Six degrees of early-stage ventures
type: CreativeWork
description: Private markets investment volume and valuations exceed the level of the dot-com bubble (PwC and CBInsights, 2019). The available amount of capital surges as investors announce new multi-billion dollar funds (Kruppa, 2019). Even large, institutional funds in the Silicon Valley, who are used to investing in later stages, move upstream to invest in fledgling firms to achieve higher ownership and returns (Clark, 2019a). Despite the high private market liquidity, standing out from the crowd is critical and has become more difficult to achieve, even for innovative entrepreneurs (Planko et al., 2017). Curiously, venture capitalists who expect the latest technology and innovation from new ventures, did not themselves significantly innovate in their approach, including methods of evaluating ventures (Kupor, 2019). Few investors came up with new, differentiated investment strategies, one such example being data-driven investing (Pitchbook, 2018). Although venture capitalists seek to invest in firms which benefit substantially from the notion that “data is the new oil”, few practice to leverage data for their investment process (Parkins, 2017; Dance et al., 2018; Arroyo et al., 2019; Gompers et al., 2020). Instead, the overwhelming majority adheres to the motto “picking investments is an art, not science” and relies primarily on its networks as the most valuable resource (Bell, 2014; Huang and Pearce, 2015; Gompers et al., 2020). Venture capitalists’ focus on their social networks could not only negatively affect investment decisions and returns, but also promote group-think and stifle the progression of their investment thesis (Wuebker et al., 2015). Reviewing the previous works on entrepreneurship, venture evaluation, and venture capital revealed a significant gap in the literature. While investors and entrepreneurs depend heavily on their social networks, these networks play an insignificant role in venture evaluation. The existing frameworks are inadequate to accurately a…
abstract: Private markets investment volume and valuations exceed the level of the dot-com bubble (PwC and CBInsights, 2019). The available amount of capital surges as investors announce new multi-billion dollar funds (Kruppa, 2019). Even large, institutional funds in the Silicon Valley, who are used to investing in later stages, move upstream to invest in fledgling firms to achieve higher ownership and returns (Clark, 2019a). Despite the high private market liquidity, standing out from the crowd is critical and has become more difficult to achieve, even for innovative entrepreneurs (Planko et al., 2017). Curiously, venture capitalists who expect the latest technology and innovation from new ventures, did not themselves significantly innovate in their approach, including methods of evaluating ventures (Kupor, 2019). Few investors came up with new, differentiated investment strategies, one such example being data-driven investing (Pitchbook, 2018). Although venture capitalists seek to invest in firms which benefit substantially from the notion that “data is the new oil”, few practice to leverage data for their investment process (Parkins, 2017; Dance et al., 2018; Arroyo et al., 2019; Gompers et al., 2020). Instead, the overwhelming majority adheres to the motto “picking investments is an art, not science” and relies primarily on its networks as the most valuable resource (Bell, 2014; Huang and Pearce, 2015; Gompers et al., 2020). Venture capitalists’ focus on their social networks could not only negatively affect investment decisions and returns, but also promote group-think and stifle the progression of their investment thesis (Wuebker et al., 2015). Reviewing the previous works on entrepreneurship, venture evaluation, and venture capital revealed a significant gap in the literature. While investors and entrepreneurs depend heavily on their social networks, these networks play an insignificant role in venture evaluation. The existing frameworks are inadequate to accurately a…
author: Felske, Marc
language: eng
keywords: venture capital, entrepreneurship, early-stage ventures, new business ventures, startups, evaluation, valuation, data-driven investing, social capital, social networks, social network analysis, models
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/4b59bd2c-0bf9-449a-8c2c-a8d404221b78/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2020-04-09
date modified: 2026-07-22T22:24:32Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.59056