Networks and News in Credit Risk Management
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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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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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Google Scholar -
Altman, E. I. (1968): Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance 23(4), 589–609.
Google Scholar -
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 -
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 -
Box, G. E. P./Cox, D. R. (1964): An Analysis of Transformations, Journal of the Royal Statistical Society. Series B (Methodological).
Google Scholar -
Cossin, D./Schellhorn, H. (2007): Credit Risk in a Network Economy, Management Science Vol. 53, 1604–1617.
Google Scholar -
Da, Z./Engelberg, J./Gao, P. (2011): In Search of Attention, Journal of Finance 66(5), 1461–1499.
Google Scholar -
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 -
Eisenberg, L./Noe, T. H. (2001): Systemic Risk in Financial Systems, Management Science 47 (2), 236–249.
Google Scholar -
Elliott, M./Golub, B./Jackson, M. O. (2014): Financial Networks and Contagion, American Economic Review 104(10), 3115–3153.
Google Scholar -
Fagiolo, G./Reyes, J./Schiavo, S. (2007): On the Topological Properties of the World Trade Web: A Weighted Network Analysis, Working Paper.
Google Scholar -
Halaj, G./Kok, C. (2013): Assessing Interbank contagion Using Simulated Networks, Working Paper.
Google Scholar -
Hastie, T./Tibshirani, R./Friedman, J. (2008): The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics.
Google Scholar -
Heston, S. L./Sinha, N. R. (2016): News versus Sentiment: Predicting Stock Returns from News Stories, Working Paper.
Google Scholar -
Jackson, M. O. (2008): Social and Economic Networks, Princeton University Press.
Google Scholar -
Kamstra, M./Kennedy, P./Teck-Kin, S. (2001): Combining bond rating forecasts using logit, The Financial Review 37, 75–96.
Google Scholar -
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 -
Mählmann, T. (2011): Is there a relationship benefit in credit ratings?, Review of Finance, 1–36.
Google Scholar -
Porter, M. F. (1980): An algorithm for suffix stripping, Program 14, 130–137.
Google Scholar -
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 -
Ratha, D./De, P. K./Mohapatra, S. (2010): Shadow Sovereign Ratings for Unrated Developing Countries, World Development 39, 295–307.
Google Scholar -
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 -
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.