Skip to main content
SpringerLink
Log in

Capturing Corporate Attributes in a New Perspective Through Fuzzy Clustering

  • Conference paper
  • First Online:
Book cover New Frontiers in Artificial Intelligence (JSAI-isAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11717))

Included in the following conference series:

Abstract

Although industrial classification plays an important role in various contexts, it is rarely questioned. However, as diversification and business transformation are ongoing, it is becoming difficult to recognize company’s real business. Therefore, there is not enough to allocate one type of business class to express the situation of the company, and a new type of industrial classification system is required. Through the analysis, we construct a new industrial classification system with Fuzzy C Means (FCM). This study also confirms the validity of proposed method through composite variance and absolute prediction error (APE). As the result, we present that there is a possibility that we are able to represent one company with overlapping industry, so to speak, assign one company more than two industries.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In this paper, FCM analysis was repeated 50 times. We used a representative one after confirming each results.

  2. 2.

    \( S_{M1} , S_{M2} ,S_{M3} ,S_{M4} \,and\, S_{M5} \) represent first industry, second industry, third industry, fourth industry and fifth industry respectively, which are newly obtained by the FCM.

References

  1. Elton, E.J., Gruber, M.J.: Homogeneous groups and the testing of economic hypotheses. J. Financ. Quant. Anal. 4(5), 581–602 (1970)

    Article  Google Scholar 

  2. Hrazdil, K., Trottier, K., Zhang, R.: An intra-and inter-industry evaluation of three classification schemes common in capital market research. Appl. Econ. 46(17), 2021–2033 (2014)

    Article  Google Scholar 

  3. Weiner, C.: The impact of industry classification schemes on financial research. SFB 649 discussion paper, No. 2005,062, SFB 649, Economic Risk, Berlin (2005)

    Google Scholar 

  4. Kimura, F.: A comparison of reliability of industrial classification in Japan. Contemp. Discl. Res. 9, 3–42 (2009)

    Google Scholar 

  5. Shintani, O.: Sector classification systems: comparison of reliability between the TSE 33 (stock price index by industry <33 sectors>) and Global Industry Classification Standard (GICS) taxonomies. Secur. Anal. J. 48(4), 77–88 (2010)

    Google Scholar 

  6. Nakaoka, T.: Statistical analysis on industrial classification: a comprehensive survey and a suggestion for new methods of composing homogeneous groups. J. Bus. Econ. Shokeigakuso 61(1), 151–180 (2014)

    Google Scholar 

  7. Sasaki, M., Shinno H.: Industrial classification from corporate website by using document classification method. In: The Association for Natural Language Processing 12th Annual Convention Proceedings, pp. 352–355 (2006)

    Google Scholar 

  8. Ando, K., Shirai, K.: Extracting and classification of industry information from the corporate web pages. In: The Association for Natural Language Processing 24th Annual Convention Proceedings, pp. 1015–1018 (2018)

    Google Scholar 

  9. Isogai, T., Dam, H.: Building classification trees on Japanese stock groups partitioned by network clustering. IEEJ Trans. Electron. Inf. Syst. 137(10), 1387–1392 (2017)

    Google Scholar 

  10. Peneder, M.: Creating industry classifications by statistical cluster analysis. Estudios de economía aplicada 23(2), 451–464 (2005)

    Google Scholar 

  11. Lee, C.M., Ma, P., Wang, C.C.: Search-based peer firms: aggregating investor perceptions through internet co-searches. J. Financ. Econ. 116(2), 410–431 (2015)

    Article  Google Scholar 

  12. Lewellen S.: Firm-specific industries. Working paper (2012)

    Google Scholar 

  13. Sook, L.Y.: A latent class cluster analysis study of financial ratios and industry characteristics. Aust. J. Basic Appl. Sci. 7(11), 46–53 (2013)

    Google Scholar 

  14. Budayan, C., Dikmen, I., Birgonul, M.T.: Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping. Expert Syst. Appl. 36(9), 11772–11781 (2009)

    Article  Google Scholar 

  15. Bezdek, C., Robert, E., William, F.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 91–203 (1984)

    Google Scholar 

  16. Amit, P., Livnat, J.: Grouping of conglomerates by their segments’ economic attributes: towards a more meaningful ratio analysis. J. Bus. Finance Account. 17, 85–99 (1990)

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by a grant-in-aid from the Kayamori Foundation of Informational Science Advancement.

Author information

Authors and Affiliations

  1. Graduate School of Business Administration, Keio University, Hiyoshi 4-1-1, Kohoku-ku, Yokohama-shi, Kanagawa-ken, Japan

    Yusuke Matsumoto, Aiko Suge & Hiroshi Takahashi

Authors
  1. Yusuke Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aiko Suge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hiroshi Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yusuke Matsumoto .

Editor information

Editors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan

    Kazuhiro Kojima

  2. University of Electro-Communications, Tokyo, Japan

    Maki Sakamoto

  3. Ochanomizu University, Tokyo, Japan

    Koji Mineshima

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Matsumoto, Y., Suge, A., Takahashi, H. (2019). Capturing Corporate Attributes in a New Perspective Through Fuzzy Clustering. In: Kojima, K., Sakamoto, M., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2018. Lecture Notes in Computer Science(), vol 11717. Springer, Cham. https://doi.org/10.1007/978-3-030-31605-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31605-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31604-4

  • Online ISBN: 978-3-030-31605-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation