Discrete PCA: an application to corporate governance research

Abstract

This thesis introduces the application of discrete Principal Component Analysis (PCA) to corporate governance research. Given the presence of many discrete variables in typical governance studies, I argue that this method is superior to standard PCA that has been employed by others working in the area. Using a dataset of 244 companies listed on the London Stock Exchange in the year 2002-2003, I find that Pearson's correlations underestimate the strength of association between two variables, when at least one of them is discrete. Accordingly, standard PCA performed on the Pearson correlation matrix results in biased estimates. Applying discrete PCA on the polychoric correlation matrix, I extract from 28 corporate governance variables 10 significant factors. These factors represent 8 main aspects of the governance system, namely auditor reputation, large shareholder influence, size of board committees, social responsibility, risk optimisation, director independence level, female representation and institutional ownership. Finally, I investigate the relationship between corporate governance and a firm's long-run share market performance, with the former being the factors extracted. Consistent with Demsetz' (1983) argument, I document limited explanatory power for these governance factors.