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

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Authors: Coad, Alex1,2 (AUTHOR) acoad@pucp.edu.pe, Srhoj, Stjepan3 (AUTHOR)
Source: Small Business Economics. Oct2020, Vol. 55 Issue 3, p541-565. 25p. 10 Charts.
Subject Terms: *Forecasting, *Big data, *Business enterprises, Gazelles
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. [ABSTRACT FROM AUTHOR]
Database: Entrepreneurial Studies Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="AR" term="%22Coad%2C+Alex%22">Coad, Alex</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> acoad@pucp.edu.pe</i><br /><searchLink fieldCode="AR" term="%22Srhoj%2C+Stjepan%22">Srhoj, Stjepan</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Small+Business+Economics%22">Small Business Economics</searchLink>. Oct2020, Vol. 55 Issue 3, p541-565. 25p. 10 Charts.
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  Data: *<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br />*<searchLink fieldCode="DE" term="%22Business+enterprises%22">Business enterprises</searchLink><br /><searchLink fieldCode="DE" term="%22Gazelles%22">Gazelles</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ent&AN=145257419
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        Value: 10.1007/s11187-019-00203-3
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 541
    Subjects:
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Business enterprises
        Type: general
      – SubjectFull: Gazelles
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      – TitleFull: Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms.
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            NameFull: Srhoj, Stjepan
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            – D: 01
              M: 10
              Text: Oct2020
              Type: published
              Y: 2020
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