An interpretable machine learning framework for explaining company valuation

Valuing private enterprises - particularly early-stage firms - remains a significant challenge due to high variability and frequent overvaluation, often exceeding 50%. These inaccuracies undermine trust in the venture capital (VC) market and complicate investment decision-making. To address these issues, we present machine learning (ML) frameworks that enhance valuation accuracy. Our study introduces a novel ML approach that outperforms traditional models, achieving a Mean Absolute Percentage Error of 34.90%, significantly improving upon industry benchmarks. The Total Cost of Ownership is identified as the most critical valuation methodology, with financial metrics becoming increasingly influential as firm value rises. Our findings highlight the potential of advanced data mining techniques to deliver more reliable, comprehensive valuations within the VC landscape. Future research should explore expanding this analysis to emerging markets and incorporating primary data for deeper insights.