Cross-country differences in the size of venture capital financing rounds: a machine learning approach

Marco Taboga ()
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Marco Taboga: Bank of Italy

Empirical Economics, 2022, vol. 62, issue 3, No 3, 1012 pages

Abstract: Abstract We analyze the potential determinants of the size of venture capital financing rounds. We employ stacked generalization and boosted trees, two of the most powerful machine learning tools in terms of predictive power, to examine a large data set on start-ups, venture capital funds and financing transactions. We find that the size of financing rounds is mainly associated with the characteristics of the firms being financed and with the features of the countries in which the firms are headquartered. Cross-country differences in the degree of development of the venture capital industry, while highly correlated with the size of funding rounds, are not significant once we control for other country-level characteristics. We discuss how our findings contribute to the debate about policy interventions aimed at stimulating start-up financing.

Keywords: Venture capital; Financial institutions; Country characteristics; Machine learning (search for similar items in EconPapers)
JEL-codes: C19 F0 G24 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Working Paper: Cross-country differences in the size of venture capital financing rounds: a machine learning approach (2019)
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DOI: 10.1007/s00181-021-02066-8

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