Exploring the Application of Financial Indicator Prediction Models in Optimizing Tax Burden

Abstract

Abstract As economic globalization progresses unceasingly, the financial activities of enterprises are increasingly complex, paralleled by a concomitant increase in the intricacy of tax laws and regulations. Consequently, the tax risks faced by enterprises are becoming more pronounced. To predict the financial status of enterprises, a logistic regression model is created by combining relevant financial indicators. Considering the large amount of enterprise financial sample data, a Lasso-Logistic regression model is constructed, and the ADMM algorithm optimizes the model to improve the model's prediction accuracy. Finally, after using the ROC curve to test the validity of the constructed financial index prediction model, the A-share listed companies in the advanced manufacturing industry are taken as the research object of empirical analysis to analyze the relationship between the impact of financial indexes on tax burden. The regression equation for the standardization of tax burden is tax risk = -0.02341+0.03572 VAT effective tax burden +0.15451 income tax effective tax burden +0.21118 current ratio -0.26875 total asset turnover +0.06574 current sales revenue +0.02432 current cost of goods sold +0.13681 gearing ratio + 0.23708 Fixed Assets Change Ratio - 0.26895 Asset Size. Based on the analysis results, this paper proposes three optimization strategies for tax burden.