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17-06-2019 | Issue 3/2020

Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms

Journal:: Small Business Economics > Issue 3/2020

Authors:: Alex Coad, Stjepan Srhoj

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Abstract

We investigate whether our limited ability to predict high-growth firms (HGF) 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.

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Title: Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms
Authors:: Alex Coad
Stjepan Srhoj
Publication date: 17-06-2019
DOI: https://doi.org/10.1007/s11187-019-00203-3
Publisher: Springer US
Journal: Small Business Economics An Entrepreneurship Journal
Issue 3/2020
Print ISSN: 0921-898X
Electronic ISSN: 1573-0913

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