Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built
using accounting-based financial ratios. This study proposes a bankruptcy prediction model …

Effective bankruptcy prediction is critical for financial institutions to make appropriate lending
decisions. In general, the input variables (or features), such as financial ratios, and …

Feature selection is an important data preprocessing step for the construction of an effective
bankruptcy prediction model. The prediction performance can be affected by the employed …

For many corporations, assessing the credit of investment targets and the possibility of
bankruptcy is a vital issue before investment. Data mining and machine learning techniques …

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 …

The emergence of big data, information technology, and social media provides an enormous
amount of information about firms' current financial health. When facing this abundance of …

This study proposes an integration strategy regarding how to efficiently combine the
currently-in-use statistical and artificial intelligence techniques. In particular, by combining …

Bankruptcy prediction has been a popular and challenging research area for decades. Most
prediction models are built using financial figures, stock market data and firm specific …

Bankruptcy prediction and credit scoring are major problems in financial distress prediction.
Studies have shown that prediction models can be made more effective by performing data …

The purpose of this paper is to propose a new binary classification method for predicting
corporate failure based on genetic algorithm, and to validate its prediction power through …