Quantification and contribution analysis of financial management risks based on AHP-XGBOOST algorithm
DOI:
https://doi.org/10.54097/hbem.v4i.3547Keywords:
AHP, XGBOOST, Financial Management Risk, Contribution AnalysisAbstract
The chemical industry is an important industry that supports the development of the national economy and is closely related to people's lives. With the impact of COVID-19, the continuous changes in the market economy, the deepening of economic globalization, and the increasing optimization of energy companies, the current chemical companies are experiencing volatile development. In order to effectively cope with the unstable external environment, resist market risks, and ensure the sustainable and healthy development of enterprises with high-quality capital management, chemical companies should pay attention to internal management and improve corporate financial management. In this paper, a new measurement method is proposed for financial management risk, and by combining the Analytic Hierarchy Process (AHP) and the Extreme Gradient Boosting (XGBOOST), the index system of financial management risk coefficient is constructed first, and the risk factors that will have an impact on financial management in the macro environment are selected, and the index system of factors affecting financial management risk is constructed to analyze the degree of influence of nine elements in the macro environment on the financial management risk of chemical enterprises The results show that this paper quantifies and contributes to the analysis of macro factors affecting the financial management risks of enterprises, which helps enterprises to predict and measure their own financial management risks more accurately, helps them to respond to and prevent financial management risks effectively in a timely manner, enriches the response methods of enterprises to deal with the influence of macro factors on their own financial management, and better promotes enterprises to achieve their strategic goals. The quantitative analysis model constructed in this paper is widely applicable and has a specific guiding significance for enterprises to apply it in practice.
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