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Graf, F., Dittgen, M. Networks and News in Credit Risk Management. Credit and Capital Markets – Kredit und Kapital, 52(2), 229-250. https://doi.org/10.3790/ccm.52.2.229
Graf, Ferdinand and Dittgen, Martin "Networks and News in Credit Risk Management" Credit and Capital Markets – Kredit und Kapital 52.2, 2019, 229-250. https://doi.org/10.3790/ccm.52.2.229
Graf, Ferdinand/Dittgen, Martin (2019): Networks and News in Credit Risk Management, in: Credit and Capital Markets – Kredit und Kapital, vol. 52, iss. 2, 229-250, [online] https://doi.org/10.3790/ccm.52.2.229
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Networks and News in Credit Risk Management

Graf, Ferdinand | Dittgen, Martin

Credit and Capital Markets – Kredit und Kapital, Vol. 52 (2019), Iss. 2 : pp. 229–250

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Article Details

Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/ccm.52.2.229
Language
English
Pages
22
Keywords

Author Details

Dr. Ferdinand Graf, d-fine GmbH, An der Hauptwache 7, 60313 Frankfurt

Martin Dittgen, d-fine GmbH, An der Hauptwache 7, 60313 Frankfurt

References

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  4. Bhojraj, S./Sengupta, P. (2003): Effect of Corporate Governance on Bond Ratings and Yields: The Role of Institutional Investors and Outside Directors, Journal of Business 76(3), 455–475.  Google Scholar
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  11. Fagiolo, G./Reyes, J./Schiavo, S. (2007): On the Topological Properties of the World Trade Web: A Weighted Network Analysis, Working Paper.  Google Scholar
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  13. Hastie, T./Tibshirani, R./Friedman, J. (2008): The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics.  Google Scholar
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  16. Kamstra, M./Kennedy, P./Teck-Kin, S. (2001): Combining bond rating forecasts using logit, The Financial Review 37, 75–96.  Google Scholar
  17. Loughran, T./McDonald, B. (2011): When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks, Journal of Finance 66, 35–66.  Google Scholar
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  25. Altman, E. I. (1968): Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance 23(4), 589–609.  Google Scholar
  26. Barber, B. M./Odean, T. (2008): All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, The Review of Financial Studies 21(2), 786–818.  Google Scholar
  27. Bhojraj, S./Sengupta, P. (2003): Effect of Corporate Governance on Bond Ratings and Yields: The Role of Institutional Investors and Outside Directors, Journal of Business 76(3), 455–475.  Google Scholar
  28. Box, G. E. P./Cox, D. R. (1964): An Analysis of Transformations, Journal of the Royal Statistical Society. Series B (Methodological).  Google Scholar
  29. Cossin, D./Schellhorn, H. (2007): Credit Risk in a Network Economy, Management Science Vol. 53, 1604–1617.  Google Scholar
  30. Da, Z./Engelberg, J./Gao, P. (2011): In Search of Attention, Journal of Finance 66(5), 1461–1499.  Google Scholar
  31. Das, S. R./Chen, M. Y. (2007): Yahoo! For Amazon: Sentiment Extraction from Small Talk on the Web, Management Science 53(9), 1375–1388.  Google Scholar
  32. Eisenberg, L./Noe, T. H. (2001): Systemic Risk in Financial Systems, Management Science 47 (2), 236–249.  Google Scholar
  33. Elliott, M./Golub, B./Jackson, M. O. (2014): Financial Networks and Contagion, American Economic Review 104(10), 3115–3153.  Google Scholar
  34. Fagiolo, G./Reyes, J./Schiavo, S. (2007): On the Topological Properties of the World Trade Web: A Weighted Network Analysis, Working Paper.  Google Scholar
  35. Halaj, G./Kok, C. (2013): Assessing Interbank contagion Using Simulated Networks, Working Paper.  Google Scholar
  36. Hastie, T./Tibshirani, R./Friedman, J. (2008): The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics.  Google Scholar
  37. Heston, S. L./Sinha, N. R. (2016): News versus Sentiment: Predicting Stock Returns from News Stories, Working Paper.  Google Scholar
  38. Jackson, M. O. (2008): Social and Economic Networks, Princeton University Press.  Google Scholar
  39. Kamstra, M./Kennedy, P./Teck-Kin, S. (2001): Combining bond rating forecasts using logit, The Financial Review 37, 75–96.  Google Scholar
  40. Loughran, T./McDonald, B. (2011): When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks, Journal of Finance 66, 35–66.  Google Scholar
  41. Mählmann, T. (2011): Is there a relationship benefit in credit ratings?, Review of Finance, 1–36.  Google Scholar
  42. Porter, M. F. (1980): An algorithm for suffix stripping, Program 14, 130–137.  Google Scholar
  43. Pozzi, F./Di Matteo, T./Aste, T. (2013): Spread of risk across financial markets: better to invest in the peripheries, Scientific Reports 3.  Google Scholar
  44. Ratha, D./De, P. K./Mohapatra, S. (2010): Shadow Sovereign Ratings for Unrated Developing Countries, World Development 39, 295–307.  Google Scholar
  45. Tetlock, P. C. (2007): Giving Content to Investor Sentiment: The Role of Media in the Stock Market, Journal of Finance 62, 1139–1167.  Google Scholar
  46. van Steen, M. (2010): An Introduction to Graph Theory and Complex Networks, Maarten van Steen.  Google Scholar

Abstract

The presumably most important function of a corporation is the establishment and management of connections to customers, suppliers, investors, debtors and competitors. All these connections may produce profits or bear risks. Hence, the isolated inspection of a corporation (or also a sovereign) may be insufficient. Instead, the economic environment of a corporation and its connections should be included in its valuation. Usually, this is done via manual and hardly standardized processes with their associated large efforts. This article presents a new method to analyze business news and to build up a network of corporations based on business news. To this end, we search in news articles from Reuters and Bloomberg for corporation names or synonyms and assume a connection exists between two corporations if the corporations are mentioned together frequently. Based on these connections, we (1) build up a network for the S&P500 companies, (2) identify groups therein to validate the approach manually and (3) test, whether corporations with many connections and a particularly favorable position in the network receive better rating grades compared to corporations with fewer connections and an average network position. The latter is equivalent to the question of whether a corporation’s connections are a driver of the firm value. Moreover, we use the business news to measure a corporation’s publicity and sentiment, and relate these to the corporation’s rating as well. Our empirical results indicate that the network properties, the sentiment and the media attention are contained in respectively affect the rating grade. Hence, the incorporation of news in the firm valuation – as it is done by many financial institutions – is reasonable. The factors mentioned above increase the explanatory power of our regression model significantly. Since many corporations have sufficient news coverage for our approach but are not rated from a rating agency, and hence must be rated with internal models, our approach may support manual processes in financial institutions and reduce efforts and costs.

Table of Contents

Section Title Page Action Price
Ferdinand Graf / Martin Dittgen: Networks and News in Credit Risk Management 1
Abstract 1
Zusammenfassung: Netzwerke und Unternehmensnachrichten in der Kreditrisikomessung 2
I. Introduction 2
II. Data 3
III. Sentiment 4
IV. Networks 8
V. Analyses 1
VI. Results 1
VII. Interpretation 1
VIII. Summary and Outlook 2
References 2
Appendix I 2
Appendix II (Online-Only-Appendix) 2

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