基于直觉模糊规划的多目标投资组合选择模型

将直觉模糊集合的概念引入投资组合模型中,并将多目标投资组合模型中的收益、方差和偏度三个目标模糊化,用隶属函数与非隶属函数作为新的目标函数.针对该模糊多目标投资组合模型,提出了一个动态遗传算法,算例给出了该模型的一个实例的最优解.     

可调整的均值-半方差可信性投资组合绩效评价

本文提出具有实际约束的可调整的均值-半方差可信性投资组合优化模型。基于可信性理论,将上述模型转化为非线性规划问题。当考虑实际投资约束情况,如交易成本、交易量限制和借款限制的影响,投资组合优化模型非常复杂,难以获得真实前沿面的解析解,这给投资组合的绩效评价带来很大的困难。本文提出基于数据的实际约束的均值-半方差可信性投资组合DEA评价模型,进而利用DEA模型的前沿面来逼近一般情形下真实的前沿面。最后,通过上海证券市场的实际数据验证了本文方法的可行性和实用性。     

具有现实约束的均值-方差模糊投资组合绩效评价

本文提出了具有实际约束的均值-方差模糊投资组合优化模型。由于实际投资约束情况,如交易成本、交易量限制、借款限制和基数约束的影响,投资组合优化模型非常复杂,难以获得真实前沿面的解析解,这给投资组合理论的应用带来了很大的困难。基于数据的实际约束的均值-方差模糊投资组合DEA评价模型,文章通过构造前沿面来逼近一般情形下真实的前沿面。最后,通过上海证券市场的实际数据验证了本文方法的合理性与可行性。     

均值-半绝对偏差区间投资组合绩效评价

本文构建了考虑现实约束的均值-半绝对偏差区间投资组合优化模型。由于存在实际投资约束,如交易成本、交易量限制和借款限制,投资组合优化模型相对复杂,不易获得真实前沿面的解析解,使得投资组合理论的应用难度加大。为了求解模型,引入DEA方法,构建均值-半绝对偏差区间投资组合DEA评价模型,通过构造前沿面来逼近真实前沿面。最后,使用上海证券市场的实际数据验证了本文方法的合理性与可行性。     

摩擦市场条件下的双目标投资组合模型模糊优化

首先建立了摩擦市场条件下基于收益率分布偏度水平的双目标投资组合模型.在此基础上,将模糊集合的概念引入到该模型中,用模糊数学中的线性隶属函数处理了其中的风险目标和收益目标,建立了摩擦市场条件下基于收益率分布偏度水平的模糊型双目标投资组合模型.然后,针对该模型进行了新型遗传算法设计(动态遗传算法).最后用一个具体的算例给出了该模型的一个实例最优解,体现了多样化投资分散风险的组合投资原理.     

项目投资组合的风险度及其最优决策

本文针对项目组合投资问题引入了风险度概念，并建立其风险度模型．在无零风险度项目的情况下，给出了该模型的最优项目组合投资策略并证明该策略为马氏有效．在有零风险度项目的情况下，讨论了该模型的有效前沿的结构、性质和有效性，同时还论证了该模型的有效前沿与威廉·夏普提出的资本资产定价模型的有效前沿相一致的线性关系．最后作为本模型的应用，构造了”保证还本”模型，给出了其最优项目投资组合的策略．     

共同资金投资组合的有效边界与最优策略

本文利用均值方差模型,分析了非线性交易成本下的共同资金投资的有效边界和在一般的效用函数下讨论了最优投资组合和最大效用,其中只考虑风险资产的总投资比例对交易成本的影响.     

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.     

求解带有交易费的CVaR投资组合模型的L-S算法

本文假设投资者是风险厌恶型,用CVaR作为测量投资组合风险的方法.在预算约束的条件下,以最小化CVaR为目标函数,建立了带有交易费用的投资组合模型.将模型转化为两阶段补偿随机优化模型,构造了求解模型的随机L-S算法.为了验证算法的有效性,用中国证券市场中的股票进行数值试验,得到了最优投资组合、VaR和CVaR的值.而且对比分析了有交易费和没有交易费的最优投资组合的不同,给出了相应的有效前沿.     

摩擦市场中最优证券组合选择的极大极小法

在考虑交易成本的基础上,构造最优投资组合选择的极大极小模型,同时允许投资者卖空风险资产.在求解过程中,针对出现的非光滑函数,通过引入极大熵函数用光滑问题来逼近非光滑问题.最后推导出连续可微的方程组,可采用经典牛顿法求解.数值分析验证了该方法的有效性.     

二阶随机占优约束下考虑偏度的投资组合模型

在DentchevaRuszczynski(2006)模型的基础上,考虑偏度对构建投资组合的影响,建立了二阶随机占优约束下最大化组合收益率偏度的投资组合优化模型,并应用分段线性近似方法将模型转化为一个非线性混合整数规划问题.利用中国股票市场的历史数据对所建模型进行了实证分析,结果表明,所建新模型比均值-方差-偏度模型和市场指数具有更稳健的表现.     

