商业银行信贷风险控制计算模型与算法优化研究
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摘要
本文综合采用聚类分析、判别分析、层次分析等方法建立具有实用价值的信贷风险控制模型，应用模糊数学方法、遗传算法等对常规算法进行优化并予以软件实现，在组合贷款风险测量与控制、商业银行信贷风险跟踪预警监测、贷款效益与风险评价等方面取得了较理想的结果。具体内容如下：
第一章比较全面地介绍了目前在西方发达国家商业银行应用的各种信用风险控制模型，对各种模型的特点、应用范围作了比较分析，同时也介绍了信用风险控制模型的各种计算方法。在此基础上，提出了不能简单照搬西方国家商业银行信用风险控制模型，必须研究适合我国国情的信用风险控制模型的观点。
第二章建立了基于单位风险收益最大原则的贷款组合优化决策模型，分析了该模型的意义和特点，该模型是一个有上下限约束的背包问题，其目标函数为所选择企业的平均净现值和所有被选择企业净现值之间的协方差之比，取值与贷款数不一定呈同方向变化，是一个非连续、多峰的复杂问题。应用二重结构编码的遗传算法，结合贪心算法和局部搜索算法及最优保存策略等，成功地解决了这个复杂而特殊的背包问题，而且收敛速度非常理想。
第三章以企业寿命分布和破产概率为背景提出了商业银行贷款风险测量与控制模型，该模型是一个约束条件有上限的线性规划问题。设计了通过求初始允许基和允许剖分减少约束条件降低矩阵规模以便应用单纯形法求解这类线性规划问题的算法，仿真计算的结果证明了模型和算法的有效性和可行性。
第四章提出了结合使用遗传算法和爬山法的动态聚类算法，改进了常规的k-均值动态聚类法，用于我国目前1200个上市公司的聚类分析，得到了准确的分类结果。应用Fisher多总体判别分析，建立
博士学位论文
摘要
了适合我国实际情况的商业银行信用风险跟踪预警监测模型，并构造
了分类标准，制定了企业信用等级分类办法。
第五章从银行作为债权人的角度出发，以企业的偿债能力为主要
分析对象，根据财政部2002年颁发的评价指标，对我国上市公司进
行分析评价，改进了Edward 1.Altman的Z一Scole破产预测模型，建
立了符合我国国情的Z一Scole破产预测模型。
第六章应用模糊数学理论和模糊聚类分析方法，建立了信贷风险
量化的模糊评价模型，提出了基于相似系数和检测孤立点集的算法，
提高了聚类分析中检测孤立点集的效率，将该算法应用于信贷风险量
化的模糊评价，得到了很好的结论;建立了贷款非财务因素评价模型，
结合使用层次分析法和模糊综合评判方法对借款人的非财务因素进
行评价，能比较准确地计算贷款综合风险度，为贷款决策提供支持。
第七章提出了基于模糊聚类的综合排序算法，应用该算法对银行
的贷款效益进行综合评价，其结论与实际相符，效率比其他综合评价
方法提高了一个数量级;提出了应用Ma全kov链预测模型预测不良贷
款的方法;建立了基于AH于法的商业银行贷款风险分类评价模型。
第八章对本文所研究的内容进行了全面的总结，提出了有待继续
研究的问题。
In this paper, the author applies methods such as clustering analysis, techniques of discriminant analysis and analytical hierarchy process in building up practical credit risk control models, uses genetic algorithms and fuzzy mathematics methods to improve the general algorithms and brings these into effect by software, which bring desirable conclusion in measurement and controlling risks of loans combination, tracking forewarning monitor testing of credit risk in commercial banks, evaluation of loan profits and risks. The arrangement of this thesis is as follows:
The first chapter introduces a variety of credit risk control models which applied in commercial banks of developed countries, and offers an analysis the upon characteristic and appliance scope of all kinds of models by comparison, meanwhile, recommends various calculate methods of the models. On the basis of these, the author points out that credit risk control models of commercial banks cannot be indiscriminately imitated from western countries, but appropriate to our country.
