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Erscheinungsjahr: 
2016
Quellenangabe: 
[Journal:] Estudios de Economía [ISSN:] 0718-5286 [Volume:] 43 [Issue:] 2 [Publisher:] Universidad de Chile, Departamento de Economía [Place:] Santiago de Chile [Year:] 2016 [Pages:] 163-198
Verlag: 
Universidad de Chile, Departamento de Economía, Santiago de Chile
Zusammenfassung (übersetzt): 
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.
Schlagwörter: 
Financial failure forecast
multivariate methods
artificial intelligence
machine learning
JEL: 
G33
C19
M4
Creative-Commons-Lizenz: 
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Dokumentart: 
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