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Neural Networks, Ordered Probit Models and Multiple Discriminants. Evaluating Risk Rating Forecasts of Local Governments in Mexico

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  • Alfonso Mendoza-Velázquez
  • Pilar Gómez-Gil

Abstract

Credit risk ratings have become an important input in the process of improving transparency of public finances in local governments and also in the evaluation of credit quality of state and municipal governments in Mexico. Although rating agencies have recently been subjected to heavy criticism, credit ratings are indicators still widely used as a benchmark by analysts, regulators and banks monitoring financial performance of local governments in stable and volatile periods. In this work we compare and evaluate the performance of three forecasting methods frequently used in the literature estimating credit ratings: Artificial Neural Networks (ANN), Ordered Probit models (OP) and Multiple Discriminant Analysis (MDA). We have also compared the performance of the three methods with two models, the first one being an extended model of 34 financial predictors and a second model restricted to only six factors, accounting for more than 80% of the data variability. Although ANN provides better performance within the training sample, OP and MDA are better choices for classifications in the testing sample respectively.

Suggested Citation

  • Alfonso Mendoza-Velázquez & Pilar Gómez-Gil, 2011. "Neural Networks, Ordered Probit Models and Multiple Discriminants. Evaluating Risk Rating Forecasts of Local Governments in Mexico," Working Papers 1, Centro de Investigación e Inteligencia Económica (CIIE), Departamento de Ciencias Sociales - UPAEP.
  • Handle: RePEc:pue:wpaper:1
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    File URL: http://cecip.upaep.mx/investigacion/CIIE/RePEc/pue/wpaper/1.pdf
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    References listed on IDEAS

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    1. Kuldeep Kumar & Sukanto Bhattacharya, 2006. "Artificial neural network vs linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances," Review of Accounting and Finance, Emerald Group Publishing, vol. 5(3), pages 216-227, August.
    2. Mendoza-Velázquez, Alfonso, 2009. "The Information Content and Redistribution Effects of State and Municipal Rating Changes in Mexico," Economics Discussion Papers 2009-17, Kiel Institute for the World Economy (IfW).
    3. Mendoza-Velázquez, Alfonso, 2009. "The information content and redistribution effects of state and municipal rating changes in Mexico," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-21.
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    More about this item

    Keywords

    Credit Risk Ratings; Ordered Probit Models; Artificial Neural Networks; Discriminant Analysis; Principal Components; Local Governments; Public Finance; Emerging Markets;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • H79 - Public Economics - - State and Local Government; Intergovernmental Relations - - - Other

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