Financial Distress Prediction with a Novel Diversity-Considered GA-MLP Ensemble Algorithm

摘要

An effective financial distress prediction model is essential, as financial distress of listed companies will cause big problems for listed company owners, shareholders, banks, and regulators. Due to the special circumstances of China, Chinese listed companies that received the ‘special treatment’ label are considered to be in financial distress in this paper. We construct a relatively new and complete financial distress data set of Chinese listed companies from 2014 to 2019. Their 14 financial indicators of year \(t-2\) are used to predict whether they will receive the ST label in year t. When building the prediction model, we first propose GA-MLP algorithm in which the Multi-Layer Perception (MLP) is optimized using Genetic Algorithm (GA) which could automatically determine the parameter values and the number of neurons in each hidden layer at the same time, and then a novel diversity-considered GA-MLP ensemble algorithm (DGAMLPE) is proposed to improve the performance. We objectively test the proposed algorithms on the constructed financial distress data set and five other datasets, and compare them with other 7 algorithms in terms of accuracy, F1-score, and AUC. Experimental results show that DGAMLPE performs very well on the financial distress data set, and it outperform other tested algorithms for the data set tested.