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3168The key of structural leverage governance is to optimize the efficiency of credit resource allocation, which requires financial institutions to master the real information of enterprises’ financial risk and capital efficiency. However, due to the confusion between the concept of “asset” and “capital”, banks generally miscalculate the leverage, yield and short-term solvency of micro enterprises (leverage series misestimate), which affects the efficiency of credit allocation and the effect of policy tools.
This paper expands the research on the influencing factors of credit resource mismatch from institutional factors to information factors (defects in the design of credit decision indicators), which provides a new solution to solve the problem of credit resource mismatch. The distortion of financial information by leverage series misestimate leads banks to deviate from the capital nature of “seeking profits to avoid risks” and the internal logic of capital movement. The correction indexes designed based on the concept of “capital” correct the financial information and clearly expose the two kinds of credit resource mismatches caused by the misleading of leverage series misestimate: Insufficient allocation of credit resources is generated for such enterprises with overrated leverage, underrated yield and underrated short-term solvency, and excessive allocation of credit resources is generated for such enterprises with overrated short-term solvency. Under the impact of leverage series misestimate, insufficient allocation of credit resources generally exists in enterprises with “low credit default risk” (low “corrected leverage”, high “corrected yield” and strong “corrected short-term solvency”), and excessive allocation of credit resources generally exists in enterprises with “high corrected default risk” (high “corrected leverage”, low “corrected yields” and weak “corrected short-term solvency”). Further study shows that the relationship between leverage series misestimate and credit resource mismatch has regional heterogeneity and industry heterogeneity. In the eastern area, where the supply chain is better developed and the capital management level is higher, the relationship between the two is closer. From the aspect of industry, this relationship is closer in technology-intensive industries and competitive industries.
The main contributions of this paper are as follows: Firstly, it expands the research on the influencing factors of credit resource mismatch and points out that financial index validity is an important factor of them. By constructing the leverage series misestimate index system, it comprehensively expounds the design defects of traditional financial index system caused by the confusion of “asset” and “capital”. Based on the revised indicators, it identifies enterprises with “low credit risk” and “high credit risk”, reveals the inverse relationship between the level of credit risk and the scale of credit resources, and explores the deep reasons for the phenomenon of “bad money driving out good” in the process of credit resource allocation. Secondly, by discussing the relationship between the different types of leverage series misestimate and the different direction of credit resource mismatch, it explores the specific areas of “reducing leverage” “stabilizing leverage” and “increasing leverage”, and analyzes the key points of risk prevention and control in structural leverage governance. Thirdly, through the heterogeneity analysis of regions and industries, it further identifies the key regions and industries of credit resource mismatch caused by leverage series misestimate, explores the regional positioning and industry guidance of structural leverage governance, and guides the optimization of regional allocation of credit resources through the correction of leverage series misestimate, which is conducive to driving the release of the vitality of emerging factors and easing the financing constraints of competitive industries.
