具指数赋权指标的证券投资多目标线性规划模型

本文提出证券投资决策的指数赋权指标体系．在该指标体系中,建立风险证券组合投资决策和存在无风险证券或无风险贷款时证券组合投资决策的多目标线性规划模型．研究了有效风险证券组合集和有效证券组合集的结构和相互关系,市场证券组合以及证券均衡市场价格和投资风险分析．     

不允许卖空的组合证券投资决策方法研究

根据组合证券投资决策模型，研究了不允许卖空的组合证券投资的有效边界及其性质，给出了不允许卖空情况下组合证券投资决策方法。     

时变β值证券组合投资决策模型研究

考虑证券收益的时变特性,将证券收益率看成随机序列,以β值证券组合投资决策模型为理论基础,提出了β值时变证券组合投资决策方法     

一类投资组合优化问题的求解及实证分析

在证券投资组合优化的决策问题中,投资者通过选取不同的证券分散风险,为了使分散化的利益最大化,还须考虑证券组合的最佳规模以及交易成本。本文在给出求解这类问题的一种计算方法的基础上,进行实证分析。这里所用的方法以及结果,也适合于其他各种具有风险的投资决策问题。     

投资组合模型的分式规划解法

以E-SV风险测度为基础提出了组合证券投资决策的效用函数,并建立了基于分式规划的投资组合选择模型,利用变换,把求解分式规划的问题转化为求解非分式规划问题。     

目标规划法在证券组合投资中的应用

证券投资是目前我国经济中的一大热点。本以Markowitz证券组合投资理论为基础，运用目标规划的方法建立一种新的证券组合投资决策模型。在本模型中综合考虑了证券组合的收益，风险，交易费用等因素，对投资选择有效证券组合有一定的实用价值。     

一种基于区间数的证券组合投资模型与求解

提出了区间数的相对左偏度的定义.利用区间数的相对左偏度作为区间数下表达证券风险损失率的一种补充,能合理地反映风险损失率与预期收益率之间的相关关系.建立了一种新的证券组合投资区间数规划模型,将区间数规划模型转化为参数线性规划问题求解,使证券组合投资决策分析更加具有柔性.最后通过实例分析了该模型的应用价值.     

研究国际证券投资及其有效集的多目标线性规划方法

本文给出国际证券组合投资决策的多目标线性规划模型，以及求解有效国际证券组合的偏好系数加权法．在此基础上，应用线性多数规划技术研究有效国际证券组合集的几何特征，并给出相应结论和简单算例．     

基于区间数的证券组合投资模型研究

提出了证券组合投资的区间数线性规划模型.通过引入区间数线性规划问题中的目标函数优化水平α和约束水平β将目标函数和约束条件均为区间数的线性规划问题转化为确定型的线性规划问题.投资者可以根据自己的风险喜好程度和客观情况,对这两个参数做出不同的估计,从而得到相应情况下的有效投资方案,使证券组合投资决策更具柔性.最后通过实例分析说明了该模型的可行性.     

使用MTV模型与Morkowitz证券组合选择模型的投资决策系统

利用期望和方差的秩系数法、MTV模型、Morkowitz证券组合选择模型构造了一实用的投资决策系统.     

Optimal portfolio choice for an insurer with loss aversion

The problem of optimal investment for an insurance company attracts more attention in recent years. In general, the investment decision maker of the insurance company is assumed to be rational and risk averse. This is inconsistent with non fully rational decision-making way in the real world. In this paper we investigate an optimal portfolio selection problem for the insurer. The investment decision maker is assumed to be loss averse. The surplus process of the insurer is modeled by a Lévy process. The insurer aims to maximize the expected utility when terminal wealth exceeds his aspiration level. With the help of martingale method, we translate the dynamic maximization problem into an equivalent static optimization problem. By solving the static optimization problem, we derive explicit expressions of the optimal portfolio and the optimal wealth process.     

