Mixed logistic model with two independent random coefficients for financial crisis prediction: Argentinean companies

Abstract

The paper develops a mixed logistic financial distress prediction model with two independent random coefficients and validates it for public Argentinean companies. This study complements existing literature on bankruptcy prediction in emerging economies advancing the application of contemporary econometric methods (Caro et al., 2013). Anticipating bankruptcy risks increases portfolios' profitability. Emerging economies and frontier markets differ from developed economies in political, cultural, social and institutional terms. Given those differences, investors and lenders need specific bankruptcy and financial distress prediction models. The model developed achieves an excellent performance using financial statements from firms listed in the Buenos Aires Stock Exchange during 1993-2000 with ratios accepted in the literature (Altman, 1993;Jones and Hensher, 2004). Results show that profitability, assets turnover and cash flow from operations reduce the likelihood of financial distress while leverage increases it for companies operating in a frontier market such as Argentina.

Design of a corporate financial crisis prediction model based on improved ABC-RNN+Bi-LSTM algorithm in the context of sustainable development

“…With the rapid development of artificial intelligence and deep learning technology, the artificial neural network has demonstrated superior performance in financial crisis prediction ( Caro et al, 2020 ). Recent research shows that recurrent neural network (RNN) has a more vital ability to extract text feature vectors ( Tan et al, 2015 ).…”