确定组合证券投资有效边界的参数线性规划方法

本文从预期收益率与风险权衡的分析，导出了连续确定组合证券投资有效边界的一种简化参数线性规划方法，研究了不允许卖空情况下有效边界的一般形状，并将有关方法和结论推广到包含一般线性的约束和无风险证券的情况，指出了有效边界出现不可导点的可能性。     

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。     

项目投资组合的风险决策模型及应用

本文对项目投资组合决策方法进行了总结分析,指出了其中的不足.通过借鉴证券投资组合理论,结合项目投资组合的特点,构建了项目投资组合的风险决策模型,弥补了现有方法的不足,并设计了一种简单的模型求解方法.该模型在保证资金等资源合理配置的前提下,考虑了项目投资的先后顺序,引入了组合风险,在风险承受范围内,实现项目组合收益的最大化.最后,通过一个实例验证了模型的有效性和实用性.     

一类证券组合投资问题的H∞状态反馈投资策略

为研究兼顾实际系统离散性和参数时变性的证券组合投资问题,提出了一个衡量风险大小的二次型性能指标,并建立了证券组合投资的离散时变状态空间模型.运用离散时变系统的H_∞控制理论,得到了证券组合投资的H_∞状态反馈投资策略.仿真证明使用该投资策略可使总收益实现其最小的不确定性.     

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.     

基于自由现金流量的证券投资组合分析

通过比较中美两国在处理和计算自由现金流量方面的不同,提出自由现金流量的计算方法,并论述了在我国会计准则下如何通过会计调整计算自由现金流量。在获得公司自由现金流量的基础上,通过引入自由现金流量乘数和自由现金流量负债比,结合经营现金流量、市盈率和低财务杠杆指标,构造了基于自由现金流量的证券投资组合,对我国证券市场做出实证分析。同时,对比其他投资策略的实际效果,证明基于自由现金流量的投资策略所具有的优越性。     

证券投资如何做好风险控制

风险与收益伴随投资的进行而产生,投资的风险分析与控制是金融投资领域永恒的、最重要的研究课题之一。证券投资在金融市场中占有重要地位,既拥有金融市场赋予它的一般性特征,也在不断发展中凸显自身的投资风险特点。在这种情况下,有效考虑市场内外的各类不确定因素及价格波动性特征,并以此为基础对证券投资如何进行风险控制进行研究,有着极其重要的理论和现实意义。     

基于一般失望模型的证券组合投资分析

基于Jia&Dyer(2001)[7]的一般失望模型,给出了一种新的非对称风险度量方法,它利用"上偏矩(upper partial mement)"来修正下方风险,不仅只考虑收益低于期望收益率时所带来的损失,而且利用了超过期望收益率时可能带来可观利润的收益.进一步给出了基于该非对称风险度量的组合投资计算方法,并通过上海证券市场的实际数据验证了该方法的有效性和实用性.     

基于股价预测的泛证券投资组合策略

本文基于期望效用最大化和L₁-中位数估计研究了在线投资组合选择问题。与EG（Exponential Gradient）策略仅利用单期价格信息估计价格趋势不同,本文将利用多期价格信息估计价格趋势,以提高在线策略的性能。首先,基于多期价格数据,利用L₁-中位数估计得到预期价格趋势。然后,通过期望效用最大化,提出一个新的具有线型时间复杂度的在线策略,EGLM（Exponential Gradient via L₁-Median）。并通过相对熵函数定义资产权重向量的距离,进而证明了EGLM策略具有泛证券投资组合性质。最后,利用国内外6个证券市场的历史数据进行实证分析,结果表明相较于UP（Universal Portfolio）策略和EG策略,EGLM策略有更好的竞争性能。     

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。     

证券组合投资有效集及有效边界的确定的方法研究

证券组合投资有效集及有效边界是确定合理投资结构的关键。本文研究了证券组合投资风险函数及有效边界的凹性，提出了将求最小风险的干净人规划问题转化为线性规划问题，并根据其最优基及其灵敏度分析，分段确定有效边界的方法。这种方法使各段有效边界可直接由相应的数学表达式求得，计算量大大减少。     

证券组合优化问题探讨

本文讨论了两种证券和三种证券组合优化公式,提出一种比较简单、精确的确定证券投资最佳比例,使证券组合的总风险最小的计算方法。     

引入无风险证券的均值——VaR投资组合模型研究

本文根据Markowitz均值-方差模型的研究发展过程,结合机会约束模型,引入无风险证券,建立了用VaR代替方差作为风险度量指标的机会约束下均值———VaR投资组合模型,讨论了模型最优解的存在性和唯一性,并得到了模型最优解的解析表达式。     

模糊随机风险偏好下的证券投资组合选择方法

在投资者风险偏好具有随机性和模糊性且效用函数具有线性可加性的假设条件下,建立 了相应的证券投资组合选择方法. 在特定的效用函数下该方法只涉及一元不等式和一元积分 的计算,具有简单实用的特点. 应该指出的是,该方法不同于传统的证券组合选择方法,它是 建立在Risto L 等提出的随机多目标可接受分析———SMAA 思想的基础上的.     

证券投资模拟实验软件中存在的问题

证券投资学是一门理论性和应用性都很强的学科,证券模拟教学实验可以帮助学生更好地掌握理论知识,提高证券技术分析能力,熟悉证券交易流程.针对目前证券模拟软件中重模拟轻教学考核的问题,本文提出增加证券技术分析评定功能等相关建议.     

一个非对称风险度量模型及组合证券投资分析

在分析Jia&Dyer的风险-价值理论基础上,给出了一个基于预先给定的目标收益的非对称风险函数。该风险函数是低于参考点的离差和高于参考点的离差的加权和,它利用一阶"上偏矩"来修正二阶下偏矩,进一步建立了在此非对称风险函数下的二次规划组合证券投资模型;并证明了该模型与三阶随机占优准则的一致性;最后通过上海证券市场的实际数据验证了该模型的有效性和实用性。     

证券投资组合的相对极小极大方法

本文讨论了无概率假设的不确定性状态的投资组合问题.提出了不完全信息下证券投资组合的一种线性规则方法-相对极小极大方法,它是一种基于相对风险的度量方法.我们讨论了几种不完全信息下的情形,并提出了相应的投资组合相对极小极大分析原则.文中进一步阐述了相对极小极大方法与Markowitz的均值-方差方法和其它的方法之间的关系.