Catching Gazelles with a Lasso: Big Data Techniques for the Prediction of High-Growth Firms

79 Pages Posted: 12 Jun 2019

See all articles by Alex Coad

Alex Coad

Waseda University

Stjepan Srhoj

University of Dubrovnik - Department of Economics and Business Economics

Date Written: April 3, 2019

Abstract

We investigate whether our limited ability to predict high-growth firms is because previous research has used a restricted set of explanatory variables, and in particular because there is a need for explanatory variables with high variation within firms over time. To this end, we apply 'big data' techniques (i.e. LASSO; Least Absolute Shrinkage and Selection Operator) to predict HGFs in comprehensive datasets on Croatian and Slovenian firms. Firms with low inventories, higher previous employment growth, and higher short-term liabilities are more likely to become HGFs. Pseudo R2 statistics of around 10% indicate that HGF prediction remains a challenging exercise.

Keywords: LASSO, high-growth firms, prediction, within variation, firm growth, post-hoc interpretation, inventories

JEL Classification: L25

Suggested Citation

Coad, Alex and Srhoj, Stjepan, Catching Gazelles with a Lasso: Big Data Techniques for the Prediction of High-Growth Firms (April 3, 2019). Available at SSRN: https://ssrn.com/abstract=3395527 or http://dx.doi.org/10.2139/ssrn.3395527

Alex Coad (Contact Author)

Waseda University ( email )

1-104 Totsukamachi, Shinjuku-ku
tokyo, 169-8050
Japan

Stjepan Srhoj

University of Dubrovnik - Department of Economics and Business Economics ( email )

Lapadska Obala 7
Dubrovnik, 20000
Croatia

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