A Machine Learning Approach to Startup Success Prediction in the Context of Venture Capital Industry – Tereza Kalendová
Tereza Kalendová
Master's thesis
A Machine Learning Approach to Startup Success Prediction in the Context of Venture Capital Industry
A Machine Learning Approach to Startup Success Prediction in the Context of Venture Capital Industry
Abstract:
Startups play a fundamental role as drivers of innovation and growth in today’s world economies, while their failure rate keeps strikingly high. Venture capitalists, as one of the major sources of financing of early-stage companies are continuously seeking to identify promising companies to reach high returns. However, their decision-making is characterized as a time-consuming, labour-intensive and …moreAbstract:
Startups play a fundamental role as drivers of innovation and growth in today’s world economies, while their failure rate keeps strikingly high. Venture capitalists, as one of the major sources of financing of early-stage companies are continuously seeking to identify promising companies to reach high returns. However, their decision-making is characterized as a time-consuming, labour-intensive and …more
Language used: English
Date on which the thesis was submitted / produced: 13. 12. 2019
Identifier:
http://www.vse.cz/vskp/eid/81662
Thesis defence
- Date of defence: 18. 9. 2020
- Supervisor: Štěpán Bahník
- Reader: Ondřej Dvouletý
Citation record
ISO 690-compliant citation record:
KALENDOVÁ, Tereza. \textit{A Machine Learning Approach to Startup Success Prediction in the Context of Venture Capital Industry}. Online. Master's thesis. Praha: University of Economics, Prague. 2019. Available from: https://theses.cz/id/ogdqje/.
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- autentizovaným zaměstnancům ze stejné školy/fakulty
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Vysoká škola ekonomická v Prazehttp://www.vse.cz/vskp/eid/81662
Vysoká škola ekonomická v Praze
Master programme / field:
Ekonomika a management / International Management
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