Application of Logistic Regression Models in Measuring Corporate Investment and Financing Efficiency

Abstract

Corporate investment and financing efficiency reflects the coordination between a company’s financing capacity, capital allocation quality, and value creation capacity, and is an important research topic in corporate finance and corporate financial management. With the continuous accumulation of corporate financial, market, and governance data, the measurement of investment and financing efficiency has gradually shifted from single financial indicator analysis to a comprehensive method combining statistical models, efficiency evaluation models, and risk identification models. The Logistic Regression Model, due to its strong classification and identification capabilities, clear variable interpretation, and ease of converting results into probabilistic judgments, has high application value in identifying corporate financing constraints, judging investment efficiency, providing early warning of financial risks, and evaluating the investment and financing status. This paper analyzes the application of the Logistic Regression Model in measuring corporate investment and financing efficiency, focusing on the theoretical basis, efficiency status construction methods, explanatory variable selection, model setting path, result evaluation methods, and applicable scenarios. The study argues that the Logistic Regression Model should not be simply regarded as a tool for directly calculating efficiency values, but is more suitable as a method for identifying the status of corporate investment and financing efficiency and analyzing influencing factors. Future research should further improve the model in terms of efficiency label construction, dynamic data modeling, nonlinear variable handling, and practical application in enterprises.