Optimization model for enterprise financial management utilizing genetic algorithms and fuzzy logic
Sujuan Wang, Musadaq Mansoor
TL;DR
This paper introduces a new financial management model using genetic algorithms and fuzzy logic to improve enterprise risk prediction and decision-making.
Contribution
A novel hierarchical reinforcement learning model with fuzzy reasoning is proposed for enterprise financial optimization.
Findings
The HRL-FR model shows superior predictive accuracy in enterprise financial management.
Genetic algorithms enhance neural network performance for financial prediction.
Working capital asset ratio and debt-to-equity ratio are key influencers in financial predictions.
Abstract
This study explores the complexities of enterprise financial management by optimizing financial models with a particular focus on enhancing risk prediction performance. A multi-objective mathematical model is first developed to establish key optimization goals, including cost reduction, improved capital utilization, and increased economic benefits. This model systematically defines decision variables and optimization objectives, providing a comprehensive framework for enterprise financial management. To improve predictive accuracy, the study integrates genetic algorithms with back-propagation (BP) neural networks, leveraging genetic algorithms to optimize the neural network’s parameters and structure. Additionally, a hierarchical reinforcement learning model based on fuzzy reasoning (HRL-FR) is proposed to enhance decision-making capabilities. This model employs hierarchical…
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Taxonomy
TopicsStock Market Forecasting Methods · Metaheuristic Optimization Algorithms Research · Scheduling and Optimization Algorithms