Impact of Behavioral Biases on Startup Valuation

This thesis investigates the impact of cognitive biases on startup valuation, particularly during the Initial Public Offering (IPO) process. Traditional valuation methods, such as Discounted Cash Flow (DCF) and Comparable Company Analysis (CCA), often assume rational decision-making, yet frequently fall short in the volatile startup environment due to inherent uncertainties and limited financial history. This research compares these traditional approaches with behavioral-adjusted models by analyzing the IPOs of three high-profile startups: Dropbox, Uber, and WeWork. Using a comparative case study methodology, the study examines financial disclosures, stock price performance (first-day, short-term, long-term), investor sentiment, and specific cognitive biases including overconfidence, optimism, anchoring, herding behavior, and loss aversion. Findings indicate that cognitive biases, particularly overconfidence and herding behavior, significantly influenced valuation outcomes, often leading to initial mispricing, valuation discrepancies, and subsequent market corrections. The study concludes that integrating behavioral finance principles into valuation practices is crucial for improving the accuracy and reliability of startup valuations, offering practical implications for investors, analysts, and founders navigating the complexities of the startup ecosystem.