Exploration of Intelligent Evaluation and Robust Optimization of the Impact of Leverage on Corporate Innovation
We first construct an intelligent evaluation model for the impact of leverage on corporate innovation using PCA comprehensive evaluation theory.Then,we design a robust interval optimization algorithm for leverage based on swarm intelligence optimization to determine the optimal leverage interval for the target innovation level.Using data from Chinese listed companies provided by the CSMAR database for testing,the results show that the intelligent evaluation model based on the RBF neural network efficiently evaluates the innovation levels of 12 randomly selected companies.The designed swarm optimization algorithm quickly converges to the optimal value during iterations,with the smallest and largest total book leverage exhibiting a"high in the middle and low on both ends"pattern.This means that for companies with medium(larger or smaller)target innovation levels,both the smallest and largest book leverage are higher,while for companies with high(very large or large)and low(not large or small)target innovation levels,both the smallest and largest book leverage are lower.Based on these findings,the following insights and suggestions are proposed:companies with high innovation levels can reduce debt and focus on improving capital utilization;companies with medium innovation levels,although achieving technological innovation,are not yet scaled or mature enough,so they can increase financial support to enhance their innovation level;companies with low innovation levels should focus on coordinated adjustment of debt scale and technological innovation to improve innovation levels,as excessive debt would otherwise increase their financial burden.
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