浅析波士顿矩阵法的局限

波士顿矩阵法是最常见的业务投资组合分析方法。然而,由于波士顿矩阵法存在诸如市场增长率及相对市场份额分界点划分不合理等现象,忽视了企业各业务间的关联性,且受不符合现代理财观念,不适合小企业及营销环境变化较大的情形等局限。因此,不能单纯依据波士顿矩阵法的结论进行业务投资组合决策。     

求半正定Hermitian矩阵特征向量的改进乘幂法

经典乘幂法结合压缩法是计算半正定Hermitian矩阵最大前n个特征值对应特征向量的重要方法,但其固定的迭代次数使得待分解矩阵的随机变化和初始向量的不同选择导致计算精度波动较大,同时,较大特征值对应特征向量的计算误差也会影响较小特征值对应特征向量的计算。为克服这些缺点,提出了一种将前后两次迭代所求向量的距离作为迭代终止条件的改进乘幂法,并证明了它在有误差传播时的收敛性。理论计算结果表明,对4阶半正定Hermitian随机矩阵,在相同计算精度前提下,所提方法比经典方法可至少降低一半计算复杂度。     

基于ISM的可达矩阵的简便算法

解释结构模型化技术是最基本、最具特色的系统结构模型化技术,求可达矩阵又是建立递阶结构模型(ISM)中最重要的一步,本文基于ISM有向图,根据布尔代数运算规则,阐述一种更简便的由邻接矩阵求可达矩阵的新算法。本文与Warshall算法作对比,体现出该新算法的简便之处。该算法以后也可以实现计算机化。     

基于熵权与改进的TOPSIS法的蔬菜供应商评估研究

蔬菜供应链的基础上,借助于熵权与相关系数矩阵的TOPSIS法相结合的供应商评价模型。首先构建供应商的评价指标体系,然后采用熵权法得到各指标的权重,再结合实例验证该模型的可行性。结果表明该模型能够更客观地对蔬菜供应商的选择进行有效评估。     

生物炭去除重金属的研究进展

随着矿山开采、金属冶炼加工、化工产业的发展,各种工业废水排放入水环境,致使各类水体污染日趋严重。近年来吸附法作为一种去除水污染物的有效方法,受到了广泛关注。研发新型高效廉价吸附剂则成为该领域的研究热点。生物炭作为一种新型吸附剂,是一种绿色环保的修复材料,具有价格低廉,制备原料来源广泛,孔隙度大、比表面积大、吸附性能强的特点,所以其在重金属吸附与去除的应用中具有良好的潜力与前景。本文针对以生物炭作为水中污染物吸附剂的研究现状进行了总结,对其吸附机理进行了分析。希望本文能为生物炭在我国的水处理技术研究领域的推广与应用提供参考。     

新形势下券商核心竞争力的战略选择

面对悄然而至的加入WTO的挑战 ,国内券商今后如何应对来自国外大券商的竞争压力 ,如何不断地壮大自身实力 ,对于券商的发展具有十分重要的现实意义。从业务创新、人才和研发实力、企业文化、资本规模与融资能力、风险控制能力等多方面讨论券商核心竞争力的培养 ,并结合券商核心竞争力矩阵 ,设计出核心竞争能力选择坐标图 ,以此来分析券商核心竞争力的战略选择与发展策略。     

巴中市农田土壤重金属分布及生态风险评价

[目的]通过测定秦巴山区巴中市农田土壤中镉(Cd)、铅(Pb)、锌(Zn)3种重金属元素的含量及分布特征,为农田重金属防治科学决策的制定提供数据支撑。[方法]文章采用内梅罗指数和潜在生态风险指数来综合评价巴中市土壤重金属污染状况。[结果]巴中市农田土壤锌平均含量比四川省丘陵地区背景值高6.78%,而镉和铅含量均未超出其背景值,总体评价良好,但各检测点之间差异较大,镉含量最多超出背景值5.7倍。内梅罗指数评价结果显示,全市平均综合污染指数为1.10,接近清洁状态。潜在生态风险指数评价结果显示,其最小值为15.94,最大值为177.74,平均值为35.38。[结论]巴中市农田土壤重金属污染状况基本处于低度风险级别,但各地块污染程度不尽一致,应因地制宜,采取适当措施治理土壤污染。     

Robust Markowitz mean‐variance portfolio selection under ambiguous covariance matrix

This paper studies a robust continuous‐time Markowitz portfolio selection problem where the model uncertainty affects the covariance matrix of multiple risky assets. This problem is formulated into a min–max mean‐variance problem over a set of nondominated probability measures that is solved by a McKean–Vlasov dynamic programming approach, which allows us to characterize the solution in terms of a Bellman–Isaacs equation in the Wasserstein space of probability measures. We provide explicit solutions for the optimal robust portfolio strategies and illustrate our results in the case of uncertain volatilities and ambiguous correlation between two risky assets. We then derive the robust efficient frontier in closed form, and obtain a lower bound for the Sharpe ratio of any robust efficient portfolio strategy. Finally, we compare the performance of Sharpe ratios for a robust investor and for an investor with a misspecified model.     

An extended likelihood framework for modelling discretely observed credit rating transitions

The estimation of the parameters of a continuous-time Markov chain from discrete-time observations, also known as the embedding problem for Markov chains, plays in particular an important role for the modeling of credit rating transitions. This missing data problem boils down to a latent variable setting and thus, maximum likelihood estimation is usually conducted using the expectation-maximization (EM) algorithm. We illustrate that the EM algorithm is likely to get stuck in local maxima of the likelihood function in this specific problem setting and adapt a stochastic approximation simulated annealing scheme (SASEM) as well as a genetic algorithm (GA) to combat this issue. Above that, our main contribution is to extend our method GA by a rejection sampling scheme, which allows one to derive stochastic monotone maximum likelihood estimates in order to obtain proper (non-crossing) multi-year probabilities of default. We advocate the use of this procedure as direct constrained optimization (of the likelihood function) will not be numerically stable due to the large number of side conditions. Furthermore, the monotonicity constraint enables one to combine structural knowledge of the ordinality of credit ratings with real-life data into a statistical estimator, which has a stabilizing effect on far off-diagonal generator matrix elements. We illustrate our methods by Standard and Poor’s credit rating data as well as a simulation study and benchmark our novel procedure against an already existing smoothing algorithm.     

一种基于自适应相似矩阵的谱聚类算法

为了消除在构建谱聚类算法的相似矩阵时,高斯核函数中尺度参数的波动影响,构建了一种自适应相似矩阵,并应用到谱聚类算法中。自适应相似矩阵中数据点间的距离度量采用测地距离算法,相距较近的两点间的距离近似于欧氏距离,相距较远的两点则先根据欧氏距离得到每个数据点的k个近邻点,然后累加近邻点的测地距离,由此得到每对数据点间的最短距离。两点间的局部密度用共享近邻的定义来表示,更好地刻画了数据集的本征结构。在5个人工数据集和国际通用UCI数据库中的5个真实数据集上进行实验。实验结果表明,所提算法的聚类准确率高于对比算法的准确率,对复杂分布数据有很强的自适应能力。研究成果为数据挖掘及机器学习提供了思路和方法。