证券数增加情形下证券组合有效边缘特征灵敏度分析

本文较为详细地讨论了当证券市场不存在无风险收益证券且允许卖空时证券数的增加对 M-V证券组合有效边缘及其特征的影响 ,给出了有效边缘、渐近线斜率、全局最小方差证券组合及其协方差、最小方差证券组合的投资权数等的变化模式     

最小方差组合证券集的性质

从分析最小方差组合证券集入手 ,研究了均值方差有效组合证券边界的性质 ,给出最小方差组合证券集是一个仿射集 ,并且对有效组合证券结构的统计特性进行了分析 ,对证券投资有一定的指导意义     

证券数增加情形下证券组合有效边缘特征灵敏度分析

本较为详细地讨论了当证券市场不存在无风险收益证券且允许卖空时证券数的增加对M－V证券组合有效边缘及其特征的影响，给出了有效边缘、渐近线斜率、全局最小方差证券组合及其协方差、最小方差证券组合的投资权数等的变化模式。     

变动基数投资组合中的系统误差与估计误差权衡

投资组合选择中的系统误差与估计误差是决定样本期外绩效的重要因素,其权衡受到资产基数N的影响。本文在变动基数的设定下,将Bootstrapping和样本期外滚动的方法应用到均权重、最小方差组合及其误差修正策略的绩效和尾部风险检验过程中,并在不同的市场状态下进行分组讨论。研究发现:(1)最小方差组合与均权重策略的样本期外夏普比率差异与N存在倒U型的关系。(2)最小方差组合的尾部风险随N的扩大而迅速降低,总体来看最小方差组合的尾部风险低于均权重策略。(3)最小方差组合的换手率与N存在正相关关系,盲目增加投资组合选择中的资产基数会带来无谓损失。研究结果表明,投资者应理性选择资产基数,充分利用最小方差组合带来的分散化收益。     

不确定条件下具有容差项的Markowitz证券组合投资模型的最优化解法

在分析证券市场中证券组合投资不确定性质的基础上,通过对Markowitz模型中证券期望收益与方差引入容差项来度量证券市场的不确定性,建立了不确定条件下具有容差项的Markowitz证券组合投资模型;分类讨论了容差的上界与下界所对应的两类有效组合前沿,得到了不确定条件下的证券组合投资模型的最优化解法及相关定理;最后给出了一个具体的数值实例．     

证券投资组合的原理及其应用

本文利用概率统计原理对证券投资组合能减轻所遇风险带来的损失作了深刻的讨论,并介绍了多种证券投资组合方案的选择及如何在多种证券中选出几种进行投资组合     

一种证券组合选择模型

本文在Ｍａｒｋｏｗｉｔｚ组合证券投资决策模型基础上提出了一种可产生更优组合证券投资策略的证券组合选择模型，研究了它的解的结构、它的有效边界的构成。     

组合证券投资优化模型的比较研究

本文给出基于历史收益率数据的均值一极差和均值一离差型组合证券投资优化模型,并用实例对两模型的结果进行比较。     

最小方差准则下一个未定权益的近似对冲

本文研究了不完备的离散时间股票市场下未定权益的定价的对冲问题.利用在最小方差准则下选择概率测度Q或权重函数LN来求最优投资组合的方法,给出了离散时间情况下的鞅表示定理,在最小方差准则下提供一个简单的方法来近似对冲一个未定权益或一个欧氏期权.     

投资比例非负约束的风险证券组合有效集及动态分析

本文提出了风险证券有效组合的决策模型 ,给出了投资比例非负约束的风险证券有效组合的解析表示 ,研究了证券个数变动对证券组合有效集的影响 .分析了它的漂移方向和漂移范围 ,给出了最小风险有效证券组合和最大收益有效证券组合的漂移距离及风险与收益的增加或减少程度     

分形统计测度构建及其在投资组合中的应用研究

准确测量证券的风险和收益无论是对投资管理,还是对金融理论研究,甚至对理论成果向实践应用转化都至关重要。本文在证券价格具有分形特征的现实背景下,基于分形理论构建了分形期望和分形方差两个分形统计测度,以克服非分形统计测度在风险收益方面测不准或不可测的缺陷。在此基础上,应用分形统计测度构建了投资组合模型,给出了分形组合模型的解析解;随后,利用实证分析验证了分形统计测度在投资组合应用中的有效性。本文创新之处在于针对证券价格具有分形特征的现实背景构建了分形期望和分形方差两个分形统计测度;并基于分形统计测度构建了投资组合模型,将证券价格普遍存在的分形特征纳入投资组合的研究框架。     

