基于约束的多目标投资组合优化研究

在金融市场上,投资组合决策是一个多目标优化问题,基于传统的方法并不能很好的解决该问题。本文提出了基于多目标粒子群算法(MOPSO)的优化解决方案,实现了对多目标优化问题的非劣最优解集的搜索,实验结果证明了算法的有效性。     

基于风险计量指标的证券组合投资的数学模型及其应用

本文建立的证券组合投资的数学模型采用风险系数β作为控制证券投资风险的参数,其经济意义明确,实用性强。该模型应用于证券组合投资,效果明显,操作方便。     

基于半离差风险收益的投资组合管理优化模型研究

本文在对投资组合管理理论分析的基础上,利用半离差法计算组合投资的风险与收益,克服了H. Markowitz提出的均值一方差模型研究的不足以及半方差模型的烦琐,提出了基于半离差风险收益的多目标优化模型;假设投资组合一般包括三种资产形式:股票、债券、现金。引进了机会成本概念在投资选择中的重要性,更加符合我国投资者的实际,便于操作,这为证券投资组合优化提供了一种新的方法,并有一定的参考借鉴价值。     

机会约束下的均值-VaR投资组合模型研究

本文在投资组合回报率服从正态分布的前提下,建立了具有投资机会约束的均值-VaR投资组合模型,讨论了模型最优解的存在唯一性,并得到了最优解的解析表达式;通过比较分析得出,具有投资机会约束的均值-方差投资组合模型只是本文讨论的模型的特款。     

不确定终止时间的多阶段最优投资组合

研究了当终止时间不确定时的多阶段最优投资组合问题.假定终止时间是个服从某分布的随机变量,将不确定终止时间的问题转化为确定时间的问题,应用动态规划求解模型,得到最优投资策略以及有效边界的解析形式.实例证明所得的结论是对确定终止时间情形的推广,最优投资策略受终止时间分布的影响.     

最佳投资组合的稳定性及其实证分析

建立组合投资的多目标决策模型,将投资者的收益-风险偏好程度量化,提出一种从品种选择、参数确定、模型建立到确定最佳组合的系统的投资方法.实证研究了最佳投资组合关于投资者的收益-风险偏好程度的稳定性,由此证明,对于不同投资风格的投资者,相应的最佳投资组合的调整直观且简便易操作.     

目标规划在原棉混配最优决策中的应用

本文根据系统优化理论,建立了配棉的目标规划模型.该模型具有多目标优化、动态决策、使用灵活方便等优点,为原棉混配工作的科学化提供了一个有效的途径和方法.     

高等教育最优投资双层规划模型研究

高等教育投资具有"双层"的特点,上层为省级主管部门,下层为高等学校。本文成功运用了双层规划模型,在不考虑高校自筹发展资金投入的情况下,建立了高等教育最优投资双层规划模型,研究了模型最优解的存在性,给出并证明了模型最优解的等价形式,设计了模型解的算法并进行了算法复杂性分析。通过求解模型,可以同时得到省级主管部门和高等学校的最优投资决策方案。文章最后还给出了考虑高校自筹发展资金的两种情况下建立投资模型和求得最优解的方法。     

不允许卖空下证券投资组合的区间二次规划问题

本文基于经典的Markowitz均值-方差模型,针对市场上不允许卖空的情况,提出了证券投资组合的区间二次规划模型,通过应用区间数排序方法(区间序关系、区间可能度和区间可接受度),给出了两种证券投资组合的区间非线性优化的数学转化模型,从而将不确定性证券投资组合模型转化为确定性的证券投资组合二次规划模型进行求解,并对由本文给出的三种求解方法与传统方法进行了比较。     

全链接分散化多阶段投资组合评价研究

现有多阶段分散化投资组合评价模型都是基于各阶段实际收益率来构建的,然而由于投资组合在各阶段的联系主要体现在财富的动态变化过程中,因此基于财富动态方程来构建多阶段投资组合评价模型更加符合实际.本文基于投资组合与前沿面的距离,给出多阶段投资组合效率的定义.在均值-方差框架下,基于财富过程构建不同导向下的全链接分散化多阶段投资组合评价模型,利用动态规划方法得到投资组合效率的解析式.最后,通过数值仿真检验了传统多阶段分散化模型与本文所提出模型之间是存在显著差异的.     

