Prediction of Financial Distress With Financial Ratio Analysis

Accurate Prediction of Financial Distress with Machine Learning Algorithms

Prediction of financial distress of companies is analyzed with several machine learning approaches. We used DIANE, a large database containing financial records of small and medium size French companies from the year of 2002 up to 2007. It is shown that inclusion of historical data, up to 3 years priori to the analysis, increase the prediction accuracy. In particular, fluctuations of some finan...

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...

Corporate Financial Distress: An Empirical Analysis of Distress Risk

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...

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...