The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments

Abstract

This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using firm-specific information in financial statements and business plans, we derive benchmarks for management revenue forecasts. With these benchmarks, we estimate forecast errors as an a priori measure of biased expectations. Using this measure for our proprietary dataset on venture-backed start-ups in Germany, we find evidence of substantial upward forecast biases. We uncover that firms with large forecast errors fail significantly more often than do less biased entrepreneurs in years following the investment. Overall, our results highlight the implications of excessive optimism and overconfidence in entrepreneurial environments and emphasize the relevance of accounting information and business plans for venture capital investment decisions.

Keywords: Management forecast biases, cross-sectional projection models, venture-backed start-ups, failure prediction, overoptimism, overconfidence

JEL Classification: G24, G32, M13, M41