基于模拟植物生长算法的协作研发网络决策优化模型

在协作研发网络决策中,合理的投资组合可使企业获得理想的收益。企业协作研发网络的投资组合是多方博弈后的结果,利用模拟植物生长算法构建的优化模型可以分析企业在网络中投资组合的博弈过程。通过模拟植物生长算法计算得到的全局最优解和局部最优解是企业协作研发决策投资组合的最优决策集。企业可以根据策略集调整自身的投资方式,制定最优的决策方案。     

一类组合投资问题的线性规划解法

根据选定总体风险的一个上界值使组合投资的收益率达到最大的原则，并在合理简化的基础上建立组合投资决策问题的线性规划模型。然后通过算例求解带有参数的线性规划问题，给出资产组合的风险控制值和相应的最大净收益率及投资比例向量的关系。     

Decision Analysis in Venture Capital

A number of decision criteria are evaluated in the context of a portfolio of investments in venture capital.The essence of this activity is that it is a sequential decision problem with stochastic time intervals between decisions, where the choice is to make an investment or not. The pay-off from the investment is a payment of random magnitude after a time interval of random length.Some of the criteria used in security portfolio analysis are adapted for use in this context and compared with simpler criteria. Due to the typically risky nature of these investments, the level of information about the investment is kept to a realistic level.The results of simulation experiments demonstrate effectively the benefit of using rational decision criteria, but that there is surprisingly little difference between the effects of these criteria.     

考虑保守卖空与财务困境的背景风险投资组合模型

如何合理地考虑投资者所面临的背景风险及现实市场限制来进行有效地投资决策是人们所广泛关注的重要实际管理决策问题。本文研究投资者同时面临加性和乘性两类背景风险的前提下具有保守卖空与财务困境的投资组合选择问题。假定投资者寻求使得投资收益最大、投资风险最小及证券主体财务困境最小的最优投资组合策略,进而提出考虑保守卖空与财务困境的背景风险投资组合模型。然后,利用具有精英策略的非支配排序遗传算法对模型进行求解。最后,通过实例来阐述模型的实用性。研究结果表明:考虑保守卖空能为投资者提供更大的收益;两类背景风险的变化均导致有效前沿面的变化。     

The optimal resource portfolio under consumer choice and demand risk for vertically differentiated products

We study the optimal resource portfolio of a firm that sells two vertically differentiated products and utilizes resource flexibility and responsive pricing. We model this decision problem as a two-stage stochastic programming problem with recourse: In the first stage, the firm determines its resource mix and capacities so as to maximize the expected profit under demand uncertainty; in the second stage, uncertainty is resolved and the firm determines its production and pricing decision, constrained by its investment decision. We show that the objective function of this decision problem is not well-behaved (ie, it may have multiple local maxima). Using the concept of Pareto dominance, we reduce the feasible investment region, without loss of optimality, to one in which the objective function is well-behaved everywhere. This reduction allows us to derive the necessary and sufficient conditions for the optimal capacity decision and to gain insights.     

损失厌恶偏好与资产组合选择研究

在不确定性条件下,期望的不可计算性、行动结果比较的局限性以及投资个体选择的非理性使理性假定的选择理论脱离现实,因此重新探讨决策选择准则是必要的.以行为金融理论中不确定性状态下的有限理性与满意准则为依据,引入与满意准则一致且体现损失厌恶偏好的VaR作为风险指标,构建行为资产组合模型,在一种简单新颖的M-V模型的矩阵解法基础上,探寻了正态与部分非正态性假设下VaR-BPT模型的显性最优解或有效前沿,解决了现实中最优投资组合选择的可操作性难题,并在中国股票市场验证了正态性转换方法是处理非正态分布下资产组合选择问题的一种优秀方法.     

An a posteriori decision support methodology for solving the multi-criteria supplier selection problem

This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.     

基于CaR风险约束的动态投资组合模型

研究了带有风险约束的动态投资组合优化问题.在Black-Scholes型金融市场下,引入了在险资本(Captical at risk,CaR)风险约束,与以往文献的风险约束仅仅施加于终端时点不同,该模型将风险约束施加于每一个交易区间.即利用条件信息不断地对风险进行重新评估,从而对投资决策连续地施加影响.利用动态规划技术和优化理论,在合理的假定下,从理论上对问题进行了分析,给出了最优投资策略的显式表达式,并与无风险约束情形进行了比较.最后给出了一些数值例子进行说明.     

Mean-chance model for portfolio selection based on uncertain measure

This paper discusses a portfolio selection problem in which security returns are given by experts’ evaluations instead of historical data. A factor method for evaluating security returns based on experts’ judgment is proposed and a mean-chance model for optimal portfolio selection is developed taking transaction costs and investors’ preference on diversification and investment limitations on certain securities into account. The factor method of evaluation can make good use of experts’ knowledge on the effects of economic environment and the companies’ unique characteristics on security returns and incorporate the contemporary relationship of security returns in the portfolio. The use of chance of portfolio return failing to reach the threshold can help investors easily tell their tolerance toward risk and thus facilitate a decision making. To solve the proposed nonlinear programming problem, a genetic algorithm is provided. To illustrate the application of the proposed method, a numerical example is also presented.