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Small Business Economics

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11-04-2023

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

Author: Ho-Chang Chae

Published in: Small Business Economics | Issue 1/2024

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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.

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Acs, Z. J., Desai, S., & Hessels, J. (2008). Entrepreneurship, economic development and institutions. Small Business Economics, 31, 219–234.CrossRef

Arrighetti, A., & Lasagni, A. (2013). Assessing the determinants of high-growth manufacturing firms in Italy. International Journal of the Economics of Business, 20(2), 245–267.CrossRef

Arrighetti, A., & Ninni, A. (2009). Firm size and growth opportunities: a survey. Department of Economics, Parma University.

Audretsch, D. B., Coad, A., & Segarra, A. (2014). Firm growth and innovation. Small Business Economics, 43, 743–749.CrossRef

Baily, M. N., Bartelsman, E. J., & Haltiwanger, J. (1996). Downsizing and productivity growth: Myth or reality? Small Business Economics, 8(4), 259–278.CrossRef

Barbero, J. L., Casillas, J. C., & Feldman, H. D. (2011). Managerial capabilities and paths to growth as determinants of high-growth small and medium-sized enterprises. International Small Business Journal, 29(6), 671–694.CrossRef

Bargagli-Stoffi, F. J., Niederreiter, J., & Riccaboni, M. (2021). Supervised learning for the prediction of firm dynamics. In Data Science for Economics and Finance: Methodologies and Applications (pp. 19–41). Springer International Publishing.CrossRef

Barringer, B. R., Jones, F. F., & Neubaum, D. O. (2005). A quantitative content analysis of the characteristics of rapid-growth firms and their founders. Journal of business venturing, 20(5), 663–687.

Belloni, A., Chen, D., Chernozhukov, V., & Hansen, C. (2012). Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica, 80(6), 2369–2429.CrossRef

Bianchini, S., Bottazzi, G., & Tamagni, F. (2017). What does (not) characterize persistent corporate high-growth? Small Business Economics, 48(3), 633–656.CrossRef

Birch, D. L. (1987) Job creation in America: How our smallest companies put the most people to work. New York: Free Press.

Birch, D. L., Mass.). Program on Neighboorhood Massachusetts Institute of, T., Change, R., Mass.). Program on, N., & Regional Change Massachusetts Institute of, T. (1979). The job generation process (Vol.302). MIT program on neighborhood and regional change Cambridge, MA.

Bjuggren, C. M., Daunfeldt, S.-O., & Johansson, D. (2013). High-growth firms and family ownership. Journal of Small Business & Entrepreneurship, 26(4), 365–385.CrossRef

Bravo-Biosca, A., & Westlake, S. (2009). The vital 6 per cent: How high-growth innovative businesses generate prosperity and jobs. NESTA.

Breiman, L. (2001). Random forests. Machine learning, 45, 5–32.CrossRef

Brnjas, Z., Vulićević, V., & Čanaićević, D. (2015). Importance and role of fast growing companies–Gazelles in modern economies. Economic analysis, 48(3-4), 44–61.

Chan, Y. E., Bhargava, N., & Street, C. T. (2006). Having arrived: The homogeneity of high-growth small firms. Journal of Small Business Management, 44(3), 426–440.CrossRef

Choi, M. (2019). Feature generation using machine learning from large sparse financial data.

Coad, A. (2007). Firm growth: A survey. In Evolutionary Economics Group (Eds.), The papers on economics and evolution. Max Planck Institute, Jena Germany.

Coad, A. (2009). The growth of firms: A survey of theories and empirical evidence. Cheltenham: Edward Elgar Publishing.

Coad, A., & Broekel, T. (2012). Firm growth and productivity growth: evidence from a panel VAR. Applied Economics, 44(10), 1251–1269.CrossRef

Coad, A., Daunfeldt, S.-O., Hölzl, W., Johansson, D., & Nightingale, P. (2014). High-growth firms: introduction to the special section. Industrial and corporate change, 23(1), 91–112.CrossRef

Coad, A., & Guenther, C. (2014). Processes of firm growth and diversification: theory and evidence. Small Business Economics, 43, 857–871.CrossRef

Coad, A., & Planck, M. (2012). Firms as bundles of discrete resources–towards an explanation of the exponential distribution of firm growth rates. Eastern Economic Journal, 38, 189–209.CrossRef

Coad, A., & Srhoj, S. (2020). Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms. Small Business Economics, 55(3), 541–565.CrossRef

Coad, A., Cowling, M., & Siepel, J. (2017). Growth processes of high-growth firms as a four-dimensional chicken and egg. Industrial and Corporate Change, 26(4), 537–554.

