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1、.第七届认证杯数学中国数学建模网络挑战赛承 诺 书我们仔细阅读了第七届认证杯数学中国数学建模网络挑战赛的竞赛规则。我们完全明白,在竞赛开场后参赛队员不能以任何方式包括、电子、网上咨询等与队外的任何人包括指导教师研究、讨论与赛题有关的问题。我们知道,抄袭别人的成果是违反竞赛规则的, 如果引用别人的成果或其他公开的资料包括网上查到的资料,必须按照规定的参考文献的表述方式在正文引用处和参考文献中明确列出。我们重承诺,严格遵守竞赛规则,以保证竞赛的公正、公平性。如有违反竞赛规则的行为,我们承受相应处理结果。我们允许数学中国()公布论文,以供网友之间学习交流,数学中国以非商业目的的论文交流不需要提前取得
2、我们的同意。我们的参赛队号为:2900参赛队员 (签名) :队员1:安成 队员2:勾旭东队员3:子嫣参赛队教练员 (签名): 石涛参赛队伍组别:本科组第七届认证杯数学中国数学建模网络挑战赛编 号 专 用 页参赛队伍的参赛队号:请各个参赛队提前填写好:2900竞赛统一由竞赛组委会送至评委团前:竞赛评阅由竞赛评委团评阅前进展:2014年第七届认证杯数学中国数学建模网络挑战赛第一阶段论文题 目土地储藏方案的风险评估 关 键 词 土地储藏 主成分分析 层次分析法 风险函数 风险评估 摘要:本文针对土地在收储过程中存在一定的金融风险这一问题,综合运用了单一变量分析、多元统计等方法,建立了主成分分析模型和
3、层次分析法模型,利用E*CEL和MATLAB软件及C语言程序进展求解,进而构造了土地储藏方案的风险函数,并利用该风险函数,分别对附件二中给出的数据进展统计分析,从而找出10个风险最大的工程,并给出了造成这 10个工程风险较大的原因。首先,我们分析了单一变量对土地储藏方案的风险的影响。我们利用E*CEL软件,分别绘制了74组方案当中的收购储藏面积、动态回收周期、总收储本钱、预期收益的比照图,粗略的得出与土地储藏方案风险有关的主要因素,即收购储藏面积,总收储本钱以及预期收益。得出影响风险的主要因素后,我们继续利用E*CEL软件,计算得出单位储藏面积的本钱以及收益,进而绘制出单位储藏面积,收益与本钱
4、的比照图,得出74组方案的收益与本钱的差值围。其次,我们建立主成分分析模型,利用MATLAB软件进展相关系数的计算,相对准确的找出与土地储藏方案的风险有关的主要因素,并构造了土地储藏方案的风险函数,即其中,S表示加权之和,即风险总值,*1表示收购储藏面积,*2表示总收储本钱,*3表示预期收益。随后,我们根据构造的土地储藏方案的风险函数S=0.264*1+0.422*2-0.313*3,将74组方案当中的相关数据代入,得出74组风险函数值,我们将74组数据值输入到C语言程序中,设计从大到小顺序排列的程序,得出前10名的数据,与之对应的工程即为10个风险最大的工程,方案序号分别为10、37、47、
5、50、51、57、60、64、66、74,而造成风险较大的原因大都是收购储藏面积过大,总存储本钱过高或预期收益较小。最后,为了保证所建立模型的可行性以及计算结果的可靠性,我们对所建立的模型进展了检验。因为土地储藏方案风险评估是一个决策问题,所以也可用层次分析法进展求解。于是我们又建立了层次分析法模型,利用MATLAB软件进展求解,得出影响土地储藏风险的三个主要因素分别为收购储藏面积,总收储本钱和预期收益,这一结果验证了主成分分析模型的可行性和风险函数计算结果的可靠性。参赛密码 由组委会填写参赛队号: 2900 所选题目: C 题英文摘要Upon the problem that there a
6、re certain financial risks in land purchasing and banking, principal ponent analysis model and analytic hierarchy process model are established in this paper through prehensive application of single variable analysis and multivariate statistical methods. E*CEL, MATLAB and C Language Program are used
7、 to solve and build the risk function of land banking projects. The data provided in Attachment 2 are statistically analyzed based on the risk function, so as to find out the ten projects with the biggest risks. Thereafter, the reasons leading to the risks of these ten projects are e*plained corresp
8、ondingly. First, the influence of single variable on the risks of land banking projects is analyzed. E*CEL is applied to draw the parison diagrams of purchasing and banking area, dynamic recovery cycle, total purchasing and banking cost and prospective earnings for the 74 projects, so as to roughly
9、find out the main factors related to the risks of land banking projects, that is, purchasing and banking area, total purchasing and banking cost and prospective earnings. After that, the cost and earnings of unit banking area are calculated with E*CEL, and the parison diagrams of cost and earnings f
10、or unit banking area are drawn, so as to obtain the scope of difference in cost and earning of these 74 projects. Second, the principal ponent analysis model is established, and MATLAB is used to calculate the correlation coefficient, thus more accurately deciding the main factors associated with th
11、e risks of land banking projects. The risk function of land banking projects is also decided, namelyS = 0.264 *1 + 0.422 *2 - 0.313 *3Following that, the relevant data of 74 projects are substituted into the risk function of land banking projects: S = 0.264 *1 + 0.422 *2 -0.313 *3, so as to obtain 7
12、4 risk function values. These values are then input to C Language Program, which is designed to list the values in the descending order, to get the top ten values. So, the projects corresponding to these values are believed to be the top ten projects with the biggest risks. These projects are Projec
13、t No. 10, No. 37, No. 47, No. 50, No. 51, No.57, No. 60, No.64, No. 66, and No. 74. The reasons for the big risks include too large purchasing and banking area, too high total banking cost or relatively low prospective earnings. Finally, the model is tested for its feasibility and the reliability of
14、 the calculation results. Considering that risk evaluation of land banking projects is a decision-making problem, the analytic hierarchy process is resorted to. Therefore, the analytic hierarchy process model is built, and MATLAB is applied for solutions. The three main factors influencing the risks
15、 of land banking projects are land purchasing and banking area, total purchasing and banking cost and prospective earnings. This result proves the feasibility of the principal ponent analysis model and the reliability of the calculation results of the risk function. . .一、问题重述(一) 相关背景实施土地收储及招拍挂,虽然增加了地方财政收入,改善了城市根底设施建立,提高了土地市场的公平性和透明性。但是,土地收储也是金融风险的关键环节。在土地收储过程中,需要动用大量的资金,如果单纯依靠有限的财政资金是不现实的。同时,当前我国的金融产品较为单一,土地银行、土地债券、土地信托等新型的金融产品至今还未兴起。于是在地方政府授权的情况下,土地收储机构往往大量利用银行的授信贷款、抵押贷款等各种渠道的信贷资金收储土地。而这些资金在土地市场活泼的情况下,风险不易显现。而当土地市场疲