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

Alex Coad, Stjepan Srhoj

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)541-565
Number of pages25
JournalSmall Business Economics
Volume55
Issue number3
DOIs
Publication statusAccepted/In press - 2019

Keywords

  • Firm growth
  • High-growth firms
  • Inventories
  • LASSO
  • Post hoc interpretation
  • Prediction
  • Within variation

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics

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