Genetic algorithms for portfolio selection problems with minimum transaction lots

Conventionally, portfolio selection problems are solved with quadratic or linear programming models. However, the solutions obtained by these methods are in real numbers and difficult to implement because each asset usually has its minimum transaction lot. Methods considering minimum transaction lots were developed based on some linear portfolio optimization models. However, no study has ever investigated the minimum transaction lot problem in portfolio optimization based on Markowitz’ model, which is probably the most well-known and widely used. Based on Markowitz’ model, this study presents three possible models for portfolio selection problems with minimum transaction lots, and devises corresponding genetic algorithms to obtain the solutions. The results of the empirical study show that the portfolios obtained using the proposed algorithms are very close to the efficient frontier, indicating that the proposed method can obtain near optimal and also practically feasible solutions to the portfolio selection problem in an acceptable short time. One model that is based on a fuzzy multi-objective decision-making approach is highly recommended because of its adaptability and simplicity.     

Multi-objective possibilistic model for portfolio selection with transaction cost

In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.     

Geometric representation of the mean–variance–skewness portfolio frontier based upon the shortage function

The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean–variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine mean–variance–skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the geometric representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop geometric representations of the MVS portfolio frontier using the shortage function and related approaches.     

含交易费用的证券组合投资模型的满意解

在证券组合投资过程中,忽略交易费用会导致非有效的证券组合投资,本文提出了一个考虑交易费用的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参数λ和约束条件满足水平参数η将目标函数和约束条件均为区间数的区间数线性规划模型转化为确定型的一般线性规划模型,进而求得相应于优化水平λ和满足水平η的满意解.     

Portfolio rebalancing model using multiple criteria

In order to achieve greater flexibility in portfolio selection, transaction cost, short selling and higher moments should be considered, and actual transactions should be reflected. In this paper, five portfolio rebalancing models, with consideration of transaction cost and consisting of some or all criteria, including risk, return, short selling, skewness, and kurtosis, are compared to determine the important design criteria for a portfolio model. Two examples are used to perform simulated transactions, and the results indicate that the investment strategy of ‘buy and hold’ does not produce better returns for all the portfolios in the first example, and the models with higher moments or adopting short selling strategy perform better while rebalancing in the second example.     

基于模糊系数的投资组合优化模型及其实证分析

采用模糊数处理不确定性信息．以模糊期望收益率最大为目标函数,使总的风险不高于给定的模糊数,建立了一种新的模型．在给定的截集下,期望收益率转化为区间数,目标函数转化为对该区间数的下限求最大值．基于模糊数大小的概率比较,从而将模糊优化模型转化为不等式约束下的线性规划模型．利用Matlab编程可解得其最优解．最后通过实例分析...     

VaR optimal portfolio with transaction costs

We consider the problem of portfolio optimization under VaR risk measure taking into account transaction costs. Fixed costs as well as impact costs as a nonlinear function of trading activity are incorporated in the optimal portfolio model. Thus the obtained model is a nonlinear optimization problem with nonsmooth objective function. The model is solved by an iterative method based on a smoothing VaR technique. We prove the convergence of the considered iterative procedure and demonstrate the nontrivial influence of transaction costs on the optimal portfolio weights.     

Portfolio selection with a minimax measure in safety constraint

In this paper, we attempt to design a portfolio optimization model for investors who desire to minimize the variation around the mean return and at the same time wish to achieve better return than the worst possible return realization at every time point in a single period portfolio investment. The portfolio is to be selected from the risky assets in the equity market. Since the minimax portfolio optimization model provides us with the portfolio that maximizes (minimizes) the worst return (worst loss) realization in the investment horizon period, in order to safeguard the interest of investors, the optimal value of the minimax optimization model is used to design a constraint in the mean-absolute semideviation model. This constraint can be viewed as a safety strategy adopted by an investor. Thus, our proposed bi-objective linear programming model involves mean return as a reward and mean-absolute semideviation as a risk in the objective function and minimax as a safety constraint, which enables a trade off between return and risk with a fixed safety value. The efficient frontier of the model is generated using the augmented -constraint method on the GAMS software. We simultaneously solve the ratio optimization problem which maximizes the ratio of mean return over mean-absolute semideviation with same minimax value in the safety constraint. Subsequently, we choose two portfolios on the above generated efficient frontier such that the risk from one of them is less and the mean return from other portfolio is more than the respective quantities of the optimal portfolio from the ratio optimization model. Extensive computational results and in-sample and out-of-sample analysis are provided to compare the financial performance of the optimal portfolios selected by our proposed model with that of the optimal portfolios from the existing minimax and mean-absolute semideviation portfolio optimization models on real data from S&P CNX Nifty index.