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  • G33  (2)
  • Santiago de Chile: Universidad de Chile, Departamento de Economía  (2)
  • La Habana
  • Spanish  (2)
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  • Spanish  (2)
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  • 1
    Publication Date: 2019-03-22
    Description: This paper offers a comparative analysis of the effectiveness of eight popular forecasting methods: univariate, linear, discriminate and logit regression; recursive partitioning, rough sets, artificial neural networks, and DEA. Our goals are: clarify the complexity-effectiveness balance of each methodology; identify a reduced set of independent variables that are significant predictors whatever the methodology is; and discuss and relate these findings to the financial theory, to help consolidate the foundations of a theory of financial failure. Our results indicate that, whatever the methodology is, reliable predictions can be made using four variables; these ratios convey information about profitability, financial structure, rotation, and operating cash flows.
    Keywords: G33 ; C19 ; M4 ; ddc:330 ; Financial failure forecast ; multivariate methods ; artificial intelligence ; machine learning
    Repository Name: EconStor: OA server of the German National Library of Economics - Leibniz Information Centre for Economics
    Language: Spanish
    Type: doc-type:article
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  • 2
    Publication Date: 2016-09-29
    Description: This paper offers an exhaustive analysis of the effectiveness of several models and methodologies that are commonly used to forecast financial failure: Linear, MDA, Logit, and artificial neural network. Our main aim is to evaluate their relative strengths and weaknesses, in terms of technical reliability and error cost; to do so, models are estimated and validated, and then used to perform an artificial simulation to evaluate which of them causes the lower cost of errors. Reliability is examined in four forecast horizons, to collect evidences about temporal (in) stability. We also check the relative advantages of financial ratios-based models, versus audit-based forecast models. Our results suggest that all models attain a high performance rate; however, artificial neural networks' forecasts seem to be more stable, both in temporal and cross-sectional perspectives.
    Keywords: G33 ; C45 ; C89 ; ddc:330 ; financial failure ; financial difficulties ; forecast insolvency ; audit report
    Repository Name: EconStor: OA server of the German National Library of Economics - Leibniz Information Centre for Economics
    Language: Spanish
    Type: doc-type:article
    Location Call Number Expected Availability
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