Daunfeldt, S.-O., Elert, N., & Johansson, D. (2010). Industrial variation of high-growth firms. [Paper presentation], 50th Congress of the European Regional Science Association, Jönköping, Sweden.

Daunfeldt, S.-O., Elert, N., & Johansson, D. (2014). The economic contribution of high-growth firms: do policy implications depend on the choice of growth indicator? Journal of Industry, Competition and Trade, 14(3), 337–365.CrossRef

Delmar, F., Davidsson, P., & Gartner, W. B. (2003). Arriving at the high-growth firm. Journal of Business Venturing, 18(2), 189–216.CrossRef

Deschryvere, M. (2008). High growth firms and job creation in Finland (Discussion Paper 1144). Retrieved from Research Institute of the Finnish Economy: https://EconPapers.repec.org/RePEc:hhs:huiwps:0035

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., & Leitão, P. J. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46.CrossRef

Edberg, E., & Ryss, J. (2019). Managing the social-and financial dimensions in Swedish gazelle companies-A qualitative study from the founder’s perspective [Master’s thesis, Uppsala University]. http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1334533&dswid=1630

Eurostat, O. (2007). Eurostat-OECD manual on business demography statistics. Office for Official Publications of the European Communities.

Fu, G. H., Yi, L. Z., & Pan, J. (2019). LASSO-based false-positive selection for class-imbalanced data in metabolomics. Journal of Chemometrics, 33(10), e3177.CrossRef

Goedhuys, M., & Sleuwaegen, L. (2015). Human capital, innovation and the distribution of firm growth rates (MERIT Working Papers 2015-013), United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT). https://www.merit.unu.edu/publications/wppdf/2015/wp2015-013.pdf

Goswami, A. G., Medvedev, D., & Olafsen, E. (2019). High-growth firms: Facts, fiction, and policy options for emerging economies. Washington, DC: World Bank Publications.

Grundström, C., Öberg, C., & Rönnbäck, A. Ö. (2012). Family-owned manufacturing SMEs and innovativeness: A comparison between within-family successions and external takeovers. Journal of family business strategy, 3(3), 162–173.

Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157–1182.

Guzman, J., & Stern, S. (2016). Nowcasting and place casting entrepreneurial quality and performance. In Measuring entrepreneurial businesses: Current knowledge and challenges (pp. 63–109). Chicago: University of Chicago Press.

Guzman, J., & Stern, S. (2020). The state of American entrepreneurship: New estimates of the quantity and quality of entrepreneurship for 32 US States, 1988–2014. American Economic Journal: Economic Policy, 12(4), 212–243.

Harrell, F. E. (2015). Multivariable modeling strategies. In Regression modeling strategies (pp. 63–102). Cham: Springer.

Helfat, C. E. (2007). Stylized facts, empirical research and theory development in management (Vol. 5, pp. 185–192). Sage Publications Sage UK.

Henrekson, M., & Johansson, D. (2010). Gazelles as job creators: a survey and interpretation of the evidence. Small Business Economics, 35(2), 227–244.CrossRef

Hölzl, W. (2014). Persistence, survival, and growth: a closer look at 20 years of fast-growing firms in Austria. Industrial and Corporate Change, 23(1), 199–231.CrossRef

IMF Survey. (2009). IMF presses Korea to continue rebalancing economy. International Monetary Fund. https://www.imf.org/en/News/Articles/2015/09/28/04/53/socar080909a. Accessed 1 Mar 2023.

Landes, W. M., & Posner, R. A. (2003). The economic structure of intellectual property law. Cambridge, MA: Harvard University Press.

Lee, N. (2014). What holds back high-growth firms? Evidence from UK SMEs. Small Business Economics, 43(1), 183–195.CrossRef

Levratto, N., Zouikri, M., & Tessier, L. (2010). The determinants of growth for SMEs-a longitudinal study from french manufacturing firms.

Lindič, J., Bavdaž, M., & Kovačič, H. (2012). Higher growth through the blue ocean strategy: Implications for economic policy. Research policy, 41(5), 928–938.

Litau, E. (2018). The information problem on the way to becoming a “gazelle”. The Proceedings of the 13th European Conference on Innovation and Entrepreneurship, pp. 394–401. https://www.proquest.com/docview/2117775052

Lopez-Garcia, P., & Puente, S. (2012). What makes a high-growth firm? A dynamic probit analysis using Spanish firm-level data. Small Business Economics, 39(4), 1029–1041.CrossRef

McKenzie, D. J., & Sansone, D. (2017). Man vs. machine in predicting successful entrepreneurs: Evidence from a business plan competition in Nigeria (The World Bank Policy Research Working Paper 8271). https://ideas.repec.org/s/wbk/wbrwps.html

