A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Using Return and Risk Model for Choosing Perfect Portfolio Applied Study in Cairo Stock Exchange

Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.

A Novel Momentum-Based Measure for Online Portfolio Algorithm

In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-the-winner strategy and to evaluate the significance of this effect, a novel measure of risk asset price momentum trend is introduced for online investment portfolio research. Firstly, a novel approach is introduced to quantify the momentum trend effect, which is determined by the product of the slope of the linear regression model and the absolute value of the linear correlation coefficient. Secondly, a new investment portfolio optimization problem is established based on the prediction of future returns. Thirdly, the Lagrange multiplier method is used to obtain the analytical solution of the optimization model, and the soft projection optimization algorithm is used to map the analytical solution to obtain the investment portfolio of the model. Finally, experiments are conducted on five benchmark datasets and compared with popular investment portfolio algorithms. The empirical findings indicate that the algorithm we are introduced is capable of generating higher investment returns, thereby establishing its efficacy for the management of the online investment portfolios.

Dynamic portfolio choice with uncertain rare‑events risk in stock and cryptocurrency markets

In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework.We reach the following conclusions in both markets:first,price diffusion and jump ambiguity mainly determine detection-error probability;second,optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity,and trivially affected by volatility diffusion ambiguity.In addition,investors tend to be more aggressive in a stable market than in a volatile one.Next,given a larger volatility jump size,investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps.Finally,the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market.These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market.The results have implications for both investors and regulators.

Is a correlation‑based investment strategy beneficial for long‑term international portfolio investors?

Using negative to low-correlated assets to manage short-term portfolio risk is not uncommon among investors,although the long-term benefits of this strategy remain unclear.This study examines the long-term benefits of the correlation strategy for portfolios based on the stock market in Asia,Central and Eastern Europe,the Middle East and North Africa,and Latin America from 2000 to 2016.Our strategy is as follows.We develop five portfolios based on the average unconditional correlation between domestic and foreign assets from 2000 to 2016.This yields five regional portfolios based on low to high correlations.In the presence of selected economic and financial conditions,long-term diversification gains for each regional portfolio are evaluated using a panel cointegration-based testing method.Consistent across all portfolios and regions,our key cointegration results suggest that selecting a low-correlated portfolio to maximize diversification gains does not necessarily result in long-term diversification gains.Our empirical method,which also permits the estimation of cointegrating regressions,provides the opportunity to evaluate the impact of oil prices,U.S.stock market fluctuations,and investor sentiments on regional portfolios,as well as to hedge against these fluctuations.Finally,we extend our data to cover the years 2017–2022 and find that our main findings are robust.

Intelligent option portfolio model with perspective of shadow price and risk‑free profit

Since Markowitz proposed modern portfolio theory,portfolio optimization has been being a classic topic in financial engineering.Although it is generally accepted that options help to improve the market,there is still an improvement for the portrayal of their unique properties in portfolio problems.In this paper,an intelligent option portfolio model is developed that allows selling options contracts to earn option fees and considers the high leverage of options in the market.Deep learning methods are used to predict the forward price of the underlying asset,making the model smarter.It can find an optimal option portfolio that maximizes the final wealth among the call and put options with multiple strike prices.We use the duality theory to analyze the marginal contribution of initial assets,risk tolerance limit,and portfolio leverage limit for the final wealth.The leverage limit of the option portfolio has a significant impact on the return.To satisfy the investors with different risk preferences,we also give the conditions for the option portfolio to gain a risk-free return and replace the Conditional Value-at-Risk.Numerical experiments demonstrate that the intelligent option portfolio model obtains a satisfactory out-of-sample return,which is significantly positively correlated with the volatility of the underlying asset and negatively correlated with the forecast error of the forward price.The risk-free option model is effective in achieving the goal of no drawdown and gaining satisfactory returns.Investors can adjust the balance point between returns and risks according to their risk preference.

Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

数字金融、数字鸿沟与家庭金融资产组合有效性——基于城乡差异视角的分析

数字金融发展有助于克服长期以来“三农”金融服务中面临的缺乏标准抵押物的“痛点”和信息不对称的“堵点”,有望加快弥合城乡数字鸿沟,让广大农民得以共同享受改革发展成果。利用中国数字普惠金融发展指数和2019年中国家庭金融调查(CHFS)数据,检验数字金融发展对城乡居民家庭金融资产组合有效性的差异,进而基于不同等级的数字鸿沟展开异质性讨论。研究表明:(1)数字金融发展显著提高了城乡家庭金融资产组合有效性,但明显更有利于城镇家庭,即城乡间存在家庭金融资产组合有效性差距拉大的危险;分维度来看,数字金融使用深度、普惠金融数字化程度也提高了城乡家庭金融资产组合有效性,且更有利于城镇家庭。(2)数字鸿沟是制约数字金融发展提高家庭金融资产组合有效性的关键因素,在城乡家庭都逾越一级和二级数字鸿沟后,数字金融发展显著提高了城乡家庭金融资产组合有效性,即数字金融发展要更好地促进城乡家庭金融资产组合有效性提升,其关键在于缩小数字鸿沟。因此,必须加快构建数字金融的包容性发展路径,建立健全城乡居民数字素养和金融素养培育的体制机制,不断弥合“信息富人”和“信息穷人”之间的数字鸿沟,提升居民家庭金融资产组合有效性。

