis Alexander Hernandez Perdomo. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder.

This thesis presents three original research frameworks, two in corporate governance and one in corporate finance, distributed in three empirical chapters, respectively. Specifically, in Chapter 1, a novel multi-criteria decision analysis (MCDA) approach is developed not only to quantify an aggregate quality of corporate governance at firm level, but also to overcome the limitations of the existing measures (i.e., corporate governance indices) mainly with respect to full compensatory structures and industry-wide heterogeneity. Furthermore, the empirical approach, using PROMETHEE methods and econometric analysis of panel data, provides a strong inverse relationship between firm performance and corporate governance quality. The results rely on outranking relationships (over five million pair comparisons) among companies (1,203 US listed firms during
2002 to 2014) across various corporate governance criteria, comparing the aggregate quality against a well-known corporate governance index (ASSET4 ESG in Datastream).

In Chapter 2, the theory of system reliability is used to model the behaviour of companies in terms of their corporate governance practices and mechanisms. Particularly, machine-learning techniques are proposed to assess a corporate governance system. The mapping of its inputs or specific indicators (e.g., corporate social responsibility, average number of board meetings, compensation policy, auditing independency and independent board) as components (either in operating or failed state), along with firm-specific conditions (i.e., age, size, risk, growth), into a reliability system aims to determine an approximate structure function that models the behaviour of the system. The proposed approach is applied to another data sample set of 1,109 US listed companies during 2002 to 2014, the financial and non-financial indicators are modelled as components of the corporate governance system, and returns on assets is defined as the system output. The results show that growth opportunities matter for the proper functioning of the system, and suggest that if companies are more transparent (i.e., components show a low probability of failure) both the trustworthiness of the companies and the system reliability improves.

In Chapter 3, a research framework to analyse failure in mergers and acquisitions (M&A) reveals that not only deal characteristics (i.e., deal attitude, means of payments, deal size, ownership), but also acquirers’ and targets’ firm size, acquirers’ economic freedom, and targets’ accounting returns significantly explain the likelihood of deal failure. To this aim, a large dataset of 137,116 worldwide M&A deals (during 1977–2014 on more than 140 countries) and novel specifications of logit regression models are analysed. This chapter contributes and expands the literature in M&A deals and business research by evaluating how incumbents’ specific information can constrain the firms’ assets movement (efficiency perspective).

Regarding the implications, the findings in Chapter 1 are of particular interest to both scholars and decision makers (e.g., managers, shareholders, investor, policy makers) including rating agencies, who want to assess advantages and disadvantages of corporate governance indices. Chapter 2’s findings are useful mainly for board of directors for detecting what corporate governance components are more line up with the most successful companies, or for quantifying firm reliability. The results in Chapter 3 suggest to bidders to be aware of not only deal characteristics, but also firm size discernments, economic freedom outlooks, and accounting figures when considering the exit option of a deal withdrawal.

Business School, The University of Hull
Guney, Yilmaz
Sponsor (Organisation)
University of Hull
Qualification level
Qualification name
3 MB
QR Code