In search of gazelles: machine learning prediction for Korean high-growth firms

Ho-Chang Chae ()
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Ho-Chang Chae: University of Central Oklahoma

Small Business Economics, 2024, vol. 62, issue 1, No 10, 243-284

Abstract: Abstract High-growth firms (HGFs), also known as gazelle firms, have attracted considerable attention due to their outstanding contributions to job creation and additional spillover benefits. Despite years of work, it is still challenging to predict high-growth firms, and many essential HGF characteristics are yet unknown. This study analyzes high-quality Korean firm data and predicts high-growth firms using big data and machine learning methods, such as LASSO, adaptive LASSO, and random forest analysis. Besides, we include variables related to business strategies and activities such as innovation, information systems like ERP, outsourcing, strategic alliances, and expansion to international markets to determine whether the addition of this information enhances HGF prediction. We discovered the significance of revenue growth, efficiency management, asset investment, and human resource management skills in increasing one’s chances of becoming an HGF. The findings suggest that rather than being an erratic process or being purely driven by structural characteristics, firm growth is determined by the firm's endogenous strategic characteristics. Plain English Summary With cutting-edge machine learning techniques including LASSO, adaptive LASSO, and random forest, we discovered the significance of revenue growth, efficiency management, asset investment, and human resource management skills in increasing one’s chances of becoming an HGF.

Keywords: High-growth firms; Gazelle; LASSO; Adaptive LASSO; Machine learning; Big data; Random forest; Innovation; South Korea; Strategic alliance; Information systems (search for similar items in EconPapers)
JEL-codes: L25 L26 M13 O53 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11187-023-00760-8

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