考虑投资者风险态度的随机模糊投资组合模型及实证

考虑投资者面临证券市场随机和模糊的双重不确定性,把证券收益率视为随机模糊变量。在前景理论下考虑投资者的风险态度,建立不同的随机模糊收益率、期望收益隶属度函数和目标权重,构建考虑投资者风险态度的随机模糊投资组合模型。采用实证方法把市场分为下降和上升两个阶段,研究不同风险态度投资者的投资组合差异及模型表现。结果表明:投资者的风险态度会影响投资组合的结构;考虑投资者风险态度的随机模糊投资组合模型,能够满足不同风险态度投资者对投资收益和风险的差异需求,且在实际投资决策中具有可行性。     

PI(Portfolio Insurance)投资者特性研究

根据期权定价理论,分析了投资组合保险策略与期权的关系及投资组合保险策略与凸收益函数的关系,通过建立投资组合保险模型,得出不同条件下购买投资组合保险投资者的特点如下:1)随着财富的增加他们的风险承受能力比市场一般投资者增加的快;2)他们的市场预期比一般市场投资者更乐观,并且受益于投资组合保险.     

基于期权博弈的中国风电投资分析

本文运用期权博弈的思想建立模型,将风电特许权投资项目看作不完全信息下的抢滩博弈问题,讨论在现有的特许权机制下,引入碳排放交易机制对风电投资的影响。模型分析了在未来碳价格存在不确定性的情况下,风电投资竞价机制会对投资者的竞价行为会产生什么样的影响,投资者应如何确定自己的最优投标价格,其他竞标者的策略对竞标者的影响将如何体现以及不同因素变化时对投资者投资行为的影响。     

安全首要准则下的借贷约束与不确定风险投资

概率统计的理论与方法在不确定性投资决策中起着重要的作用.文章从安全首要的决策视角,分析信贷约束对投资者风险投资行为是否存在影响以及如何影响.在存款利率和贷款利率不等且存在贷款额度限制的融资背景下,首先建立了一种包含融资资产的改进型安全首要(MSF)投资组合选择模型,然后给出了最优投资组合以及MSF有效前沿的解析表示以及图形示例,进而解释了利率变化和贷款限制对MSF风险投资行为的影响.研究发现:提高贷款利率和设定贷款限制,既可以预防投资者的违约风险,也可能造成MSF投资者的投资效率损失.因此,合理调整存贷款利率与设定贷款限额有助于发挥货币政策对投资市场的有效调节作用.     

上市公司退市风险预警模型研究

运用统计方法,选取有效建模变量,建立了L og istic预测模型对我国上市公司退市风险进行了预测,研究结果表明该模型具有良好的预测精度,该模型预测精度可达98.10%,可以作为证券投资者和分析人员使用的一种有效预测上市公司退市风险的工具.     

基于投资者风险偏好的上市公司投资价值分类模型

从投资者的角度出发,建立上市公司投资价值分类模型.提出基于投资者风险偏好变量的分类模型,并采用广义最小二乘支持向量机进行求解.实证分析表明,模型能很好的体现投资者的风险偏好,并且算法也能针对问题有效的进行处理,在机器学习方法中融入投资者的个人决策具有可行性和有效性.     

过度自信与风险溢价研究

非理性投资者的心理会影响风险溢价。本文基于投资者的过度自信心理偏差构建了证券投资的理性风险溢价度量模型、非理性风险溢价度量模型,并利用理性投资者和非理性投资者的相互作用,构建了证券投资的市场风险溢价度量模型,研究了非理性风险溢价对理性风险溢价和市场风险溢价的偏离问题。研究结论表明:非理性风险溢价偏离市场风险溢价的程度依赖于非理性投资者的市场价值权重。     

农业上市公司股票的风险结构特征研究

本文在引入行业因素和市场因素的"正交化"变换基础上,对农业上市公司股票收益建立了行业因素模型,并利用该模型对农业上市公司股票收益率的方差(风险)进行分解,考察了风险来源及其比例大小(即风险结构)。本文的研究结果可以为投资者构建合适的投资组合、规避投资风险提供参考。     

基于偏度的多期组合投资调整模型

由于不同时期资产收益率以及投资者对风险和收益偏好的变化，加之资金等条件的限制，大多数组合投资问题具有明显的动态特征。本文把单期投资组合拓展到多期，引入偏度和风险度量工具VaR，并考虑交易费用的影响，建立了多期投资组合调整模型。最后，给出实证分析对模型进行分析研究，这对投资者的连续投资行为具有一定的指导作用。     

切换成本对线性市场投资决策影响的实物期权分析

本文基于Hotelling模型,针对一个企业在线性区域内的投资决策,考虑投资者在采用为自己作为新进入公司,为新客户使用本公司的产品付出切换成本来竞争市场的策略行为,建立需求不确定环境中的连续动态模型,应用实物期权分析方法评价投资者的投资机会价值和最优投资策略,研究切换成本对投资者投资机会的价值和投资决策的影响.本文研究结果给出了投资者在付出切换成本获得市场份额和减少成本增加投资价值之间的权衡.     

