The Application of Panel Data Mining Based on Gene Schema in Predicting Finance Distress

Various different methods has been applied in the field of predicting finance distress, including statistical analysis, neural network technologies, genetic algorithm, logistic analysis etc. Although these classical methods have good performance in the prediction of financial distress, there still exist some other disadvantages. As financial data should be panel ones, most investigations only focus on one year’s financial data to interpret the underlying statistic model, which hence may fail to characterize the business failure tendency of ST companies. In comparison, panel data combines cross-section data with time series data so that it can provide researcher with a huge amount of data as well as multi-dimension perspectives. By utilizing panel data based on the binary gene expressions, this article aims at constructing a dynamic prediction model which can explore multiple years’ financial data. By resorting to the dynamic thresholding techniques, the marginal value during discretization can be properly derived by a relative floating on the corresponding industry average value. Relying on the discrete expression, the period gene can be identified from the provided time binary sequence, which can be then explored to recognize ST company. Numerical simulation has demonstrated that our new method can significantly improve the prediction accuracy of realistic financial data, which is of great significance to both theoretical analysis and realistic applications.