供应商优选的多目标混合整数规划模型

供应商评价和选择是全球制造和全球竞争的需要.在运营管理文献中,供应商优选和评价已经成为主要话题之一.建立了多目标整数规划模型,试图实现在满足多约束条件下,供应商选择的多目标权衡优选问题,并对一些大型重要企业进行了调研,对此模型进行实证检验.     

物流中心选址的多目标优化研究

物流中心选址是一个定性与定量相结合的问题.首先以最小化建设成本和最大化建站的综合评价值为目标,建立一个双目标规划模型.首先,根据决策者是否知道目标函数的偏好,提出加权法和折衷规划法.最后,通过多目标模糊决策分析方法获得了多目标优化方案,利用模糊分析法的原理,确定评价指标,然后把供选择的物流配送中心方案在模糊环境下进行排序,从而得出最优的选址方案.     

基于多目标混合整数规划的采购计划研究

本文运用多目标混合整数规划方法解决了多目标条件下的零部件采购计划问题.构建了基于多目标混合整数规划的采购计划的选择模型,包括采购总成本最小,净退货量最少和延迟到货量最少三个目标以及需求量、供应能力、采购供应商数量等约束条件.最后通过算例说明了提出模型的有效性.     

跟踪误差多因素投资组合决策模型

本文研究了积极投资组合更一般的风险收益关系，提出了传统跟踪误差模型和均值方差模型的统一形式。该模型不仅考虑了超额收益带来的相对风险，同时还考虑了总体风险，有效地改进了传统跟踪误差优化模型所固有的缺陷，并给出了模型最优解的显示表达式。另外，为减少模型中的待估参数．引入了多因素模型。最后给出一个例子。     

油气开发多目标二层规划投资决策模型研究及应用——以X油田为例

近年来,国内各大石油公司认真贯彻落实国家战略,保障能源安全。在油气田高质量发展的新形势下,油田开发投资决策如何落实发展目标、优化投资结构、提升投资价值、确保发展规划目标的实现已成为亟待解决的头等要务。文章以X油田为例,采用单变量因素分析法、蒙特卡洛分析法等,建立一套多目标二层规划投资决策模型,以期指导未来的投资规划,为实现发展目标奠定基础。     

Excel在多目标规划求解和灵敏度分析中的应用

多目标规划可以利用Excel中的[工具][规划求解]命令进行求解;但由于在建模过程中引入了优先级和权系数,利用Excel求解多目标规划模型比求解线性规划更复杂,特别是用Excel求解多目标规划模型时输出的敏感性报告没有实际的经济意义。本文运用Excel中的菜单[工具][方案]命令,轻松地实现了多目标规划问题的灵敏度分析,使管理者可以随时检验各种可能的方案,从而为其做出科学决策提供支持。     

