The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
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Analysis of Important Indicators of TCB Using GBM

일반화가속모형을 이용한 기술신용평가 주요 지표 분석

  • Received : 2017.10.22
  • Accepted : 2017.11.26
  • Published : 2017.11.30
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Abstract

In order to provide technical financial support to small and medium-sized venture companies based on technology, the government implemented the TCB evaluation, which is a kind of technology rating evaluation, from the Kibo and a qualified private TCB. In this paper, we briefly review the current state of TCB evaluation and available indicators related to technology evaluation accumulated in the Korea Credit Information Services (TDB), and then use indicators that have a significant effect on the technology rating score. Multiple regression techniques will be explored. And the relative importance and classification accuracy of the indicators were calculated by applying the key indicators as independent features applied to the generalized boosting model, which is a representative machine learning classifier, as the class influence and the fitness of each model. As a result of the analysis, it was analyzed that the relative importance between the two models was not significantly different. However, GBM model had more weight on the InnoBiz certification, R&D department, patent registration and venture confirmation indicators than regression model.

기술력 기반의 중소벤처기업에 대한 기술금융 지원을 위해 정부는 2014년 7월부터 기술보증기금 및 일정 자격을 갖춘 민간 기술신용평가사에게 일종의 기술력 등급평가인 기술신용평가를 실시하여 은행의 여신에 활용토록 하였다. 본 논문에서는 최근까지의 기술신용평가 현황 및 한국신용정보원에서 축적하고 있는 기술평가 관련 가용 지표들에 대한 선행 연구를 개략적으로 살펴본 후 기술평가등급점수에 유의적인 영향을 미치는 지표(indicator)를 통상적인 다중회귀기법으로 탐색할 것이다. 본 논문의 관심 대상인 지표 별 등급 영향도와 모형의 적합도는 대표적인 기계학습 분류기(classifier)인 일반화가속모형(Generalized Boosting Model; GBM)을 적용하여 분석하였는 바, 주요 지표를 독립변수(feature)로 투입하여 지표의 상대적 중요성 및 분류 정확도를 산출하였다. 분석결과 회귀모형과 기계학습 모형 간 지표별 상대적인 중요도는 크게 차이나지 않는 것으로 분석되었으나, GBM 모형의 경우 회귀모형에 비해서 이노비즈인증, 연구소 및 연구개발전담부서 보유, 특허등록건수, 벤처확인 지표 등 기술개발역량이 상대적으로 기술등급에 더 큰 영향을 미치는 것으로 분석되었다.

Keywords

References

  1. Hong, J. B., "Development of technology evaluation model for investment," Pukyong National University, p. 2, 2015.
  2. Cha, W. J., "Government's Role for Effective SME Financing, Legislation and Policy," National Assembly Legislation Bureaup, pp. 325-326, 2016.
  3. Lee, J. M., "Current Status of Technology Finance and Improvements for Effective Settlement," National Future Research Institute, pp. 4-5, 2015.
  4. Jeon, W. J. and Seo, Y. W., "Analysis of TCB Grade and PD employing TCB Big Data," KIEC, pp. 403-420, 2016.
  5. Financial Services Commission, "From "quantitative expansion" to "qualitative improvement," "systematization of technical financial system and improvement of system" is promoted," 2015.
  6. Lee, Y. B., "An Analysis of the Relative Efficiency and the Total Factor Productivity Changes of SMEs in SME Funding Program," Korean Journal of Public Administration, Vol. 46, No. 1, pp. 199-229, 2006.
  7. Cho, S. P., Lee, Y. H., Park, S. Y., and Bae, J. H., “Analysis of R&D Scoreboard for R&D Investment and Performance,” Journal of Technology Innovation, Vol. 10, No. 1, pp. 98-123, 2002.
  8. Sung, T. K., “A Firm Size-Innovative Activity Relationship: An Empirical Study of the Korean Manufacturing Industry,” The Korean Small Business Review, Vol. 25, No. 2, pp. 305-325, 2003.
  9. Lee, J. M., Noh, M. S., and Chung, S. Y., "A Study on the Effects of SME's Technology Planning Competency on the Success of Commercialization," Journal of Technology Innovation, Vol. 21, No. 1, pp. 253-278, 2013.
  10. Park, S. G., Moon, H. C., and Cha, K. C., "The Effects of Venture Firm's Phased Internationalization and Learning Capability on Its Business Performance," The Korean Small Business Review, Vol. 35, No. 2, pp. 129-157, 2013.
  11. Kim, S. W. and Choi, Y. H., "The Relation Between Patenting Behavior and Company Performance at the level of the Chemical Firm," KTIS, pp. 389-402, 2003.
  12. Zahra, S. A., "Technology strategy and new venture performance: a study of corporate-sponsored and independent biotechnology ventures," Journal of Business Venturing, Vol. 11, No. 4, pp. 289-321, 1996. https://doi.org/10.1016/0883-9026(95)00128-X
  13. Kim, Y. B. and Lee, B. H., “Patterns of Technological Learning among the Strategic groups in the Korean Electronic Parts Industry,” Research Policy, Vol. 31, No. 4, pp. 543-567, 2002. https://doi.org/10.1016/S0048-7333(01)00127-5
  14. Freund, Y. and Schapire, R. E., "A decision-theoretic generalization of online learning and an application to boosting," Journal of Computer and System Sciences, Vol. 55, No. 1, pp. 119-139, 1997. https://doi.org/10.1006/jcss.1997.1504
  15. Friedman, J. H., “Greedy function approximation: a gradient boosting machine,” Annals of Statistics, Vol. 29, No. 5, pp. 1189-1232, 2001. https://doi.org/10.1214/aos/1013203451
  16. Ridgeway, G., "Generalized Boosted Models: A guide to the gbm package," Update 1.1, 2007.
  17. Lee, J. W. and Yun, J. Y., "A Study on Suitability of Technology Appraisal Model in Technology Financing," Journal of Korea Technology Innovation Society Vol. 20, No. 2, pp. 305-308, 2017.
  18. Park, C. G., Roh, H. S., Choi, Y. G., Kim, H. W., and Lee, J., K., "A Study on the Application Methods of Big Data in the Technology Commercialization Process," The Journal of Society for e-Business Studies, Vol. 19, No. 4, pp. 73-99, 2014. https://doi.org/10.7838/jsebs.2014.19.4.073