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

Reber, Beat (2014) Estimating the risk–return profile of new venture investments using a risk-neutral framework and ‘thick’ models. European Journal of Finance, 20 (4). pp. 341-360. ISSN 1466-4364

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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.

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in European Journal of Finance on 2 August 2012, available online:
Keywords: risk-neutral framework, risk–return profile, financing rounds, neural networks
Schools/Departments: University of Nottingham, UK > Faculty of Social Sciences > Nottingham University Business School
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Depositing User: Eprints, Support
Date Deposited: 05 Apr 2018 07:42
Last Modified: 04 May 2020 20:17

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