Abstract:
FSA techniques appear to offer valuable complementary theoretical and empirical insights to conventional finance research methods in order to better understand the financial impact of corporate diversification strategies. FSA can provide a conceptual framework to integrate the often confusing and conflicting theoretical explanations and empirical results of past research. This thesis explores the potential usefulness of FSA in addressing finance research problems or paradoxes that are characterized by large numbers of inter-connected variables, complex causality and where different configurations lead to similar outcomes. Specifically fuzzy set analysis is used on cross-sectional data from firms listed in London stock exchange FTSE All-share index (2001-2010) in order to address a gap in the literature as to “how corporate diversification necessarily and sufficiently leads to favorable financial performance”.
The results of this research show that there is no simple answer to this question nor is there a simple theoretical explanation. It appears that a diversification strategy per se is neither a necessary nor a sufficient indicator of favorable or unfavorable financial performance. The FSA results showed multiple configurations of corporate diversifications and other firm attributes which are usually or more often than not sufficiently associated with favorable firm value, profitability, and risk-return performance. This indicates presence of complex causality, asymmetric causality, and equifinality in examining determinants of financial performance. The results are partially explained by elements of standalone theories but better explained by the construction of a series of hybrid theoretical frameworks. The usefulness of FSA in helping understand and improve decision making processes that rely on complex financial or numeric information has been demonstrated, and it is hoped that this research acts as a “stepping stone” to legitimate a new set of analytical techniques for accounting and finance researchers to use. This would help corporate managers/CEOs, analysts, and investors in decision making processes.
