Fuzzy portfolio selection using genetic algorithm

This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.     

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

In this paper,we mainly study a kind of risk-sensitive optimal control problem motivated by a kind of portfolio choice problem in certain financial market.Using the classical convex variational technique,we obtain the maximum principle for this kind of problem.The form of the maximum principle is similar to its risk-neutral counterpart.But the adjoint equation and the variational inequality heavily depend on the risk-sensitive parameterγ.This is one of the main difference from the risk-neutral case.We use this result to solve a kind of optimal portfolio choice problem.The optimal portfolio strategy obtained by the Bellman dynamic programming principle is a special case of our result when the investor only invests the home bond and the stock.Computational results and figures explicitly illustrate the relationships between the maximum expected utility and the parameters of the model.     

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.     

Optimal consumption and portfolio choice with ambiguity and anticipation

This paper, adopting the recursive multiple-priors utility, studies the optimal consumption and portfolio choice in a Merton-style model with anticipation when there is a difference between ambiguity and risk. The fundamental issue is what the effects of ambiguity and anticipation on the investor’s behavior are. In the case of a logarithmic felicity function, the paper also shows that no hedging demand arises that is affected by both ambiguity and anticipation. Finally, the optimal portfolio is derived in terms of Malliavin derivatives and stochastic integrals.     

高速公路路网动态规划方法的研究与设计

在高速公路路网布局的确定中,一是节点重要度的确定,另一是路网中各条线路联接和组合的方法。基于某环岛高速公路的勘察数据,提出动态规划的路网规划设计的框架。在规划框架下,重点研究了动态规划的思想。最后对路网中各条路段和线路进行联接、组合,建立了规划方法的数学模型,并设计了相应的算法。     

概率准则下房地产组合投资的选择模型

将组合投资理论引入房地产投资领域，在假设收益率为正态分布的组合投资选择模型的基础上，将实现预期收益的概率作为目标函数，利用Edgeworth展式来逼近目标函数；给出了收益率为非正态分布的房地产组合投资选择模型，并进一步提出了考虑无风险资产的房地产组合投资的概率选择模型。     

基于聚类的股票波动分析及其应用

Markowitz提出的“期望均值收益-收益方差“规则(M-V),模型要求选择差异性较大的资产进行组合,从而在给定收益率水平下,降低组合的风险。在M-V模型的基础上,采用了数据挖掘中聚类的方法,定义出一种衡量时间序列样本之间相似性程度的指标,这个指标反应了股票间波动行情趋势的异同。在此基础上对资产价格序列性进行聚类分析,与单纯M-V模型相比,在给定的收益率水平下降低了资产组合的风险。采用上证指数中若干股票进行实验验证表明,在给定的收益率下,采用基于密度的层次聚类方法的股票组合可以得到比随机组合更小的风险水平。     

基于Telser安全优先模型的项目投资组合研究

安全性和效益性是项目投资的两项重要原则,Telser安全优先模型兼顾了这两方面。结合我国项目管理的实践,建立了项目投资组合的安全优先模型并对模型解的情况进行深入探讨。预先设定的底限收益水平和项目组合小于底限水平的概率是项目投资组合管理的关键参数,通过Telser安全优先模型可以求解出同时实现安全和效益目标的项目投资组合。     

Uncertain programming models for portfolio selection with uncertain returns

In an indeterminacy economic environment, experts’ knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.     

A class of continuous-time portfolio selection with liability under jump-diffusion processes

A continuous-time mean-variance portfolio selection model is formulated with multiple risky assets and one liability under discontinuous prices which follow jump-diffusion processes in an incomplete market. The correlations between the risky assets and the liability are considered. The corresponding Hamilton–Jacobi–Bellman equation of the problem is presented. The optimal dynamic strategy and the efficient frontier in closed forms are derived explicitly by using stochastic linear-quadratic control technique. Finally, the effects on efficient frontier under the value-at-risk constraint are illustrated.