基于随机基准的动态均值-方差投资组合选择

在不完全市场下, 研究基于随机基准的动态均值-方差投资组合选择问题. 该问题也可以理解为一个跟踪误差动态投资组合问题, 并将之转化为一个等价的考虑风险调整的期望相对收益最大化问题. 利用随机动态规划方法, 给出了最优投资策略和有效前沿的显式表达式. 最后通过实证分析表明了不完全市场和完全市场下最优投资策略和有效前沿的变化, 并对相关结论进行了经济解释.

     

基于多Agent系统的组合投资模型

针对多阶段组合投资问题,提出了一个基于多Agent系统的自调节及协同工作的组合投资策略模型.该模型系统中的各个Agent通过通讯共享知识,在求解问题的搜索空间中进行协同搜索,在更短的搜索步长内得到问题的解,极大地提高了系统性能.该模型具有不基于任何股票模型、时间复杂度低以及逼近最优投资策略速度较快等优点,实验证明具有一定的实际意义.     

铁路工程项目评价系统开发

文章分析了现有铁路投资项目经济评价存在的问题,提出了以应用决策支持系统的形式开发铁路投资项目经济评价系统,并给出了系统的功能模型,着重描述了基础数据处理、报表生成和财务指标计算等问题.系统适用于新建、改扩建和项目后评价,实践表明该系统能有效地对投资项目进行经济评价,具有一定的实用价值.     

供应链系统专用性投资不足的关系合约算法

针对供应链信息系统专用性投资不足的问题,论文建立了激励投资的关系合约算法模型,分析了关系合约可自执行的条件,设计了最优关系合约模型,并实现了求解.研究结果表明运用该算法模型可使专用性投资水平达到帕累托最优.最后,讨论了有关因子对最优关系合约的影响.     

带投资约束P-中位问题的混合蚁群算法

将投资限制引入经典约束p-中位问题,提出带投资的约束P-中位问题,该问题更适用于交通、物流等领域的设施选址。在深入分析带投资约束P-中位问题的数学模型的基础上,首先提出了适用于该问题求解的局部搜索策略;其次,将局部搜索策略与拉格朗日启发式算法和蚁群算法相结合,设计了求解该问题的拉格朗日混合蚁群算法。实验结果表明：带投资的约束P-中位问题能够根据投资金额规划不同的投资方案;且提出的混合蚁群算法较大程度上提高了蚁群算法和拉格朗日启发式算法的求解精度,具有较好的收敛性。     

基于多Agent系统的自调节及协同工作的组合投资模型

Agent技术特别是多Agent系统(MAS,Multi-Agent System)为解决人工智能等领域复杂问题提供了一个新途径,多Agent系统重点研究如何协调系统中的各个Agent的行为使其协同工作.针对多阶段组合投资问题,提出了一个基于多Agent系统的自调节及协同工作的组合投资策略模型.该模型系统中的各个Agent通过通讯共享知识,在求解问题的搜索空间中进行协同搜索,在更短的搜索步长内得到问题的解,极大地提高了系统性能.该模型具有不基于任何股票模型、时间复杂度低以及逼近最优投资策略速度较快等优点,实验证明具有一定的实际意义.     

基于风险价值约束的动态均值-方差投资组合的研究

研究了基于风险价值约束的动态均值-方差项目投资组合的数学模型,该模型是控制带约束的随机线性二次型（LQ）控制问题.在讨论该随机LQ控制问题的解之后,给出投资组合动态数学模型对应的随机哈密顿-雅克比-贝尔曼方程的解,得出了有效边界和最佳策略,讨论了风险价值约束的影响.最后,针对某油田勘探开发项目的实际情况,应用上述结论求出该实例的解,并讨论了风险价值约束发挥的作用.     

竞争环境下B2C平台增值服务投资决策

为提高竞争环境下的平台经济效益,讨论平台企业对双边用户的增值服务投资问题,在考虑用户多归属条件下构建B2C平台竞争模型.通过比较分析发现,在双边单归属或一边多归属条件下,平台企业的最优投资满足一个区间策略:若投资资源小于该区间的下界,则根据边际投资成本小于或大于某一阈值,平台企业选择投资全部或部分资源;若投资资源大于该区间的上界,则最优投资存在两个纯策略均衡;若投资资源位于该区间内,则最优投资存在唯一纳什均衡.此外,在双边多归属条件下,平台企业的最优投资满足一个单阈值策略:根据边际投资成本小于或大于某一阈值,平台企业选择投资全部或部分资源.     

