@article{fe9a6caa3e9d4f7c992384cad498e2df,
title = "New technology assessment in entrepreneurial financing - Does crowdfunding predict venture capital investments?",
abstract = "Crowdfunding is a relatively new gateway for entrepreneurs to access capital for creative and innovative ideas. It allows individuals to start experiments with new products and technologies where the outcome is distant. Yet predicting the success of hitherto unseen products and technologies is fraught with ambiguity and uncertainty. Early stage product experimentation and market access through reward-based crowdfunding, where potential customers provide funds for new unproven products, can therefore provide quality signals to subsequent financiers of new technologies. Our study investigates whether there is a long-run relationship between crowd funding and VC investments on the aggregate and the industry level. We draw on a dataset covering 77,654 projects that successfully raised funds on Kickstarter and 3260 VC investments in the US between 2012 and 2017. The results suggest that crowdfunding Granger causes VC investments. Moreover, the monthly crowd funding and VC investment time series are cointegrated. We therefore conclude that successful crowdfunding campaigns lead to a subsequent increase in VC investments. This holds at the aggregate level and particularly for hardware and consumer electronics, as well as fashion. These results enhance our understanding of the co-development between crowdfunding and VC investments. Reward-based crowdfunding helps VC investors in assessing future trends rather than crowding them out of the market.",
keywords = "Technology prediction, Granger causality, Reward-based crowdfunding, Venture capital, TIME-SERIES, PRIVATE EQUITY, UNIT-ROOT, COINTEGRATION, EXPERIENCE, INFERENCE, DYNAMICS, DEMAND, IMPACT, CROWD",
author = "Jermain Kaminski and Christian Hopp and Tereza Tykvova",
note = "Funding Information: We would like to thank Cesar A. Hidalgo, Cristian Jara-Figueroa, Dominik Hartmann, and Yves-Alexandre de Montjoye (MIT) for valuable comments on earlier versions of this paper. Also, we are grateful to Yasin Ozcan, NBER, Vincenzo Capizzi, Universit{\`a} del Piemonte Orientale and Douglas Cumming, York University Schulich School of Business, for discussions. Oleksandr Zastupailo provided great support in generating large parts of the dataset. We gratefully acknowledge access to Dow Jones Venture Source and Thomson One provided by the DALAHO, University of Hohenheim. We further like to thank CrunchBase for providing us with full data access for academic use. The authors would like to thank the German Federal Ministry of Education and Research for supporting this reseach through the project {"}InnoFinance{"} ( 01IO1702 ). Funding Information: We would like to thank Cesar A. Hidalgo, Cristian Jara-Figueroa, Dominik Hartmann, and Yves-Alexandre de Montjoye (MIT) for valuable comments on earlier versions of this paper. Also, we are grateful to Yasin Ozcan, NBER, Vincenzo Capizzi, Universit{\`a} del Piemonte Orientale and Douglas Cumming, York University Schulich School of Business, for discussions. Oleksandr Zastupailo provided great support in generating large parts of the dataset. We gratefully acknowledge access to Dow Jones Venture Source and Thomson One provided by the DALAHO, University of Hohenheim. We further like to thank CrunchBase for providing us with full data access for academic use. The authors would like to thank the German Federal Ministry of Education and Research for supporting this reseach through the project “InnoFinance” (01IO1702). Publisher Copyright: {\textcopyright} 2018 Elsevier Inc.",
year = "2019",
month = feb,
doi = "10.1016/j.techfore.2018.11.015",
language = "English",
volume = "139",
pages = "287--302",
journal = "Technological Forecasting and Social Change",
issn = "0040-1625",
publisher = "Elsevier Inc.",
}