This book explores the influence of due diligence information on the valuation of venture-backed start-ups and default risk assessment. It investigates biases in multi-year management forecasts, highlighting the roles of optimism and confidence in entrepreneurial settings. The study introduces cross-sectional models that help investors detect potential biases in management forecasts prior to making investment decisions. Utilizing a proprietary dataset of venture capital investments, the book comprises three research papers. The first paper reveals significant biases in management forecasts and analyzes their determinants, while also assessing a cross-sectional projection method for establishing benchmarks. The second paper focuses on the importance of financial and non-financial information in valuing venture-backed start-ups, evaluating the accuracy of common valuation methods based on industry-specific revenue or asset multiples. The third paper connects findings from the previous studies and examines default risks, proposing a new cross-sectional projection method for investors to derive benchmarks for management forecasts. Empirical analyses indicate that firms with substantial forecast errors are more likely to file for bankruptcy than less biased entrepreneurs post-investment. The overall findings underscore the importance of accounting information and business plans, aiding investors in making informed decisions and enh