The second chapter builds up a decision-making model of loans portfolio optimization based on principle of maximum earning-per risk, and analyses the meaning and characteristic of this model. This model is a type of Knapsack Problem which has restrictions between lower-limit 'and upper-limit, whose object function is the ratio between the average net present value of selected enterprise and the covariance of the average net
present value-the net present value does not change with the number
of loans in the same direction. The model is a discontinuous and multi-maximum complicated problem. By making use of genetic algorithms with dual-structure codes, greedy algorithms, local search algorithms and optimal storage strategy,the thesis solves this complex and particular Knapsack Problem, and the convergence rates is perfect.
On the grounds of corporation's life distribution and the probability of bankrupt, the third chapter puts forward a measurement and control loans risk model in commercial banks that is a linear programming problem with upper limit. To use simplex method to solve the type linear programming problem, designs a algorithm by calculating the original admissible base and admissible part to reduce restrictive conditions and decrease the scale of the matrix. The simulation results verify the availability and feasibility of the model and algorithm.
The fourth chapter introduces a dynamic clustering analysis, which combines genetic algorithms and mountain climbing algorithm, improves the normal k-mean clustering analysis. This method obtains the correct results while applied in the clustering analysis of 1200 listed companies in our country. By applying Fisher multi- population discriminant analysis method, the author introduces a tracking forewarning monitor testing of credit risk in commercial banks which is fit for our facts, frames the sort standard, and institutes the method of enterprise credit evaluation grading.
Taking banks as creditor, taking the enterprise's solvency as the main analysis object, according to the estimation index forced by Ministry of Finance from 2002, the fifth chapter improves the Edward I. Altaian's Z-Scole bankrupt prediction model, and builds a Z-Scole bankrupt prediction model which suits our national conditions. In the end of this chapter, the author analyses and estimates our listed companies.
The sixth chapter adopts fuzzy mathematics theory and fuzzy clustering analysis methods, builds the fuzzy evaluation model of quantifying credit risk, and puts forward clustering algorithm to check outlier based on similar coefficient sum, which can raise the efficiency of checking outlier in clustering analysis. It comes to a perfect conclusion while applying this algorithm in fuzzy evaluating of quantifying credit risk. At the end, the author sets up a non-financial factors loans evaluation model, which estimates the debtor's non-financial factors combining analytical hierarchy process with fuzzy comprehensive judgment. This precise metho
第一章比较全面地介绍了目前在西方发达国家商业银行应用的各种信用风险控制模型，对各种模型的特点、应用范围作了比较分析，同时也介绍了信用风险控制模型的各种计算方法。在此基础上，提出了不能简单照搬西方国家商业银行信用风险控制模型，必须研究适合我国国情的信用风险控制模型的观点。
第二章建立了基于单位风险收益最大原则的贷款组合优化决策模型，分析了该模型的意义和特点，该模型是一个有上下限约束的背包问题，其目标函数为所选择企业的平均净现值和所有被选择企业净现值之间的协方差之比，取值与贷款数不一定呈同方向变化，是一个非连续、多峰的复杂问题。应用二重结构编码的遗传算法，结合贪心算法和局部搜索算法及最优保存策略等，成功地解决了这个复杂而特殊的背包问题，而且收敛速度非常理想。
第三章以企业寿命分布和破产概率为背景提出了商业银行贷款风险测量与控制模型，该模型是一个约束条件有上限的线性规划问题。设计了通过求初始允许基和允许剖分减少约束条件降低矩阵规模以便应用单纯形法求解这类线性规划问题的算法，仿真计算的结果证明了模型和算法的有效性和可行性。
第四章提出了结合使用遗传算法和爬山法的动态聚类算法，改进了常规的k-均值动态聚类法，用于我国目前1200个上市公司的聚类分析，得到了准确的分类结果。应用Fisher多总体判别分析，建立
博士学位论文
摘要
了适合我国实际情况的商业银行信用风险跟踪预警监测模型，并构造
了分类标准，制定了企业信用等级分类办法。
第五章从银行作为债权人的角度出发，以企业的偿债能力为主要
分析对象，根据财政部2002年颁发的评价指标，对我国上市公司进
行分析评价，改进了Edward 1.Altman的Z一Scole破产预测模型，建
立了符合我国国情的Z一Scole破产预测模型。
第六章应用模糊数学理论和模糊聚类分析方法，建立了信贷风险
量化的模糊评价模型，提出了基于相似系数和检测孤立点集的算法，
提高了聚类分析中检测孤立点集的效率，将该算法应用于信贷风险量
化的模糊评价，得到了很好的结论;建立了贷款非财务因素评价模型，
结合使用层次分析法和模糊综合评判方法对借款人的非财务因素进
行评价，能比较准确地计算贷款综合风险度，为贷款决策提供支持。
第七章提出了基于模糊聚类的综合排序算法，应用该算法对银行
的贷款效益进行综合评价，其结论与实际相符，效率比其他综合评价
方法提高了一个数量级;提出了应用Ma全kov链预测模型预测不良贷
款的方法;建立了基于AH于法的商业银行贷款风险分类评价模型。
第八章对本文所研究的内容进行了全面的总结，提出了有待继续
研究的问题。
