Mixed logistic model for predicting financial crisis in argentinean and chilean enterprises - Caro, Norma Patricia (2017)
Since the 1960s, companies aim to evaluate future performance of the business management to predict the medium term, processes of gestation and installation of statements of financial vulnerability. The information contained in the financial statements of companies and the ability to analyze the evolution in time of financial ratios allows building models predicting risk of financial crisis. This paper presents a risk prediction model based on the information contained in the financial statements of companies with public offering on the Santiago Stock Exchange and the Stock Exchange of Buenos Aires (Argentina) in the 2000s. The financial crisis is characterized by an inability to meet payment obligations, obtaining excessive quantities of waste and in extreme situations like bankruptcy and subsequent liquidation of the company. Until a little over a year ago, most of the work done to quantify the impact of financial ratios in business crisis apply cross-sectional models, but the construction of models for panel data (longitudinal studies) is relevant given that incorporate the temporal dimension in the study. In particular, it has been demonstrated that the mixed logistic model considered unobserved heterogeneity exceeds the performance of standard logistic model. Both Argentina and Chile have recently applied mixed models with random coefficients to predict statements of financial vulnerability.  The results indicate that in Chilean companies, the ratio of working capital accounts for the largest proportion of heterogeneity induced correlation present data, which justifies its inclusion as random coefficient, while in the Argentine market it is profitability rate. In addition, as fixed effects, indicators best predictor of the financial crisis they are profitability ratios, rotation and debt. It is concluded that significant ratios have discriminatory power and their behavior shows that are indicators for predicting crises.