Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?

Abstract

While startup valuations are influenced by revenues, risks, age, and macroeconomic conditions, specific causality is traditionally a black box. Because valuations are not disclosed, roles played by other factors (industry, geography, and intellectual property) can often only be guessed at. VC valuation research indicates the importance of establishing a factor-hierarchy to better understand startup valuations and their dynamics, suggesting the wisdom of hiring data-scientists for this purpose. Bespoke understanding can be established via construction of hierarchical prediction models based on decision trees and random forests. These have the advantage of understanding which factors matter most. In combination with OLS, the also tell us the circumstances of when specific causalities apply. This study explores the deterministic role of categorical variables on the valuation of start-ups (i.e. the joint-combination geographic, urban, and sectoral denomination-variables), in order to be able to build a generalized valuation scorecard approach. Using a dataset of 1,089 venture-capital investments, containing 1,042 unique EU and EEA, this study examines microeconomic, sectoral, and local-level impacts on startup valuation. In principle, the study relies on Fixed-effects and Joint-fixed-effects regressions as well as the analysis and exploration of divergent micro-populations and fault-lines by means of non-parametric approaches combining econometric and machine-learning techniques.

Keywords: Valuation, Startup Valuation, Venture Capital, Entrepreneurial Finance, Machine Learning, Decision Tree, Random Forest, Hierarchical Analysis