The Effectiveness of Empirical Analysis of Digital Finance based on Logistic Regression Model and the Measurement of Empirical Model Risk

Abstract

Digital finance represents the trend of financial development in the 21st century, with its own unique basic concepts and theoretical foundations, and is currently a hot research field in the academic community. In the process of constructing the theory of digital finance, a large number of research literature has been used to conduct empirical studies on various topics, with the help of the Digital Inclusive Finance Index developed by the Digital Finance Research Center of Peking University, which is a convenient definition of digital finance. However, the validity of many empirical research results is still worth further investigation. This article has conducted a comprehensive study on this point, defining new concepts for the first time and building a new analytical framework. Through logistic regression model, 1137 empirical results were empirically studied, and it was found that less than 30% of the empirical models met the criteria of low risk and could be adopted. The new achievement of this article is the first definition of the empirical model risk level index, which provides a specific expression of the interval estimation of the model fitting risk level index, namely the calculation formulas for the upper and lower bounds of the interval. The Shannon’s information entropy is applied to supplement the reliability test of the empirical results of the regression model and further conduct variance tests. Therefore, in order to avoid modern financial risks, especially financial operational risks, constructing empirical models strictly according to the testing standards in this article is highly likely to resolve the occurrence of financial operational risks.