Analyzing the Influence of Headline News on Credit Markets in Japan

  • Hiroaki Jotaki
  • Yasuo YamashitaEmail author
  • Satoru Takahashi
  • Hiroshi Takahashi
Part of the Agent-Based Social Systems book series (ABSS, volume 12)


This paper analyzes the influence of text information on credit markets in Japan, focusing on headline news, a source of information that has immediate influence on the money market and also which is regarded as an important source of information when making investment decisions. In this research, we employ an automatic text classification algorithm in order to classify the headline news into several categories. As a result of intensive analysis, we made the following findings (Antweiler W, Frank MZ J Financ 59:1259–1294, 2004): it is possible to build a headline news algorithm to an accuracy of 80% (Ben-Saud T Adopting a liability-led strategy. Pension management, April, pp 34–35, 2005); headline news has an influence on corporate bond spreads in Japan after items of news become public (Black F, Cox J J Financ, 31:351–367, 1976). The reaction of CDS spread is different from that of corporate bond spread even though both spreads relate to credit risk. These results are suggestive from both academic and practical viewpoints.


Fixed income Credit risk Asset management Natural language processing Information technology Artificial intelligence 


  1. Antweiler W, Frank MZ (2004) Is all that talk just noise? The information content of internet stock message boards. J Financ 59:1259–1294CrossRefGoogle Scholar
  2. Ben-Saud T (2005) Adopting a liability-led strategy. Pension management, April, pp 34–35Google Scholar
  3. Black F, Cox J (1976) Valuing corporate securities: some effects of bond indenture provision. J Financ 31:351–367CrossRefGoogle Scholar
  4. Crosbie PJ, Galai D, Mark R (2000) A comparative analysis of current credit risk models. J Bank Financ 24:59–117CrossRefGoogle Scholar
  5. Lewis DD (1998) Naïve (Bayes) at forty: the independence assumption in information retrieval. In: Proceedings of the 10th European Conference on Machine Learning (ECML). Springer-Verlag, New York, pp 4–15Google Scholar
  6. Longstaff FE, Schwartz ES (1995) Valuing risky debt: a new approach. J Financ 50:789–821CrossRefGoogle Scholar
  7. Merton RC (1974) On the pricing of corporate debt: the structure of interest rates. J Financ 29:449–470Google Scholar
  8. Mitchell TM (1997) Machine learning (ECML). McGraw-Hill, New YorkGoogle Scholar
  9. Nigam K, McCallum AK, Thrum S, Mitchell T (2000) Text classification from labeled and unlabeled documents using EM. Mach Learn 39:103–134CrossRefGoogle Scholar
  10. Salton G, McGill MJ (1983) Introduction to modern information retrieval. McGraw-Hill, New YorkGoogle Scholar
  11. Salton G, Yang CC (1973) On the specification of term values in automatic indexing. J Doc 29:351–372CrossRefGoogle Scholar
  12. Sparck JK (1972) Statistical interpretation of term specificity and its application retrieval. J Doc 28:11–21CrossRefGoogle Scholar
  13. Takahashi S, Takahashi H, Tsuda K (2004) An efficient learning system for knowledge of asset management. In: Negoita M, Howlett R, Jain L (eds) Lecture notes in computer science 3213(knowledge-based intelligent information and engineering systems). Springer, Berlin/New York, pp 509–515Google Scholar
  14. Takahashi S, Takahashi M, Takahashi H, Tsuda K (2006) Analysis of the validity of textual data in stock market through text mining. WSEAS Trans Bus Econ 3:310–315Google Scholar
  15. Takahashi S, Takahashi H, Tsuda K (2009) Analysis of the effect of headline news in financial market through text categorization. Int J Comput Appl Technol 35:204–209CrossRefGoogle Scholar
  16. Tetlock PC (2007) Giving content to investor sentiment: the role of Media in the Stock Market. J Financ 68:1139–1168CrossRefGoogle Scholar
  17. Tumarkin R, Tobert FW (2001) News or noise? Internet message board activity and stock prices. Financ Anal J 57:41–51CrossRefGoogle Scholar
  18. Wuthrich B, Cho V, Leung S, Perrmunetilleke D, Sankaran K, Zhang J, Lam W (1998) Daily prediction of major stock indices from textual www data. KDDM’98 conference. AAAI Press, New York, pp 364–368Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Hiroaki Jotaki
    • 1
  • Yasuo Yamashita
    • 1
    • 2
    Email author
  • Satoru Takahashi
    • 1
  • Hiroshi Takahashi
    • 2
  1. 1.Sumitomo Mitsui Trust BankChiyoda-ku, TokyoJapan
  2. 2.Keio University, Graduate School of Business AdministrationKohoku-ku, YokohamaJapan

Personalised recommendations