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Predicci—n de fracaso en empresas latinoamericanas utilizando el mŽtodo del vecino más cercano para predecir efectos aleatorios en modelos mixtos || Prediction of Failure in Latin-American Companies Using the Nearest-Neighbor Method to Predict Random Effects in Mixed Models

Author

Listed:
  • Caro, Norma Patricia

    (Facultad de Ciencias Econ—micas. Universidad Nacional de C—rdoba (Argentina))

  • Arias, Ver—nica

    (Facultad de Ciencias Econ—micas. Universidad Nacional de C—rdoba (Argentina))

  • Ortiz, Pablo

    (Facultad de Ciencias Econ—micas. Universidad Nacional de C—rdoba (Argentina))

Abstract
En la presente dŽcada, en econom’as emergentes como las latinoamericanas, se han comenzado a aplicar modelos log’sticos mixtos para predecir el fracaso financiero de las empresas. No obstante, existen limitaciones subyacentes a la metodología, vinculadas a la factibilidad de predicción del estado de nuevas empresas que no han formado parte de la muestra de entrenamiento con la que se estimó el modelo. En la literatura se han propuesto diversos métodos de predicción para los efectos aleatorios que forman parte de los modelos mixtos, entre ellos, el del vecino más cercano. Este método es aplicado en una segunda etapa, luego de la estimación de un modelo que explica la situación financiera (en crisis o sana) de las empresas mediante la consideración del comportamiento de sus ratios contables. En el presente trabajo, se consideraron empresas de Argentina, Chile y Perú, estimando los efectos aleatorios que resultaron significativos en la estimación del modelo mixto. De este modo, se concluye que la aplicación de este método permite identificar empresas con problemas financieros con una tasa de clasificación correcta superior a 80%, lo cual cobra relevancia en la modelación y predicción de este tipo de riesgo. || In the present decade, in emerging economies such as those in Latin-America, mixed logistic models have been started applying to predict the financial failure of companies. However, there are limitations for the methodology linked to the feasibility of predicting the state of new companies that have not been part of the training sample which was used to estimate the model. In the literature, several methods have been proposed for predicting random effects in the mixed models such as, for example, the nearest neighbor. This method is applied in a second step, after estimating a model that explains the financial situation (in crisis or healthy) of companies by considering the behavior of its financial ratios. In this study, companies from Argentina, Chile and Peru were considered, estimating the random effects that were significant in the estimation of the mixed model. Thus, we conclude that the application of these methods allow for identifying companies with financial problems with a correct classification rate of over 80%, which becomes important in modeling and predicting this risk.

Suggested Citation

  • Caro, Norma Patricia & Arias, Ver—nica & Ortiz, Pablo, 2017. "Predicci—n de fracaso en empresas latinoamericanas utilizando el mŽtodo del vecino más cercano para predecir efectos aleatorios en modelos mixtos || Prediction of Failure in Latin-American Companies U," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 24(1), pages 5-24, Diciembre.
    • Handle: RePEc:pab:rmcpee:v:24:y:2017:i:1:p:5-24
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    fracaso empresarial; ratios contables; modelos mixtos; predicción; vecino más cercano; business failure; accounting ratios; mixed model; prediction; nearest neighbors;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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