Financial Distress Prediction: The Way Forward

Financial distress prediction using the hybrid associative memory with translation

This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been...

The effect of feature selection on financial distress prediction

Financial distress prediction is always important for financial institutions in order for them to assess the financial health of enterprises and individuals. Bankruptcy prediction and credit scoring are two important issues in financial distress prediction where various statistical and machine learning techniques have been employed to develop financial prediction models. Since there are no gene...

The prediction of financial distress using structured financial data from the internet

This paper uses Þnancial structure data via internet network to enable the prediction of Þnancial distress. Financial distress is deÞned as a Þrm that has entered into liquidation, receivership or is declared as of negligible value by the Inland Revenue. A sample of distressed and nondistressed quoted companies listed on the London Stock Exchange for the last 20 years (1980 1999) was obtained f...

health scope in iran: the way forward

Cost-sensitive AdaBoost Selective Ensemble for Financial Distress Prediction

Financial distress prediction (FDP) models are effective tools to prevent stakeholders from suffering economic loss. In the process of FDP, the misclassification cost of typeI error of the model is much higher than that of typeIIerror. Some FPD models based on single classifiers take the asymmetric costs into consideration, but the study on cost-sensitive ensemble approach for FDP is rarely exp...