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A predictive model of sovereign investment grade using machine learning and natural language processing

Author

Listed:
  • María Victoria Landaberry

    (Banco Central del Uruguay)

  • Kenji Nakasone

    (UTEC - Universidad Tecnológica)

  • Johann Pérez

    (UTEC - Universidad Tecnológica)

  • María del Pilar Posada

    (Banco Central del Uruguay)

Abstract

Las agencias calificadoras de riesgo como Moody's, Standard and Poor's y Fitch califican los activos soberanos basados en un análisis matemático de factores económicos, sociales y políticos conjuntamente con un análisis cualitativo de juicio de experto. De acuerdo a la calificación obtenida, los países pueden ser clasificados como aquellos que tienen grado inversor o cuentan con grado especulativo. Tener grado inversor es importante en la medida que reduce en costo de financiamiento y expande el conjunto de potenciales inversores en una economía. En este documento nos proponemos predecir si la deuda soberana de un país será calificada con grado inversor utilizando un conjunto de variables macroeconómicas y variables obtenidas a partir del análisis de texto de los reportes de Fitch entre 2000 y 2018 utilizando técnicas de procesamiento natural de lenguaje. Utilizamos una regresión logística y un conjunto de algoritmos de machine learning alternativos. De acuerdo a nuestros resultados, el índice de incertidumbre, construido a partir de los reportes de Fitch, es estadísticamente significativo para predecir el grado inversor. Al comparar los distintos algoritmos de machine learning, random forest es el que tiene mejor poder predictivo fuera de la muestra cuando la variable dependiente refiere al mismo año que las variables explicativas mientras que knearest neighbors tiene el mejor desempeño predictivo cuando las variables independientes refieren al año anterior en términos del f1-score y recall.

Suggested Citation

  • María Victoria Landaberry & Kenji Nakasone & Johann Pérez & María del Pilar Posada, 2022. "A predictive model of sovereign investment grade using machine learning and natural language processing," Documentos de trabajo 2022005, Banco Central del Uruguay.
  • Handle: RePEc:bku:doctra:2022005
    as

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    File URL: https://www.bcu.gub.uy/Estadisticas-e-Indicadores/Documentos%20de%20Trabajo/5.2022.pdf
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    References listed on IDEAS

    as
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    4. Richard Cantor & Frank Packer, 1996. "Determinants and impact of sovereign credit ratings," Economic Policy Review, Federal Reserve Bank of New York, vol. 2(Oct), pages 37-53.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Riesgo soberano; agencias calificadoras; variables macroeconómicas; análisis de texto; procesamiento natural del lenguaje; machine learning;
    All these keywords.

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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