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Estimation of Firms' Default Rates in terms of Intangible Assets

Author

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  • Saiki Tsuchiya

    (Bank of Japan)

  • Shinichi Nishioka

    (Bank of Japan)

Abstract

This paper quantitatively analyzes how firms' default rates are affected by intangible assets, which play a crucial role in business management but are difficult to assess objectively. We use intangible assets such as firms' technological capability and the qualifications of senior management, for which numerical data from each firm are available. The results are as follows: (1) intangible assets have statistical explanatory power for firms' default rates in addition to financial data; (2) a model that incorporates intangible assets has greater accuracy in estimating default rates than one that incorporates only financial data, and the difference in the accuracy is statistically significant; and (3) the impact of changes in intangible assets on firms' default rates is comparable with that of changes in financial data. Based on our analysis, it may be effective to take into consideration intangible assets to enhance the accuracy in estimating firms' default rates. Therefore, in assessing firms' credit risk, it is important to enhance the information on intangible assets to objectively assess these assets.

Suggested Citation

  • Saiki Tsuchiya & Shinichi Nishioka, 2014. "Estimation of Firms' Default Rates in terms of Intangible Assets," Bank of Japan Working Paper Series 14-E-2, Bank of Japan.
  • Handle: RePEc:boj:bojwps:wp14e02
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    File URL: http://www.boj.or.jp/en/research/wps_rev/wps_2014/data/wp14e02.pdf
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    References listed on IDEAS

    as
    1. Ono, Arito & Hasumi, Ryo & Hirata, Hideaki, 2014. "Differentiated use of small business credit scoring by relationship lenders and transactional lenders: Evidence from firm–bank matched data in Japan," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 371-380.
    2. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
    3. Tadanobu Nemoto & Yoshiaki Ogura & Wako Watanabe, 2013. "The Decision-Making Mechanism of Regional Financial Institutions and the Utilization of Soft Information," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 9(1), pages 87-116, January.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    6. Edward I. Altman & Gabriele Sabato, 2007. "Modelling Credit Risk for SMEs: Evidence from the U.S. Market," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 332-357, September.
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    Keywords

    Estimated default rates; Intangible assets; Logit model; Bootstrap method;

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