This Stanford CS230 project tests whether deep learning can predict a startup's valuation step-up multiple in its next financing round, using Pitchbook data on over 34,000 deals across 22,000 US companies founded between 1990 and 2020. The authors built a 10-layer fully-connected neural network with Leaky ReLU activations and L1 regularization, achieving a 1.26 validation MSE, beating a linear regression baseline's 2.34 MSE. A random forest regressor outperformed both models, reaching a 1.10 validation MSE, suggesting tree-based models suit this structured tabular data better than deep learning. The paper also flags that its dataset shows only a 7% company failure rate versus the roughly 25% national average, a sign of survivorship bias that likely inflates the apparent predictability of successful outcomes.

This paper is useful precisely because of its negative result, that a simpler, cheaper random forest model beat the deep neural network the authors originally set out to build, a genuinely instructive finding for anyone assuming more sophisticated architectures automatically produce better predictions on structured deal data by default alone. Investors or data teams considering building their own valuation prediction tools get a concrete benchmark suggesting tree-based methods may be the more sensible starting point for tabular venture data rather than deep learning. The explicit discussion of survivorship bias, quantified against the actual national VC failure rate, is also a useful methodological caution for anyone building predictive models from Pitchbook or similar commercial datasets that skew toward tracked, surviving companies only.