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Combining Bond Rating Forecasts Using Logit

Author

Listed:
  • Kamstra, M.
  • Kennedy, P.
  • Suan, T.-K.

Abstract

This paper uses the ordered logit regression combining method to form consensus forecasts from different individual bond rating forecasts, to predict bond ratings in the transportation and industrial sectors form Moody's bond rating service.

Suggested Citation

  • Kamstra, M. & Kennedy, P. & Suan, T.-K., 1998. "Combining Bond Rating Forecasts Using Logit," Discussion Papers dp98-10, Department of Economics, Simon Fraser University.
    • Handle: RePEc:sfu:sfudps:dp98-10
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    Citations

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    Cited by:

    1. Kamphol Panyagometh & Gordon S. Roberts, 2010. "Do Lead Banks Exploit Syndicate Participants? Evidence from Ex Post Risk," Financial Management, Financial Management Association International, vol. 39(1), pages 273-299, March.
    2. Arundina, Tika & Azmi Omar, Mohd. & Kartiwi, Mira, 2015. "The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 273-292.
    3. Rodrigues, Bruno Dore & Stevenson, Maxwell J., 2013. "Takeover prediction using forecast combinations," International Journal of Forecasting, Elsevier, vol. 29(4), pages 628-641.
    4. Riddha Basu & James P. Naughton, 2020. "The Real Effects of Financial Statement Recognition: Evidence from Corporate Credit Ratings," Management Science, INFORMS, vol. 66(4), pages 1672-1691, April.
    5. Themistokles Lazarides & Evaggelos Drimpetas, 2016. "Defining the factors of Fitch rankings in the European banking sector," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 6(2), pages 315-339, August.
    6. Caporale, Guglielmo Maria & Matousek, Roman & Stewart, Chris, 2012. "Ratings assignments: Lessons from international banks," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1593-1606.
    7. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    8. Shen, Chung-Hua & Huang, Yu-Li & Hasan, Iftekhar, 2012. "Asymmetric benchmarking in bank credit rating," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(1), pages 171-193.
    9. Graziani, Carlo & Rosner, Robert & Adams, Jennifer M. & Machete, Reason L., 2021. "Probabilistic recalibration of forecasts," International Journal of Forecasting, Elsevier, vol. 37(1), pages 1-27.
    10. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    11. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    12. Alexander B. Matthies, 2013. "Empirical Research on Corporate Credit-Ratings: A Literature Review," SFB 649 Discussion Papers SFB649DP2013-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. Patrick Behr & Darren J. Kisgen & Jérôme P. Taillard, 2018. "Did Government Regulations Lead to Inflated Credit Ratings?," Management Science, INFORMS, vol. 64(3), pages 1034-1054, March.
    14. Mafudi & Negina Kencono Putri, 2012. "The Impact of Corporate Governance Implementation on Public Company Bond Ratings and Yield: a Case of Indonesia," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 8(6), pages 88-98, December.
    15. Salvador, Carlos & Pastor, Jose Manuel & Fernández de Guevara, Juan, 2014. "Impact of the subprime crisis on bank ratings: The effect of the hardening of rating policies and worsening of solvency," Journal of Financial Stability, Elsevier, vol. 11(C), pages 13-31.
    16. Altman, Edward I. & Rijken, Herbert A., 2004. "How rating agencies achieve rating stability," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2679-2714, November.
    17. Petropoulos, Anastasios & Siakoulis, Vasilis & Stavroulakis, Evangelos & Vlachogiannakis, Nikolaos E., 2020. "Predicting bank insolvencies using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1092-1113.
    18. repec:zbw:bofrdp:2012_013 is not listed on IDEAS
    19. Rubina Shaheen & Attiya Yasmin Javid, 2014. "Effect of Credit Rating on Firm Performance and Stock Return; Evidence form KSE Listed Firms," PIDE-Working Papers 2014:104, Pakistan Institute of Development Economics.
    20. Jaspreet Kaur & Madhu Vij & Ajay Kumar Chauhan, 2023. "Signals influencing corporate credit ratings—a systematic literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 91-114, March.
    21. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    22. Shen, Chung-Hua & Huang, Yu-Li & Hasan, Iftekhar, 2012. "Asymmetric benchmarking in bank credit rating," Bank of Finland Research Discussion Papers 13/2012, Bank of Finland.
    23. Rosemarie Bröker Bone & Eduardo P Ribeiro, 2013. "Informational content of corporate ratings in a developing country: the case of Brazilian firms," Economics Bulletin, AccessEcon, vol. 33(1), pages 35-45.
    24. Delgado-Vaquero, David & Morales-Díaz, José, 2018. "Estimating a Credit Rating for Accounting Purposes: A Quantitative Approach/Estimación del Rating Crediticio para Contabilidad: Un enfoque cuantitativo," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 459-488, Mayo.
    25. Irving Fisher Committee, 2019. "The use of big data analytics and artificial intelligence in central banking," IFC Bulletins, Bank for International Settlements, number 50, July.

    More about this item

    Keywords

    PROJECTIONS ; STATISTICAL ANALYSIS ; ECONOMETRIC MODELS ; INVESTMENTS;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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