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.
Rodríguez López, M., Piñeiro Sánchez, C., & Llano Monelos, P. de. (2015). Financial risk determination of failure by using parametric model, artificial intelligence and audit information. Estudios De Economía, 41(2), pp. 187–217. Retrieved from https://estudiosdeeconomia.uchile.cl/index.php/EDE/article/view/37250