A Novel DBN-EFA-CFA-Based Dimensional Reduation for Credit Risk Measurement

Abstract

Affected by the Federal Reserve's interest rate hike and the downward pressure on the domestic economy, the phenomenon of default is still prominent. The credit risk of the listed companies has become a growing concern of the community. In this paper we present a novel credit risk measurement method based on a dimensional reduation technique. The method first extracts the risk measure indexes from the basal financial data via dimensional reduation by using deep belief network (DBN), exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in turn. And then the credit risk is measured by a systemic structural equation model (SEM) and logistic distribution. To validate the proposed method, we employ the financial data of the listed companies from Q1 2019 to Q2 2022. The empirical results show its effectiveness on statistical evaluation, assessment on testing samples and credit risk forecasting.