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

Reber, Beat

Authors

Beat Reber



Abstract

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.

Journal Article Type Article
Publication Date Jan 1, 2014
Journal European Journal of Finance
Print ISSN 1351-847X
Electronic ISSN 1466-4364
Publisher Taylor & Francis (Routledge)
Peer Reviewed Peer Reviewed
Volume 20
Issue 4
APA6 Citation Reber, B. (2014). Estimating the risk–return profile of new venture investments using a risk-neutral framework and ‘thick’ models. European Journal of Finance, 20(4), doi:10.1080/1351847X.2012.708471
DOI https://doi.org/10.1080/1351847X.2012.708471
Keywords risk-neutral framework, risk–return profile, financing rounds, neural networks
Publisher URL https://www.tandfonline.com/doi/abs/10.1080/1351847X.2012.708471
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
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: http://www.tandfonline....0/1351847X.2012.708471.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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