清洁能源与金属市场间时变溢出效应及投资组合策略研究

为深入剖析清洁能源与金属的金融联系及投资策略,集成时变参数向量自回归(TVP-VAR)模型与DY溢出指数方法,本文考察11个清洁能源子行业市场与金属市场间的动态溢出效应,在此基础上构建边际净溢出网络刻画风险溢出的传染特征和路径,最后计算这些资产间的最优投资组合权重和对冲策略。研究结果表明,清洁能源和金属市场间的溢出效应具有时变性,并且对金融和经济不确定事件较为敏感。清洁能源子行业市场与金属市场间的溢出效应存在异质性,能源管理和储能股票与金属市场间的溢出效应较强,基本金属处于溢出传导者地位,而稀土金属则是溢出的主要接收者。边际净溢出网络结果表明,新冠肺炎疫情冲击导致清洁能源与金属市场间的风险溢出效应显著增强。大多数清洁能源投资组合中加入金属资产可以获得多元化收益,其对冲成本和有效性取决于清洁能源股票的类型。

Copula模型的改进及其应用

Copula模型能精确计算投资组合尾部风险,弥补Person相关系数的不足。文章基于信用风险Cop⁃ula模型,探讨了不同抽样算法在信贷投资组合中的应用问题,优化重要性抽样和交叉熵算法,测试了高斯及t-Copula模型的风险计算算法,并通过数值模拟予以检验,结果表明:朴素蒙特卡罗模拟的精度和效率较低;重要性抽样算法通过解析逼近显著降低计算方差,提高精度,但求解复杂且耗时;交叉熵算法同样有效,但需自适应算法求解优化问题。算例分析结果表明,基于不同场景选择Copula模型,可提高信贷投资组合风险计算精度和效率。

连锁股东与企业战略定位:差异化竞争抑或趋同管理

本文采用2007—2021年沪深A股企业样本考察连锁股东与企业战略定位之间的关系,研究发现:连锁股东会显著提升其持股上市公司之间的战略差异度,表现为对差异化战略的偏好。机制检验发现,连锁股东主要通过加强信息交流和提升风险承担水平进而提升企业战略差异度。进一步分析发现,连锁股东对差异化战略的推动能有效提升企业价值创造。异质性分析发现,当连锁股东为机构型和战略型及企业为非国有性质时,连锁股东对企业战略差异度的影响更强。截面分析发现,企业内部环境更稳定(上期未经历亏损或业绩下降及当期财务状况良好)和所处行业竞争更激烈时,连锁股东对企业的差异化竞争战略和战略变革的推动作用更强;面对宏观营商环境的变化冲击,连锁股东对企业战略差异度的影响在疫情前后未表现出显著差异,但对战略变革的提升作用在更稳定的营商环境下(疫情前)更强。本文是对连锁股东经济后果与企业战略影响因素研究的有益补充,同时也为如何改善战略决策提供了基于连锁股东层面的有益参考。

Heston模型下的两人鲁棒非零和随机微分投资组合博弈

用Heston模型描述风险资产的价格动态,构建了包含一种无风险资产和一种风险资产的金融市场,投资者可以将其财富自由地配置于无风险资产和风险资产中.考虑到投资者之间经济行为的随机博弈,用相对业绩刻画投资者之间的博弈行为,同时考虑模型的不确定性,以最大化最坏情境下投资者相对业绩的期望效用为目标,构建了包含两个投资者的鲁棒非零和随机微分投资组合博弈模型,利用动态规划方法分别求得了CRRA效用下Nash均衡策略的解析表达,借助数值仿真算例进行了参数的敏感性分析并给出了相应的经济意义阐释.研究发现:相较于不涉及市场竞争的传统投资策略,竞争将使投资者产生羊群效应,跟风投资风险资产,致使金融市场的系统性风险上升.此外,与不考虑模型不确定性相比,模型的不确定性使得投资者减少对风险资产的投资.

基于EWM-AHP-RF的改进灰色关联云数据确定性删除效果评价

针对当前数据确定性删除效果受多因素影响、难以客观评价的问题,提出一种基于熵权法-层次分析法-随机森林(EWM-AHP-RF)确定权重的方法,并与改进的灰色关联模型相结合,对云数据确定性删除方案进行了删除效果的组合评价。首先,构建了删除效果评价指标体系;然后,将熵权法和层次分析法进行结合,确定了指标的组合权重,并基于随机森林方法进行权重的更新;最后,基于改进灰色关联评价模型对云数据确定性删除方案的删除效果进行了评价。结果验证了所提方法的有效性和科学性。

学习带边信息专家意见的在线投资组合策略

针对以往学习专家意见的在线投资组合策略中专家策略并未考虑有助于提高投资者收益的边信息的不足,选取在相同边信息状态下投资相同单只股票、不同边信息状态下可能投资不同单只股票的策略为专家意见,基于指数加权平均算法(EWA)提出了学习带边信息专家意见的在线投资组合策略(EWAES).然后,从理论上证明了对任何的股票价格序列该策略都能够追踪最优专家意见.最后,采用中美金融市场实际股票数据对EWAES策略进行了数值分析,结果说明了该策略的有效性.

