An Investment Decision Model with the Survival Probability Criterion and its Numerical Solutions: The Finite Horizon Case

This paper studies a finite horizon investment decision model. Suppose that an investor is endowed with initial wealth in the beginning. At every stage, he needs to consume a part of his wealth and allocate the rest between a risky and a riskless asset. The investor wishes to maximize the survival probability that his wealth can satisfy the consumption requirements during the horizon and reach a disaster level at the end. Since the allocation decision depends on not only his wealth but also the disaster level, we introduce a Markov decision process based on decision space to describe the investment behavior of the investor and prove the existence of a deterministic Markov optimal policy. An algorithm to compute the optimal policy and the maximal probability of survival is given and four numerical examples are discussed.     

Mean-variance model for portfolio optimization with background risk based on uncertainty theory

The aim of this paper is to develop a mean-variance model for portfolio optimization considering the background risk, liquidity and transaction cost based on uncertainty theory. In portfolio selection problem, returns of securities and assets liquidity are assumed as uncertain variables because of incidents or lacking of historical data, which are common in economic and social environment. We provide crisp forms of the model and a hybrid intelligent algorithm to solve it. Under a mean-variance framework, we analyze the portfolio frontier characteristic considering independently additive background risk. In addition, we discuss some effects of background risk and liquidity constraint on the portfolio selection. Finally, we demonstrate the proposed models by numerical simulations.     

基于FDH的分区域多目标遗传算法

提出了一种基于FDH的分区域多目标遗传算法（FDH MOGA）。该算法通过FDH对种群中所有个体进行评价,根据评价所得的效率值和拥挤度对种群进行选择,提高了该算法的局部搜索能力,同时引入分区策略增加算法的搜索范围,有效避免了遗传算法早熟的缺陷,提高了所获解的多样性。对多个测试函数以及投资组合优化问题的测试结果显示,FDH MOGA算法具有良好的计算性能,更具有效性。     

Using business critical design rules to frame new architecture introduction in multi-architecture portfolios

When introducing new architectures to an industrial portfolio, counting multiple existing product and manufacturing solutions, time-to-market and investments in manufacturing equipment can be significantly reduced if new concepts are aligned with the existing portfolio. This can be done through component sharing, or sharing critical design principles. This alignment is not trivial, as extensive design knowledge is needed to overview a portfolio with many, often highly different products and manufacturing lines. In this paper, we suggest establishing a frame of reference for new-product introduction based on several ‘game rules’, or Business Critical Design Rules (BCDRs), which denote the most critical features of the product and manufacturing architectures, and should be considered an obligatory reference for design when introducing new architectures. BCDRs are derived from the portfolio, architecture and module levels, including modelling of the most critical links between the product and manufacturing domains. The suggested modelling principle has been tested as a frame for new-architecture introduction, capturing critical modularisation principles in a large and global OEM. Application of the suggested method revealed a potential for reducing time-to-market and potentially cutting 35% off investments in new manufacturing equipment when introducing new products in the portfolio.     

基于改进NSGA2算法的配电网项目组合优选方法研究

电力体制改革新形势以及电网发展的新阶段,要求电网企业加强电网的精准化投资。面对电网投资项目资金需求不断加大而资金总额有限的情况,需要对电网项目进行多目标组合优选以实现综合效益最大化。为此以配电网项目为研究对象,提出一种基于改进的第二代非支配排序遗传算法(non-dominated sorting genetic algorithm 2,NSGA2)的项目多目标组合优选方法。基于灰色预测方法计算总投资情况,从安全性、经济性、紧迫程度3个属性构建配电网项目综合评价体系,对项目进行综合评价;设定多个目标函数及约束条件,通过网格搜索改进NSGA2算法进行项目组合优选,使算法具有更强的全局搜索能力和收敛能力,实现满足多目标的项目组合优选;最后通过算例分析,验证上述多目标项目组合优选方法的有效性及可操作性,对提高配电网精准投资管理水平具有指导意义。     

基于整数规划的水利企业项目组合管理研究

目前,多数水利企业为了获得更高的经济效益已不再仅仅对单个水利项目进行单独管理,而是在企业总体战略目标的指导下对多个相关项目进行组合管理。根据项目之间的相关性,利用整数规划算法构建了水利企业在采用项目组合管理时进行项目选择的数学模型,从而筛选出最优的项目组合,实现企业效益最大化的目标。以某水利企业为例,对其中标的若干个工程项目进行组合管理,选择使其获得最大利润的项目组合。由此阐明在水利企业中应用项目组合管理的适用性和科学性。     

Multistage portfolio optimization with stocks and options

We develop a multistage portfolio optimization model that utilizes options for mitigating market risk in a dynamic setting. Due to the key role of scenarios in the quality of investment decisions, a new scenario generation method is proposed that characterizes the dynamic behavior of asset returns. This methodology takes the dependence structure of different asset returns into account, and also considers serial correlations of each of the asset returns. Moreover, it preserves marginal distributions of asset returns. Also, it precludes arbitrage opportunities. To investigate the role of options, we implement the scenario generation method on a set of stocks selected from the New York Stock Exchange. Results show the high performance of the proposed scenario generation method. Afterwards, the generated set of scenarios is used as the uncertainty set for the multistage portfolio optimization model. Static and dynamic assessments are used for measuring the performance of options in mitigating market risks and generating additional returns. Finally, backtesting simulations are used for assessing different trading strategies of options.     

A parallel portfolio approach to configuration optimization for large software product lines

Software product line (SPL) engineering demands for optimal or near‐optimal products that balance multiple often competing and conflicting objectives. A major challenge for large SPLs is to efficiently explore a huge space of various products and satisfy a large number of predefined constraints simultaneously. To improve the optimality and convergence speed, we propose a parallel portfolio approach, called IBEAPORT, which designs three algorithm variants by incorporating constraint solving into the indicator‐based evolutionary algorithm in different ways and performs these variants by utilizing parallelization techniques. Our approach utilizes the exploration capabilities of different algorithms and improves optimality as far as possible within a limited time budget. We evaluate our approach on five large‐scale real‐world SPLs. Empirical results demonstrate that our approach significantly outperforms the state of the art for all five SPLs on a quality indicator and a diversity indicator. Moreover, IBEAPORT quickly converges to a relatively stable hypervolume value even for the largest SPL with 6888 features.     

基于ArchimedeanCopula方法的VaR估计

应用了ArchimedeanCopula方法计算了投资组合VaR值,介绍了该方法函数的选择及参数估计,通过对上证综指和浦发银行2只股票的收益率投资组合的VaR计算表明,由GumbelCopula模拟得出的结果与实际数据差距小,能很好地度量股市间的风险.     

基于微粒群算法的投资组合分析

介绍了将微粒群算法应用于求解均值．方差一峰度投资组合模型,分析了模型中的参数和求解结果之间的关系,并选取深交所4只股票来进行模拟仿真,最后仿真的结果说明微粒群算法对均值一方差．峰度模型是有效的。