Evidence map›Paper›PMID 40567732›Full record

ArticlePeerJ. Computer science2025

Optimization model for enterprise financial management utilizing genetic algorithms and fuzzy logic.

Sujuan Wang, Musadaq Mansoor

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sujuan WangSchool of Accounting, Zhengzhou University of Economics and Business, Zhengzhou, Henan, China.
Musadaq MansoorSchool of Computing Sciences, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, Pakistan.ORCID 0000-0002-8649-4093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 decision-making and policy optimization, incorporating fuzzy reasoning to address uncertainties in complex and dynamic financial environments. Experimental validation using the Compustat dataset confirms the effectiveness of the proposed model. Key financial variables, including the working capital asset ratio and debt-to-equity ratio, are identified as significant influencers of prediction accuracy, reinforcing the model's robustness. The genetic algorithm's search and optimization process identifies parameter combinations that maximize neural network performance, further improving predictive capabilities. Comprehensive evaluations conducted on the Center for Research in Security Prices (CRSP) and Compustat datasets for 2022 confirm the HRL-FR model's superior ability to predict and analyze enterprise financial management information accurately. The model demonstrates higher profitability, enhanced efficiency, and predictive curves that closely align with optimal financial models. These findings highlight the HRL-FR model's potential as a powerful tool for enterprise financial management optimization, offering valuable insights for risk mitigation and strategic decision-making.

Indexed as

BP neural networkFinancial management optimizationFuzzy logicGenetic algorithmHierarchical reinforcement learning

Identifiers

PMID40567732
PMCPMC12190508

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.