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) |
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| 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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| 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] |
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| ISSN: | 0921898X |
| DOI: | 10.1007/s11187-019-00203-3 |
