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

Author

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  • Alex Coad

    (CENTRUM Católica Graduate Business School (CCGBS)
    Pontificia Universidad Católica del Perú (PUCP))

  • Stjepan Srhoj

    (University of Dubrovnik)

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.

Suggested Citation

  • Alex Coad & Stjepan Srhoj, 2020. "Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms," Small Business Economics, Springer, vol. 55(3), pages 541-565, October.
    • Handle: RePEc:kap:sbusec:v:55:y:2020:i:3:d:10.1007_s11187-019-00203-3
    DOI: 10.1007/s11187-019-00203-3
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    More about this item

    Keywords

    LASSO; High-growth firms; Prediction; Within variation; Firm growth; Post hoc interpretation; Inventories;
    All these keywords.

    JEL classification:

    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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