Determining Drivers of Private Equity Return with Computational Approaches

Abstract

Private equity (PE) represents the acquisition of stakes in non-listed companies, often long-term, with the objective of improving the performance and value of the company to obtain significant benefits at time of disinvestment. PE has gained particular importance in the global financial system for delivering superior risk-adjusted returns. Knowing the PE return drivers has been of great interest among researchers and academics, and some studies have developed statistical models to determine PE return drivers. Still, the explanatory capacity of these models has certain limitations related to their precision levels and exclusive focus on groups of countries located in Europe and the EE.UU. Therefore, in the current literature, new models of analysis of the PE return drivers are demanded to provide a better fit in worldwide scenarios. This study contributes to the accuracy of the models that identify the PE return drivers using computational methods and a sample of 1606 PE funds with a geographical focus on the world’s five regions. The results have provided a unique set of PE return drivers with a precision level above 86%. The conclusions obtained present important theoretical and practical implications, expanding knowledge about PE and financial forecasting from a global perspective.