限制投资下界的风险证券有效组合模型及算法研究

本文研究了具有投资下界限制的风险证券有限组合决策问题，提出了限制投资下界的风险证券有效组合优化模型，在一定的条件下，给出了风险证券有限组合投资比例的算法及解析表示，最后进行了实际数值计算，结果说明了所给算法是有效和实用的。     

A mixed R&D projects and securities portfolio selection model

The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integer stochastic programming model. Moreover, we use semi-absolute deviation risk functions to measure the risk of mixed asset portfolio. Based on the idea of moments approximation method via linear programming, we propose a scenario generation approach for the mixed single-stage R&D projects and multi-stage securities portfolio selection problem. The bi-objective mixed-integer stochastic programming problem can be solved by transforming it into a single objective mixed-integer stochastic programming problem. A numerical example is given to illustrate the behavior of the proposed mixed single stage R&D projects and multi-stage securities portfolio selection model.     

An expected regret minimization portfolio selection model

Fuzzy portfolio selection has been widely studied within the framework of the credibility theory. However, all existing models provide only concentrated investment solutions, which contradicts the risk diversification concept in the classical portfolio selection theory. In this paper, we propose an expected regret minimization model, which minimizes the expected value of the distance between the maximum return and the obtained return associated with each portfolio. We prove that our model is advantageous for obtaining distributive investment and reducing investor regret. The effectiveness of the model is demonstrated by using an example of a portfolio selection problem comprising ten securities in the Shanghai Stock Exchange 180 Index.     

A risk index model for portfolio selection with returns subject to experts’ estimations

Portfolio selection is concerned with selecting an optimal portfolio that can strike a balance between maximizing the return and minimizing the risk among a large number of securities. Traditionally, security returns were regarded as random variables. However, there are cases that the predictions of security returns are given mainly based on experts’ judgements and estimations rather than historical data. In this paper, we introduce a new type of variable to reflect the subjective estimations of the security returns. A risk index for uncertain portfolio selection is proposed and a new safe criterion for judging the portfolio investment is introduced. Based on the proposed risk index, a new mean-risk index model is developed and its crisp forms are given. In addition, to illustrate the application of the model, two numerical examples are also presented.     

基于EEMD与模糊回归的投资组合选择模型及实证

随着模糊理论的不断发展与其在证券市场的广泛应用,越来越多的学者关注到参数模糊化对投资组合优化具有重要作用。本文利用集合经验模态分解(EEMD)和模糊线性回归相结合的预测方法,构建了基于对称三角模糊数的投资组合模型。并将提出的模型与集合经验模态分解和普通最小二乘结合的方法、单一模糊线性回归方法进行了对比分析,结果表明基于集合经验模态分解和模糊线性回归建立的投资组合模型最优,这对构建最优投资组合具有参考意义。     

On the number of securities which constitute an efficient portfolio

The purpose of this paper is to discuss the relationship between the number of securities which constitute an efficient portfolio as defined by the standard mean-variance portfolio selection model and the number of periods used to compute the efficient portfolio. It is shown that the number of data gives the upper bound of the number of securities which constitute an efficient portfolio, when each efficient portfolio is unique for a given expected return. Empirical tests based on actual return data show that this upper bound is very tight when the number of data is small. However, when more data are used, the upper bound becomes looser. This result is incompatible with the market efficiency. These empirical tests also indicate that a very tight upper bound often causes a degenerate case ensuring zero-variance portfolios.     

有交易成本的模糊最优化投资

本文针对交易成本在证券组合投资中的重要地位 ,提出了考虑交易成本 ,并兼顾收益与风险的模糊最优化投资模型 ,分析了交易成本对投资有效边界的影响 ,并给出了最优投资比例公式 .这对投资者进行投资有重要的理论与实践意义 .最后 ,通过释例进行了说明 .     

A Portfolio Approach to Capital Budgeting: An Application to the Expansion to Additional Product Lines

The importance of the covariance of returns between capital assets is one of the basic principles of modern portfolio theory. An investor should seek capital assets which have negative covariance of returns, or if such capital assets are not available, capital assets with low covariance should be sought for a portfolio. From the variance-covariance structure of returns of the capital assets and the expected returns for each capital asset, a risk-reward trade-off or efficient frontier can be generated. The trade-off represents the minimum risk, as measured by portfolio variance, that could be incurred to realize a desired rate of return for the portfolio. This concept applies to a portfolio of capital budgeting projects as well as to a portfolio of securities. This paper demonstrates how this concept of portfolio diversification can be applied to a capital budgeting problem. The problem involves an actual problem faced by a U.S. distributor who must decide whether to expand sales into one of two industries. Quadratic programming is used to generate the risk-reward relationships and it is shown that the entry into one industry clearly provides a superior risk-reward relationship than entry into the other industry and compared to the company's present sales policy.