Credit risk assessment of high-tech enterprises based on RSNCL-ANN ensemble model

Abstract

Now, Chinese economic development strategy is focusing on the restructuring of industrial structure, and the high-tech enterprises are facing great opportunities. However, due to the development and evaluation risks, investors are hard to assess their risks accurately. This paper proposed RSNCL-ANN ensemble strategies to build a risk assessment model and establishes indicators that cover corporate debt service, profitability, management, ownership structure and other aspects. These indicators are used to build a comprehensive and complete index system. In the RSNCL-ANN model, the neural network model was used as the base learner, and the strategies of random subspace and negative correlation learning were used to increase the diversity of the base learner so as to enhance the generalization ability of the integrated model. The experiment proved that this model had better predictive ability for venture firms.