一个风险敏感最优控制问题的随机最大值原理及在投资选择中的应用

在本文, 我们主要研究了一类产生于金融市场中投资选择问题的风险敏感最优控制问题. 用经典的凸变分技术, 我们得到了该类问题的最大值原理. 最大值原理的形式相似于风险中性的情形. 但是, 对偶方程和变分不等式明显地依赖于风险敏感参数 γ. 这是与风险中性情形的主要区别之一. 我们用该结果解决一类最优投资选择问题. 在投资者仅投资国内债券和股票的情况下, 前人用贝尔曼动态规划原理所得的最优投资策略仅是我们结果的特殊形式. 我们也给了一些数值算例和图, 他们显式地解释了最大期望效用和模型中参数的关系.     

动态联盟的一种模糊最优投资策略

针对动态联盟或虚拟企业中两个互补型合作企业, 提出了一种模糊投资决策模型, 该模型同时考虑了企业的相关性和鼓励投资的激励措施应用模糊优化技术对该模型进行求解, 确定了获得最大总利润的最优投资策略 ;并用计算实例证明了该方法的有效性. 为虚拟企业进行科学的投资决策, 提供了有益的借鉴.     

具有不确定收益的新的投资组合优化模型的研究

解决了具有不确定收益的投资组合问题。从一个新的视角给出了不确定投资组合的风险定义,在此基础上,提出了新的投资组合优化模型,并设计出新的混合智能算法来解决这一新的优化问题。在新的算法中,99方法被用来计算期望值和机会值,与之前的算法相比,大大减少了计算的工作量,加快了求解过程。最后,提出一个数值例子来验证新的优化模型和所提算法的可行性和正确性。     

A new perspective for optimal portfolio selection with random fuzzy returns

The aim of this paper is to solve the portfolio selection problem when security returns contain both randomness and fuzziness. Utilizing a different perspective, this paper gives a new definition of risk for random fuzzy portfolio selection. A new optimal portfolio selection model is proposed based on this new definition of risk. A new hybrid intelligent algorithm is designed for solving the new optimization problem. In the proposed new algorithm, neural networks are employed to calculate the expected value and the chance value. These greatly reduce the computational work and speed up the process of solution as compared with the random fuzzy simulation used in our previous algorithm. A numerical example is also presented to illustrate the new modelling idea and the proposed new algorithm.     

A fuzzy portfolio selection method based on possibilistic mean and variance

This paper deals with the portfolio selection problem when the returns of assets obey LR-type possibility distributions and there exist the limits on holdings. A new possibilistic mean–variance model to portfolio selection is proposed based on the definitions of the possibilistic return and possibilistic risk, which can better integrate an uncertain decision environment with vagueness and ambiguity. This possibilistic mean–variance model can be regarded as extensions of conventional probabilistic mean–variance methodology and previous possibilistic approaches since it contains less parameter and has a more extensive application. A numerical example of a possibilistic fuzzy portfolio selection problem is given to illustrate our proposed effective means and approaches. This project was supported by NCET (No.06-0749) and The National Natural Science Foundation of China (No.70571024).     

Mean-variance-skewness model for portfolio selection with transaction costs

A mean-variance-skewness model is proposed for portfolio selection with transaction costs. It is assumed that the transaction cost is a V-shaped function of the difference between the existing portfolio and a new one. The mean-variance-skewness model is a non-smooth programming problem. To overcome the difficulty arising from non-smoothness, the problem was transformed into a linear programming problem. Therefore, this technique can be used to solve large-scale portfolio selection problems. A numerical example is used to illustrate that the method can be efficiently used in practice.     

Fuzzy mean–variance–skewness portfolio selection models by interval analysis

In portfolio selection problem, the expected return, risk, liquidity etc. cannot be predicted precisely. The investor generally makes his portfolio decision according to his experience and his economic wisdom. So, deterministic portfolio selection is not a good choice for the investor. In most of the recent works on this problem, fuzzy set theory is widely used to model the problem in uncertain environments. This paper utilizes the concept of interval numbers in fuzzy set theory to extend the classical mean–variance (MV) portfolio selection model into mean–variance–skewness (MVS) model with consideration of transaction cost. In addition, some other criteria like short and long term returns, liquidity, dividends, number of assets in the portfolio and the maximum and minimum allowable capital invested in stocks of any selected company are considered. Three different models have been proposed by defining the future financial market optimistically, pessimistically and in the combined form to model the fuzzy MVS portfolio selection problem. In order to solve the models, fuzzy simulation (FS) and elitist genetic algorithm (EGA) are integrated to produce a more powerful and effective hybrid intelligence algorithm (HIA). Finally, our approaches are tested on a set of stock data from Bombay Stock Exchange (BSE).     

