Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28226
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Making it into a successful series a funding : an analysis of Crunchbase and LinkedIn data
Authors: Te, Yiea-Funk
Wieland, Michèle
Frey, Martin
Pyatigorskaya, Asya
Schiffer, Penny
Grabner, Helmut
et. al: No
DOI: 10.1016/j.jfds.2023.100099
10.21256/zhaw-28226
Published in: The Journal of Finance and Data Science
Volume(Issue): 9
Issue: 100099
Issue Date: 2023
Publisher / Ed. Institution: Elsevier
ISSN: 2405-9188
Language: English
Subjects: Success prediction; Machine learning; Startup; Venture capital; Crunchbase; Gradient boosting
Subject (DDC): 006: Special computer methods
658.1: Organization and finance
Abstract: Startups are a key force driving economic development, and the success of these high-risk ventures can bring huge profits to venture capital firms. The ability to predict the success of startups is a major advantage for investors to outperform their competitors. In this study, we explore the potential of using publicly available LinkedIn profiles as an alternative and complementary data source to Crunchbase for predicting startup success. We provide a comprehensive review of the existing literature on the factors that influence startup success to create a large set of features for predictive modeling. We train two models for predicting startup success employing light gradient boosting that use LinkedIn data as a standalone and as a complementary data source, and compare them to baseline models based on Crunchbase data. We show that using LinkedIn as a complementary data source yields the best result with a mean area under the curve (AUC) value of 84%. We also provide a thorough analysis of what types of information contribute most to modeling startup success using the Shapley value method. Our models and analysis can be used to develop a decision support system to facilitate startup screening and the due diligence process for venture capital firms.
URI: https://digitalcollection.zhaw.ch/handle/11475/28226
Fulltext version: Published version
License (according to publishing contract): CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Published as part of the ZHAW project: Machine Learning-Aided Startup Investing (MALASI)
Appears in collections:Publikationen School of Engineering

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Te, Y.-F., Wieland, M., Frey, M., Pyatigorskaya, A., Schiffer, P., & Grabner, H. (2023). Making it into a successful series a funding : an analysis of Crunchbase and LinkedIn data. The Journal of Finance and Data Science, 9(100099). https://doi.org/10.1016/j.jfds.2023.100099
Te, Y.-F. et al. (2023) ‘Making it into a successful series a funding : an analysis of Crunchbase and LinkedIn data’, The Journal of Finance and Data Science, 9(100099). Available at: https://doi.org/10.1016/j.jfds.2023.100099.
Y.-F. Te, M. Wieland, M. Frey, A. Pyatigorskaya, P. Schiffer, and H. Grabner, “Making it into a successful series a funding : an analysis of Crunchbase and LinkedIn data,” The Journal of Finance and Data Science, vol. 9, no. 100099, 2023, doi: 10.1016/j.jfds.2023.100099.
TE, Yiea-Funk, Michèle WIELAND, Martin FREY, Asya PYATIGORSKAYA, Penny SCHIFFER und Helmut GRABNER, 2023. Making it into a successful series a funding : an analysis of Crunchbase and LinkedIn data. The Journal of Finance and Data Science. 2023. Bd. 9, Nr. 100099. DOI 10.1016/j.jfds.2023.100099
Te, Yiea-Funk, Michèle Wieland, Martin Frey, Asya Pyatigorskaya, Penny Schiffer, and Helmut Grabner. 2023. “Making It into a Successful Series a Funding : An Analysis of Crunchbase and LinkedIn Data.” The Journal of Finance and Data Science 9 (100099). https://doi.org/10.1016/j.jfds.2023.100099.
Te, Yiea-Funk, et al. “Making It into a Successful Series a Funding : An Analysis of Crunchbase and LinkedIn Data.” The Journal of Finance and Data Science, vol. 9, no. 100099, 2023, https://doi.org/10.1016/j.jfds.2023.100099.


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