In this paper, the author applies methods such as clustering analysis, techniques of discriminant analysis and analytical hierarchy process in building up practical credit risk control models, uses genetic algorithms and fuzzy mathematics methods to improve the general algorithms and brings these into effect by software, which bring desirable conclusion in measurement and controlling risks of loans combination, tracking forewarning monitor testing of credit risk in commercial banks, evaluation of loan profits and risks. The arrangement of this thesis is as follows:
The first chapter introduces a variety of credit risk control models which applied in commercial banks of developed countries, and offers an analysis the upon characteristic and appliance scope of all kinds of models by comparison, meanwhile, recommends various calculate methods of the models. On the basis of these, the author points out that credit risk control models of commercial banks cannot be indiscriminately imitated from western countries, but appropriate to our country.
The second chapter builds up a decision-making model of loans portfolio optimization based on principle of maximum earning-per risk, and analyses the meaning and characteristic of this model. This model is a type of Knapsack Problem which has restrictions between lower-limit 'and upper-limit, whose object function is the ratio between the average net present value of selected enterprise and the covariance of the average net
present value-the net present value does not change with the number
of loans in the same direction. The model is a discontinuous and multi-maximum complicated problem. By making use of genetic algorithms with dual-structure codes, greedy algorithms, local search algorithms and optimal storage strategy,the thesis solves this complex and particular Knapsack Problem, and the convergence rates is perfect.
On the grounds of corporation's life distribution and the probability of bankrupt, the third chapter puts forward a measurement and control loans risk model in commercial banks that is a linear programming problem with upper limit. To use simplex method to solve the type linear programming problem, designs a algorithm by calculating the original admissible base and admissible part to reduce restrictive conditions and decrease the scale of the matrix. The simulation results verify the availability and feasibility of the model and algorithm.
The fourth chapter introduces a dynamic clustering analysis, which combines genetic algorithms and mountain climbing algorithm, improves the normal k-mean clustering analysis. This method obtains the correct results while applied in the clustering analysis of 1200 listed companies in our country. By applying Fisher multi- population discriminant analysis method, the author introduces a tracking forewarning monitor testing of credit risk in commercial banks which is fit for our facts, frames the sort standard, and institutes the method of enterprise credit evaluation grading.
Taking banks as creditor, taking the enterprise's solvency as the main analysis object, according to the estimation index forced by Ministry of Finance from 2002, the fifth chapter improves the Edward I. Altaian's Z-Scole bankrupt prediction model, and builds a Z-Scole bankrupt prediction model which suits our national conditions. In the end of this chapter, the author analyses and estimates our listed companies.
The sixth chapter adopts fuzzy mathematics theory and fuzzy clustering analysis methods, builds the fuzzy evaluation model of quantifying credit risk, and puts forward clustering algorithm to check outlier based on similar coefficient sum, which can raise the efficiency of checking outlier in clustering analysis. It comes to a perfect conclusion while applying this algorithm in fuzzy evaluating of quantifying credit risk. At the end, the author sets up a non-financial factors loans evaluation model, which estimates the debtor's non-financial factors combining analytical hierarchy process with fuzzy comprehensive judgment. This precise metho
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