基于改进粒子群算法的复杂现实约束投资组合研究

基于可能性理论,假设各资产的未来收益率均为梯形模糊数,本文构建了带有V-型交易费用、投资比例上下限和基数约束限制的均值-方差-Yager熵模型。本文采用了带有宽容量的逐步宽容法使构建的三目标模型转化为单目标模型,通过调整宽容量的大小来控制收益和风险的大小,从而使得投资者根据自己的偏好选择适合自己的投资决策。此外,本文通过非线性惯性权重来刻画搜索速度,通过对个体最优适应度值较差的部分粒子进行初始化处理,提出了改进的粒子群算法,从而降低了陷入局部最优的可能性;同时通过0-1矩阵和放缩因子处理了基数约束和上下限约束,使得模型的求解更加有效。最后,通过实例说明了算法的可行性和有效性,给出了投资模型的有效前沿,分析了收益/风险宽容量不变时,风险/收益宽容量变化的作用,从而给投资者提供了更多的决策方案。     

Credit risk estimation using payment history data: a comparative study of Turkish retail stores

Our paper presents a comparative study applying logistic regression and multiple criteria decision analysis tools to the operations of wholesalers to assess the credit risk of their retailers using payment history data and to cluster the risky customers by ranking their risk levels. Our sample comprises approximately 6,000 retailer customers and 600.000 transactions of one of the major wholesalers of Turkey. Our findings emphasize the importance of using payment history and some non-financial factors data for predicting the creditworthiness of a firm.     

Modeling international investment decisions for financial holding companies

This research analyzes the internationalization process model developed by Johanson and Vahlne and derives two integer programming investment decision models that consider the risk attitudes of investment firms. Johanson and Vahlne’s model provides a starting point for building a model that suits the investment approach and decision making process of financial holding companies. In practice, when firms make an international investment decision, there is a need for a model that can generate outputs based on financial measures such as profit, investment returns, and tolerable levels of risk. Thus, in this paper, Johanson and Vahlne’s concepts are studied and financial managers are interviewed to derive models that match the investment decision procedures of the firms. The model helps firms manage the risks of their investments and derive accurate investment strategies based on investment objectives and constraints.     

A Satisficing Chance Constrained Model in the Portfolio Selection of Insurance Lines and Investments

This paper sets out a model that simultaneously determines insurers' satisficing compositions of their insurance and investment portfolios. This model can be explained as follows: different insurance lines and investments have different rates of return and different risks associated with those rates of return. Different insurers also have different, but satisfactory levels of return on equity and risk levels of violating the minimum requirement on cash and liquid assets. We propose a chance constrained programming approach to incorporate all of these factors in the portfolio analysis.     

Predicting time-to-churn of prepaid mobile telephone customers using social network analysis

Mobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability.     

On the credibility of insurance claim frequency: Generalized count models and parametric estimators

We analyze the concept of credibility in claim frequency in two generalized count models–Mittag-Leffler and Weibull count models–which can handle both underdispersion and overdispersion in count data and nest the commonly used Poisson model as a special case. We find evidence, using data from a Danish insurance company, that the simple Poisson model can set the credibility weight to one even when there are only three years of individual experience data resulting from large heterogeneity among policyholders, and in doing so, it can thus break down the credibility model. The generalized count models, on the other hand, allow the weight to adjust according to the number of years of experience available. We propose parametric estimators for the structural parameters in the credibility formula using the mean and variance of the assumed distributions and a maximum likelihood estimation over a collective data. As an example, we show that the proposed parameters from Mittag-Leffler provide weights that are consistent with the idea of credibility. A simulation study is carried out investigating the stability of the maximum likelihood estimates from the Weibull count model. Finally, we extend the analyses to multidimensional lines and explain how our approach can be used in selecting profitable customers in cross-selling; customers can now be selected by estimating a function of their unknown risk profiles, which is the mean of the assumed distribution on their number of claims.     

二层信用策略下部分延期付款的库存模型

当前二层信用期相关文献考虑的都是零售商提供给其顾客相同的信用期,但现实中零售商往往会根据物品的种类不同提供给顾客不同的信用期.为研究此问题,建立了优化供货周期使零售商平均相关成本最小的库存模型,证明了最优供货周期的存在性,并给出实例加以说明.     

基于随机模糊投资乘数的离散CPPI资产配置研究

以传统CPPI投资策略的分析框架为基础,在风险资产为连续价格波动的条件下,构建离散投资决策时点的CPPI投资策略。引入模糊决策的分析方法度量投资决策者的心理预期,将传统CPPI投资策略中的投资乘数修正为随机模糊投资乘数,采用马尔科夫链蒙特卡洛模拟风险资产未来市场价格,利用模糊隶属函数描述投资决策者对未来市场运行状况预期的不确定性,保证即使投资决策者预期不精确的条件下,也能保证离散CPPI投资策略获得相对稳定的投资效果。利用中国证券市场上的真实数据进行实证检验,认为:随机模糊投资乘数最大限度地涵盖了投资决策者主观预测的不确定性;基于随机模糊投资乘数的离散CPPI投资策略在不同的市场运行状况中,较传统的CPPI投资策略更具投资的灵活性,可以在保证投资保险的前提下,追求较高的投资收益。     

Customer Allocation in Maximum Capture Problems

The maximum capture (MAXCAP) model and its variants have been widely used to find the maximum capture that a firm can get as it enters a spatial market where there are already existing (competitor??s) facilities. While the model obtains the optimal demand capture, it however allows the customers to be assigned to the non-closest facility which may incur additional operating costs. A two stage method can be used that overcomes the drawback of the original model while requiring a negligible extra computational effort. To make the original model mathematically self contained and more concise two revised formulations of the problem RMAXCAP-1 and RMAXCAP-2 are proposed which assure that the customers patronize only their closest entering facilities. These models are tested on different sizes of datasets and their performances are compared.     

Bringing cost transparency to the life annuity market

The financial industry has recently seen a push away from structured products and towards transparency. The trend is to decompose products, such that customers understand each component as well as its price. Yet the enormous annuity market combining investment and longevity has been almost untouched by this development.We suggest a simple decomposed annuity structure that enables cost transparency and could be linked to any investment fund. It has several attractive features: (i) it works for any heterogeneous group; (ii) participants can leave before death without financial penalty; and (iii) participants have complete freedom over their own investment strategy.