Investors’ Preferences in Financing New Ventures: A Data Mining Approach to Equity

Abstract

This book aims at providing an empirical understanding of the main drivers affecting investors’ preferences in financing new ventures through equity crowdfunding (ECF) and determining fundraising campaign success. ECF is increasing in prominence as a route for new ventures in obtaining external financial resources. To raise capital, entrepreneurs are required to convey quality signals of their proposals with real-time information and knowledge sharing. This book advances knowledge in entrepreneurial finance by investigating the factors that affect individuals’ decisions to participate in ECF. The authors adopt a data mining approach to extract publicly available information from a multitude of crowdfunding platforms across different countries, producing a unique dataset. The book uses an innovative hybrid analysis to generate knowledge patterns creating data-driven models on one hand, and on the other test research hypotheses adopting statistical models to investigate empirical evidence in line, or in contrast, with the extant literature. The book also integrates organizational theories to examine the extent to which ECF platform managers follow a strategy of isomorphism in their choice of information disclosure. The final part of the book discusses how signals are interpreted by investors, how these affect financing preferences, and ultimately the successful completion of a fundraising campaign. The book will be of interest to academics and practitioners in entrepreneurial finance, FinTech, and investment behaviour.