Estimating the risk-return profile of new venture investments using a risk-neutral framework and 'thick' models

This study proposes cascade neural networks to estimate the model parameters of the Cox-Ross-Rubinstein risk-neutral approach, which, in turn, explain the risk-return profile of firms at venture capital and initial public offering (IPO)financing rounds. Combining the two methods provides better estimation accuracy than risk-adjusted valuation approaches, conventional neural networks, and linear benchmark models. The findings are persistent across in-sample and out-of-sample tests using 3926 venture capital and 1360 US IPO financing rounds between January 1989 and December 2008. More accurate estimates of the risk-return profile are due to less heterogeneous risk-free rates of return from the risk-neutral framework. Cascade neural networks nest both the linear and nonlinear functional estimation form in addition to taking account of variable interaction effects. Better estimation accuracy of the risk-return profile is desirable for investors so they can make a more informed judgement before committing capital at different stages of development and various financing rounds.