Megaravalli, A. V., & Sampagnaro, G. (2018). Predicting the growth of high-growth SMEs: Evidence from family business firms. Journal of Family Business Management, 9(1), 98–109.CrossRef

Miyakawa, D., Miyauchi, Y., & Perez, C. (2017). Forecasting firm performance with machine learning: Evidence from Japanese firm-level data. (RIETI Research Discussion Paper 17-E-068). Retrieved from Research Institute of Economy, Trade and Industry (RIETI) website: https://www.rieti.go.jp/jp/publications/dp/17e068.pdf

Mohr, V., Garnsey, E., & Theyel, G. (2014). The role of alliances in the early development of high-growth firms. Industrial and Corporate Change, 23(1), 233–259.CrossRef

Moschella, D., Tamagni, F., & Yu, X. (2019). Persistent high-growth firms in China’s manufacturing. Small Business Economics, 52(3), 573–594.CrossRef

Myhrvold, N. (2010). The big idea: funding eureka!. Harvard Business Review, 88(3), 44–50.

Obwegeser, N., De Araújo Burcharth, A. L., & Carugati, A. (2015). The value of IT: Explaining the strategic role of information systems for fast growing SMEs. 2015 Mediterranean Conference on Information Systems. https://aisel.aisnet.org/mcis2015/37/

Parker, S. C., Storey, D. J., & Van Witteloostuijn, A. (2010). What happens to gazelles? The importance of dynamic management strategy. Small Business Economics, 35(2), 203–226.CrossRef

Penrose, E. (2009). The Theory of the Growth of the Firm (4th ed.). Oxford: Oxford University Press.

Pereira, V., & Temouri, Y. (2018). Impact of institutions on emerging European high-growth firms. Management Decision., 56(1), 175–187. https://doi.org/10.1108/MD-03-2017-0279.CrossRef

Sampagnaro, G., & Lubrano Lavadera, G. (2013). Identifying high growth SMEs through balance sheet ratios. Available at SSRN 2207550.

Schlichter, J., Klyver, K., & Haug, A. (2021). The moderating effect of ERP system complexity on the growth–profitability relationship in young SMEs. Journal of Small Business Management, 59(4), 601–626.CrossRef

Segarra, A., & Teruel, M. (2014). High-growth firms and innovation: an empirical analysis for Spanish firms. Small Business Economics, 43(4), 805–821.CrossRef

Shepherd, D., & Wiklund, J. (2009). Are we comparing apples with apples or apples with oranges? Appropriateness of knowledge accumulation across growth studies. Entrepreneurship theory and practice, 33(1), 105–123.

Simbaña-Taipe, L. E., Mullo, D. C. U., Chuquin, M. S., Morales-Urrutia, X., & Sánchez, M. I. (2019). Key determinants for growth in high-growth Ecuadorian manufacturing firms. International Journal of Management and Enterprise Development, 18(4), 293–315.CrossRef

Statistics Korea. (n.d.). https://kostat.go.kr/menu.es?mid=a20203080000. Accessed 29 Mar 2023.

Storey, D. J. (1994). New firm growth and bank financing. Small Business Economics, 6(2), 139–150.CrossRef

Storey, D. J. (2011). Optimism and chance: The elephants in the entrepreneurship room. International Small Business Journal, 29(4), 303–321.

Sutton, J. (2012). Competing in capabilities: the globalization process. Oxford: OUP Oxford.

Szalavetz, A. (2015). A dynamic capabilities perspective of high-growth firms. International Journal of Management and Economics, 48, 45–62.CrossRef

Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52, 89–100.CrossRef

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.

Tomczyk, D., Lee, J., & Winslow, E. (2013). Entrepreneurs’ personal values, compensation, and high growth firm performance. Journal of Small Business Management, 51(1), 66–82.CrossRef

van Witteloostuijn, A., & Kolkman, D. (2019). Is firm growth random? A machine learning perspective. Journal of Business Venturing Insights, 11, e00107.CrossRef

Virtanen, J. (2019). Predicting high-growth firms with machine learning methods. [Master’s thesis, University of Jyväskylä]. https://jyx.jyu.fi/handle/123456789/63260

Weinblat, J. (2018). Forecasting european high-growth firms-a random forest approach. Journal of Industry, Competition and Trade, 18(3), 253–294.CrossRef

Zekić-Sušac, M., Šarlija, N., Has, A., & Bilandžić, A. (2016). Predicting company growth using logistic regression and neural networks. Croatian Operational Research Review, 7(2), 229–248.CrossRef

Title: In search of gazelles: machine learning prediction for Korean high-growth firms
Author: Ho-Chang Chae
Publication date: 11-04-2023
Publisher: Springer US
Published in: Small Business Economics / Issue 1/2024
Print ISSN: 0921-898X
Electronic ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-023-00760-8

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