健康投资对家庭资产组合有效性的影响研究

健康作为一种典型的背景风险,对家庭的投融资决策有重要影响。基于2019年中国家庭金融调查(CHFS)数据,文章使用普通最小二乘法(OLS)和两阶段最小二乘法(2SLS)研究了健康投资对家庭资产组合有效性的影响。研究发现:健康投资能显著提升家庭资产组合有效性且结果具有较好的稳健性;健康投资会通过影响居民主客观金融素养水平和家庭收入水平从而影响家庭资产组合有效性;健康投资对城镇家庭、东部地区家庭、中高教育水平家庭的资产组合有效性的影响更大。该研究为健康投资如何影响家庭财富提供了重要的理论依据和政策含义。

档案袋评价法在高校发展型资助育人机制中的应用

新时期高校资助工作理念从保障型向发展型转变是时代发展的必然趋势。但是,当前高校发展型资助育人工作存在育人理念的理解不够深入、育人机制不够完善、缺少有效的学生评价方法等问题。档案袋评价法作为较为成熟的评价方法,其区别于传统的量性评价,重视多元化、个性化及人的全面和谐发展,优势明显。为此,在分析学生发展中的需求后,将“档案袋评价法”引入高校发展型资助育人机制中,设计应用“发展型学生档案袋”,为提升家庭困难学生未来发展能力,完善资助育人机制提供了新的理论方向和实践路径。

基于改进Black-Litterman模型的投资组合优化

考虑到金融市场“非完全有效性”且投资者“非完全理性”,通过贝叶斯框架建立了投资者观点与多渠道信息相结合的改进Black-Litterman模型,由此确定了最优的个性化投资策略.在中国股票市场的实证研究中,利用SVM-ARIMA-GARCH模型解决了投资者观点量化的问题.对比几类参考策略,改进Black-Litterman模型所确定的最优投资策略的样本外绩效表现更加稳健,在不同市场行情下均能获得较高的夏普比率和较低的换手率.

科技型中小企业专利组合策略对创新绩效影响研究--动态能力的调节作用

在知识经济时代,全面提升知识产权创造、运用、保护和管理水平,是实现知识产权强国的必然要求。如何运用专利组合策略以提高组织创新绩效是我国科技型中小企业面临的重大挑战。以2016—2021年创业板和科创板上市公司面板数据,实证检验了专利组合规模、多样性与价值对创新绩效的影响机理以及企业动态能力的调节效应。研究发现:专利组合规模、专利组合多样性与专利组合价值正向影响科技型中小企业的创新绩效;动态能力强化了专利组合规模、多样性与创新绩效的正向关系。研究结论为科技型中小企业在动态环境下实施有效的专利组合布局提供理论参考。

工程项目组合风险扩散模型的级联失效分析

为构建符合工程项目组合特点的风险扩散模型,解决风险扩散问题。本文考虑风险扩散的双关系载体—项目交互关系和风险因素逻辑关系,构建工程项目组合风险扩散的双层相依网络模型,运用级联失效理论提出风险扩散规则,通过数值仿真分析风险扩散特征。仿真结果发现:工程项目组合中,双层网络模型能够反映风险在项目和风险因素双关系载体中的扩散特征;与单层网络风险扩散模型相比,构建的模型展现出差异性的风险扩散效应,风险扩散在工程项目组合中表现出更剧烈的级联失效过程。风险扩散模型作为工程项目组合风险扩散问题的前置性研究,为进一步风险扩散网络稳定性的研究提供新思路和启发。

考虑可再生能源配额的风水火多能源电力系统年度调度模型

通过分析配额制的实施对购电策略的影响,建立考虑可再生能源配额的风水火多能源电力系统的年度调度模型。为减小计算负担,将月度的负荷与风电用数个典型场景及其占比来表示。提出能够自动选择最佳聚类数的改进k-means算法,并应用于典型场景生成过程中。将同一天的负荷与风电的历史数据曲线拼接为一条功率曲线,并基于改进k-means算法进行聚类,得到考虑负荷与风电时序相关性的典型场景。仿真结果表明:(1)所提典型场景生成方法对年度电量刻画的精度平均提高了12.7%,由其所生成典型场景的平均符合率提高了20.25%,所提方法具有更好的年度电量刻画效果和更高的平均场景符合率;(2)当绿色证书的价格高于风电与火电价格差值时,实施配额制后能够提高风电的消纳量,这对绿色证书价格的确定具有重要参考价值。