考虑组合预测股价的泛证券投资组合选择策略北大核心CSCD

Online portfolio selection is regarded as an important research issue in the field of quantitative finance, which often aims to maximize returns or risk-adjusted returns. Mean-variance model, a classic portfolio model, assumes that the returns on assets obey a certain probability distribution, which characterizes the return and risk by calculating the mean value and covariance matrix of the portfolio, respectively. However, it is difficult to accurately obtain the future return or return distribution of assets, and the only information that can be accurately grasped is historical price data. Therefore, some scholars try to use only historical information to construct portfolio strategy, so they pay more and more attention to online portfolio selection problem. The so-called “online” means that when making decisions in the current period, the updated investment proportion only depends on the historical data obtained up to the beginning of the current investment, and the cycle is carried out until the end of the whole investment. Stock price prediction based on past information is one of the key problems of online portfolio selection without statistical assumption. In this paper, historical price data are used to predict the stock prices, and then a new online portfolio selection strategy is constructed. In the first part of this paper, we design a new online portfolio selection strategy based on the predicted stock prices with the goal of maximizing expected returns. First of all, in order to minimize the influence of market outliers or white noise, we adopt multiperiod historical price information to predict the stock prices for the next period. Secondly, in order to reduce the prediction bias caused by a single prediction model, the exponential smoothing method and L1-median estimation method are combined to construct a combination forecasting model. Then, the stock estimator can be obtained based on the above-mentioned combination forecasting model. Finally, a new online portfolio selection strategy named Combination Forecasting for Exponential Gradient (CFEG) is proposed by taking the maximization of expected return as the goal and adding a penalty term into the objective function to reduce the transaction costs caused by each transaction adjustment. In the second part, the competitive ratio analysis is adopted to analyze the competitive performance of the proposed strategy theoretically, and the Best Constant Rebalanced Portfolios (BCRP) strategy is regarded as a straw man. After a series of derivations, it is proven that the average logarithmic growth rate of CFEG strategy is asymptotically consistent with that of the BCRP strategy, namely, the proposed strategy CFEG is a universal strategy. In the third part, numerical examples are conducted to test the performance of the proposed strategy in terms of final cumulative wealth, statistical t-test, Sharpe ratio, Calmar ratio, transaction cost sensitivity, and other parameter sensitivities. It is necessary to test the performance of our proposed CFEG strategy. Therefore, this paper further demonstrates the performance of CFEG through numerical experiments related to 8 real stock market datasets in China and the United States. First of all, the most important indicator to judge the performance of a strategy is its final cumulative wealth. We compare the final cumulative wealth between the CFEG strategy with 3 benchmark strategies and 6 related online strategies, and compare the difference of the average logarithmic growth rate between CFEG strategy and BCRP strategy. On 8 datasets, the final cumulative wealth of CFEG strategy is stably higher than that of all online strategies, and the difference of the average logarithmic growth rate between CFEG and BCRP is almost zero. The CFEG strategy has a good performance on the whole, and the p-value is very small on each dataset in the statistical t-test. Secondly, the Sharpe ratio and Calmar ratio of CFEG strategy are compared with other strategies. The results show that CFEG strategy can better balance the returns and risks, and obtain higher risk-adjusted returns. Since the transaction costs are an important realistic constraint, the sensitivity analysis of the transaction cost rate of CFEG strategy is carried out subsequently. Meanwhile, 4 strategies are also selected for the purpose of comparison. The results show that CFEG strategy can withstand reasonable transaction costs and still obtain high returns. Finally, we conduct the sensitivity analyses of 3 parameters included in the design of CFEG strategy. The results show that the proposed CFEG strategy is stable and insensitive to parameter selection. Although the best parameter values are not selected, the CFEG strategy maintains excellent performance. Therefore, effective parameters can be selected easily in practical applications. In conclusion, the proposed strategy CFEG is suitable for investors to make investment decisions effectively and efficiently. The CFEG strategy is able to update the investment proportion in time without the future stock price information, so as to achieve the goal of maximizing returns, and provide some guidance for online investors. © (2023). All Rights Reserved.     

多阶段资产投资的动态规划决策模型

本文针对多阶段资产投资问题,给出了在满足一定的风险承受能力情况下的、以最终的总收益尽可能大为决策目标的资产投资组合问题的一个多阶段动态规划决策模型,从中可以求得多阶段投资的整休最优投资组合。继而讨论了其与单阶段投资模型的关系。最后把模型转化为线性动态规划模型。     

多阶段条件下投资组合的优化研究

实际中的投资行为都是一个持续不断、贯穿各阶段的过程，单阶段的组合优化方法不能有效地指导实际中的多阶段投资过程。本文重点探讨了在证券统计特征不变的前提下，应如何选择多阶段组合优化策略（ｗ１，ｗ２，…，ｗＴ），并用动态规划的理论方法，研究并给出了求解多阶段投资组合优化的过程和公式，最后，通过一个例子直观地说明了单阶段展期策略不是最优的。