Fuzzy R&D portfolio selection of interdependent projects

Global competition of markets has forced firms to invest in targeted R&D projects so that resources can be focused on successful outcomes. A number of options are encountered to select the most appropriate projects in an R&D project portfolio selection problem. The selection is complicated by many factors, such as uncertainty, interdependences between projects, risk and long lead time, that are difficult to measure. Our main concern is how to deal with the uncertainty and interdependences in project portfolio selection when evaluating or estimating future cash flows. This paper presents a fuzzy multi-objective programming approach to facilitate decision making in the selection of R&D projects. Here, we present a fuzzy tri-objective R&D portfolio selection problem which maximizes the outcome and minimizes the cost and risk involved in the problem under the constraints on resources, budget, interdependences, outcome, projects occurring only once, and discuss how our methodology can be used to make decision support tools for optimal R&D project selection in a corporate environment. A case study is provided to illustrate the proposed method where the solution is done by genetic algorithm (GA) as well as by multiple objective genetic algorithm (MOGA).     

Portfolio selection with the effect of systematic risk diversification: formulation and accelerated gradient algorithm

In this paper, we propose an optimal trade-off model for portfolio selection with the effect of systematic risk diversification, measured by the maximum marginal systematic risk of all the risk contributors. First, the classical portfolio selection model with constraints on allocation of systematic risk is shown to be equivalent to our trade-off model under certain conditions. Then, we transform the trade-off model into a special non-convex and non-smooth composite problem equivalently. Thus a modified accelerated gradient (AG) algorithm can be introduced to solve the composite problem. The efficiency of the algorithm for solving the composite problem is demonstrated by theoretical results on both the convergence rate and the iteration complexity bound. Finally, empirical analysis demonstrates that the proposed model is a preferred tool for active portfolio risk management when compared with the existing models. We also carry out a series of numerical experiments to compare the performance of the modified AG algorithm with the other three first-order algorithms.     

A portfolio selection model with borrowing constraint based on possibility theory

Compared with the conventional probabilistic mean-variance methodology, fuzzy number can better describe an uncertain environment with vagueness and ambiguity. In this paper, the portfolio selection model with borrowing constraint is proposed by means of possibilistic mean, possibilistic variance, and possibilistic covariance under the assumption that the returns of assets are fuzzy numbers. And a quadratic programming model with inequality constraints is presented when the returns of assets are trapezoid fuzzy numbers. Furthermore, Lemke algorithm is utilized to solve the model. Finally, a numerical example of the portfolio selection problem is given to illustrate our proposed effective means and variances. The results of the numerical example also show that the investor can make different decisions according to different requirements for the values of expected returns. And the efficient portfolio frontier of the model with borrowing constraints can be easily obtained.     

Distributionally robust fuzzy project portfolio optimization problem with interactive returns

Effective project selection and staff assignment strategies directly impact organizational profitability. Based on critical value optimization criterion, this paper discusses how uncertainty and interaction impact the project portfolio return and staff allocation. Since the exact possibility distributions of uncertain parameters in practical project portfolio problems are often unavailable, we adopt variable parametric possibility distributions to characterize uncertain model parameters. Furthermore, this paper develops a novel parametric credibilistic optimization method for project portfolio selection problem. According to the structural characteristics of variable parametric possibility distributions, we derive the equivalent analytical expressions of credibility constraints, and turn the original credibilistic project portfolio model into its equivalent nonlinear mixed-integer programming models. To show the advantages of the proposed parametric credibilistic optimization method, some numerical experiments are conducted by setting various values of distribution parameters. The computational results support our arguments by comparing with the optimization method under fixed possibility distributions.     

风险效用投资组合模型的遗传算法求解

给出一个折衷考虑风险最小化和收益最大化的单目标决策方法,以单位风险收益最大化为决策目标建立了投资组合的非线性分式规划模型,考虑到分式规划问题的求解难度,利用遗传算法求解模型,并给出算法步骤。最后,给出了数值算例,结果表明该算法是简单有效的。