西南石油大学学报(社会科学版) ›› 2012, Vol. 14 ›› Issue (1): 11-16.DOI: 10.3863/j.issn.1674-5094.2012.01.003

• 石油与天然气软科学 • 上一篇    下一篇

基于组合权集的油气田企业财务预警模型研究

张力军1 张春生2 同胜利2   

  1. 1.成都理工大学能源学院,四川 成都 610059; 2.中国石油长庆油田分公司长南气田开发项目部,陕西 西安 710021
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2012-01-01 发布日期:2012-01-01

RESEARCH ON OIL AND GAS ENTERPRISE FINANCIAL EARLYWARNING MODEL BASED ON CONSTITUTE COMBINATION WEIGHT

ZHANG Li-jun1 ZHANG Chun-sheng2 TONG Sheng-li2   

  1. 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
  • Received:1900-01-01 Revised:1900-01-01 Online:2012-01-01 Published:2012-01-01

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

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.

Key words: entropy weight, Analytic Hierarchical Process, multiple objective decision, oil and gas enterprise, financial earlywarning model

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