IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v314y2024i3p1111-1126.html
   My bibliography  Save this article

A new ordinal mixed-data sampling model with an application to corporate credit rating levels

Author

Listed:
  • Goldmann, Leonie
  • Crook, Jonathan
  • Calabrese, Raffaella

Abstract

In this paper we propose a new ordinal logistic regression model (OLMIDAS) that allows the inclusion of independent variables at higher frequencies than that of the dependent variable. A simulation study shows that our proposed model can find the true patterns in the data. In an empirical study we apply OLMIDAS to the prediction of corporate credit rating levels and compare its performance to classical logistic regression models with an annual aggregation of the higher-frequency variable, such as ordinal logistic regression and multinomial logistic regression. We find that OLMIDAS outperforms the classical logistic regression models while providing additional knowledge of the structure of the higher-frequency explanatory variable.

Suggested Citation

  • Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.
  • Handle: RePEc:eee:ejores:v:314:y:2024:i:3:p:1111-1126
    DOI: 10.1016/j.ejor.2023.10.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221723007890
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2023.10.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    2. Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas, 2014. "Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 898-915, December.
    3. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    4. Ederington, Louis H, 1985. "Classification Models and Bond Ratings," The Financial Review, Eastern Finance Association, vol. 20(4), pages 237-262, November.
    5. Marshall E. Blume & Felix Lim & A. Craig MacKinlay, "undated". "The Declining Credit Quality of US Corporate Debt: Myth or Reality?," Rodney L. White Center for Financial Research Working Papers 3-98, Wharton School Rodney L. White Center for Financial Research.
    6. Psillaki, Maria & Tsolas, Ioannis E. & Margaritis, Dimitris, 2010. "Evaluation of credit risk based on firm performance," European Journal of Operational Research, Elsevier, vol. 201(3), pages 873-881, March.
    7. 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.
    8. Doumpos, M. & Kosmidou, K. & Baourakis, G. & Zopounidis, C., 2002. "Credit risk assessment using a multicriteria hierarchical discrimination approach: A comparative analysis," European Journal of Operational Research, Elsevier, vol. 138(2), pages 392-412, April.
    9. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    10. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    11. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
    12. Khoo, Joye & Cheung, Adrian (Wai Kong), 2021. "Does geopolitical uncertainty affect corporate financing? Evidence from MIDAS regression," Global Finance Journal, Elsevier, vol. 47(C).
    13. repec:bla:jfinan:v:53:y:1998:i:4:p:1389-1413 is not listed on IDEAS
    14. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    15. Amato, Jeffery D. & Furfine, Craig H., 2004. "Are credit ratings procyclical?," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2641-2677, November.
    16. Stevenson, Matthew & Mues, Christophe & Bravo, Cristián, 2021. "The value of text for small business default prediction: A Deep Learning approach," European Journal of Operational Research, Elsevier, vol. 295(2), pages 758-771.
    17. Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
    18. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    19. Liu, Yixin & Jiraporn, Pornsit, 2010. "The effect of CEO power on bond ratings and yields," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 744-762, September.
    20. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    21. Marshall E. Blume & Felix Lim & A. Craig MacKinlay, "undated". "The Declining Credit Quality of US Corporate Debt: Myth or Reality?," Rodney L. White Center for Financial Research Working Papers 03-98, Wharton School Rodney L. White Center for Financial Research.
    22. Ruey‐Ching Hwang & K. F. Cheng & Cheng‐Few Lee, 2009. "On multiple‐class prediction of issuer credit ratings," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(5), pages 535-550, September.
    23. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    24. Doumpos, Michael & Niklis, Dimitrios & Zopounidis, Constantin & Andriosopoulos, Kostas, 2015. "Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 599-607.
    25. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
    26. Kim, Yoonseong & Sohn, So Young, 2008. "Random effects model for credit rating transitions," European Journal of Operational Research, Elsevier, vol. 184(2), pages 561-573, January.
    27. 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.
    28. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    29. Sanjeev Bhojraj & Partha Sengupta, 2003. "Effect of Corporate Governance on Bond Ratings and Yields: The Role of Institutional Investors and Outside Directors," The Journal of Business, University of Chicago Press, vol. 76(3), pages 455-476, July.
    30. Kaplan, Robert S & Urwitz, Gabriel, 1979. "Statistical Models of Bond Ratings: A Methodological Inquiry," The Journal of Business, University of Chicago Press, vol. 52(2), pages 231-261, April.
    31. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    32. Kuldeep Kumar & Sukanto Bhattacharya, 2006. "Artificial neural network vs linear discriminant analysis in credit ratings forecast," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 5(3), pages 216-227, July.
    33. Samuel B. Bonsall & Brian P. Miller, 2017. "The impact of narrative disclosure readability on bond ratings and the cost of debt," Review of Accounting Studies, Springer, vol. 22(2), pages 608-643, June.
    34. Audrino, Francesco & Kostrov, Alexander & Ortega, Juan-Pablo, 2019. "Predicting U.S. Bank Failures with MIDAS Logit Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(6), pages 2575-2603, December.
    35. Reinganum, Marc R & Shapiro, Alan C, 1987. "Taxes and Stock Return Seasonality: Evidence from the London Stock Exchange," The Journal of Business, University of Chicago Press, vol. 60(2), pages 281-295, April.
    36. repec:fth:pennfi:67 is not listed on IDEAS
    37. Bojing Feng & Wenfang Xue & Bindang Xue & Zeyu Liu, 2020. "Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks," Papers 2012.03744, arXiv.org.
    38. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    39. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
    4. 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.
    5. Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
    6. Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
    7. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    8. 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.
    9. Ken Hung & Hui Wen Cheng & Shih-shen Chen & Ying-Chen Huang, 2013. "Factors that Affect Credit Rating: An Application of Ordered Probit Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 94-108, December.
    10. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    11. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    12. Matthies, Alexander B., 2013. "Empirical research on corporate credit-ratings: A literature review," SFB 649 Discussion Papers 2013-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. repec:hum:wpaper:sfb649dp2013-003 is not listed on IDEAS
    14. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    15. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    16. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    17. Demirtas, K. Ozgur & Rodgers Cornaggia, Kimberly, 2013. "Initial credit ratings and earnings management," Review of Financial Economics, Elsevier, vol. 22(4), pages 135-145.
    18. Hang Luo & Linfeng Chen, 2019. "Bond yield and credit rating: evidence of Chinese local government financing vehicles," Review of Quantitative Finance and Accounting, Springer, vol. 52(3), pages 737-758, April.
    19. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    20. Nava, Consuelo R. & Osti, Linda & Zoia, Maria Grazia, 2022. "Forecasting Domestic Tourism across Regional Destinations through MIDAS Regressions," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202207, University of Turin.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:314:y:2024:i:3:p:1111-1126. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.