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基于组合权集的油气田企业财务预警模型研究
引用本文:张力军,张春生,同胜利.基于组合权集的油气田企业财务预警模型研究[J].西南石油大学学报(社会科学版),2012(1):11-16,8,9.
作者姓名:张力军  张春生  同胜利
作者单位:成都理工大学能源学院;中国石油长庆油田分公司长南气田开发项目部
摘    要:当前世界经济缓慢复苏,但仍面临许多不稳定因素,针对油气田行业特点和所面临的财务风险,通过综合比较国内外经典财务预警模型的基础上,系统地构建了包含财务指标和非财务指标的油气田企业财务预警指标体系。提出将评价指标的客观熵权与主观AHP权重相结合,建立基于熵权修正AHP权重、以相对优属度代替绝对隶属度的油气田企业财务预警综合评判模型。以某气田企业为例,运用所构建模型进行实证分析,结果显示:该气田企业出现财务危机的可能性总体呈不断上升趋势。此模型的建立,对企业有效警示财务风险并指导管理具有一定的借鉴意义。

关 键 词:熵权  AHP法  多目标决策  油气田企业  财务预警模型

RESEARCH ON OIL AND GAS ENTERPRISE FINANCIAL EARLY-WARNING MODEL BASED ON CONSTITUTE COMBINATION WEIGHT
ZHANG Li-jun,ZHANG Chun-sheng,TONG Sheng-li.RESEARCH ON OIL AND GAS ENTERPRISE FINANCIAL EARLY-WARNING MODEL BASED ON CONSTITUTE COMBINATION WEIGHT[J].Journal of Southwest Petroleum University(Social Sciences Edition),2012(1):11-16,8,9.
Authors:ZHANG Li-jun  ZHANG Chun-sheng  TONG Sheng-li
Institution:1.College of Energy Resources,Chengdu University of Technology,Chengdu Sichuan,610059;2.Department of Changnan Gas-field Project,Changqing Oil-field Company,Petrochina,Xi’an Shaanxi,710021,China)
Abstract:Starting from the characteristics of oil and gas enterprises and financial risks against the background of the global financial crisis,we systematically study the financial early-warning indices,which include financial and non-financial indices.After comparing several classic financial early-warning models,and taking relative membership grade instead of absolute membership grade,we propose the comprehensive judgments model of oil and gas enterprises,the weights of which relate to the criteria determined by AHP combined with entropy weights.Finally,we take a gas field as an example and apply the constructed financial warning model to practice.The result shows the possibility of financial crisis is on the rise in general.This model is hoped to enhance the financial early-warning capability of oil enterprises and therefore improve their fiancial mangement.
Keywords:entropy weight  Analytic Hierarchical Process  multiple objective decision  oil and gas enterprise  financial early-warning model
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