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.

高校教师教学质量过程性评价系统的设计与实现——基于Assessment Portfolios的实践

论述了基于Assessment Portfolios的教师教学质量过程性评价的概念和基本理念,根据其评价过程的特点和实际用户需求,设计了系统的功能结构,提出了基于Assessment Portfolios的教师教学质量过程性评价模式,完成了系统软件的设计和开发,并投入使用,实现了教师教学质量评价活动的信息化管理,提高了评价的科学性和公平性,完善了评价指标体系、评价工作机制和反馈机制。

The Scientometric Measurement of Interdisciplinarity and Diversity in the Research Portfolios of Chinese Universities

Purpose:Interdisciplinarity is a hot topic in science and technology policy.However,the concept of interdisciplinarity is both abstract and complex,and therefore difficult to measure using a single indicator.A variety of metrics for measuring the diversity and interdisciplinarity of articles,journals,and fields have been proposed in the literature.In this article,we ask whether institutions can be ranked in terms of their(inter-)disciplinary diversity.Design/methodology/approach:We developed a software application(interd_vb.exe)that outputs the values of relevant diversity indicators for any document set or network structure.The software is made available,free to the public,online.The indicators it considers include the advanced diversity indicators Rao-Stirling(RS)diversity and DIV*,as well as standard measures of diversity,such as the Gini coefficient,Shannon entropy,and the Simpson Index.As an empirical demonstration of how the application works,we compared the research portfolios of 42“Double First-Class”Chinese universities across Web of Science Subject Categories(WCs).Findings:The empirical results suggest that DIV*provides results that are more in line with one’s intuitive impressions than RS,particularly when the results are based on sampledependent disparity measures.Furthermore,the scores for diversity are more consistent when based on a global disparity matrix than on a local map.Research limitations:“Interdisciplinarity”can be operationalized as bibliographic coupling among(sets of)documents with references to disciplines.At the institutional level,however,diversity may also indicate comprehensiveness.Unlike impact(e.g.citation),diversity and interdisciplinarity are context-specific and therefore provide a second dimension to the evaluation.Policy or practical implications:Operationalization and quantification make it necessary for analysts to make their choices and options clear.Although the equations used to calculate diversity are often mathematically transparent,the specification in terms of computer code helps the analyst to further precision in decisions.Although diversity is not necessarily a goal of universities,a high diversity score may inform potential policies concerning interdisciplinarity at the university level.Originality/value:This article introduces a non-commercial online application to the public domain that allows researchers and policy analysts to measure“diversity”and“interdisciplinarity”using the various indicators as encompassing as possible for any document set or network structure(e.g.a network of co-authors).Insofar as we know,such a professional computing tool for evaluating data sets using diversity indicators has not yet been made available online.

Weapons equipment portfolios selection based on equipment system contribution rates

Equipment selection is an essential work in the research and development planning of equipment.The scientific and rational development of weapons equipment portfolios is of considerable significance to the optimization of equipment architecture design,the adequate resources allocation,and the joint combat performance.From the system view,this paper proposes a method of weapons equipment portfolios selection(WEPS)based on the contribution rate of weapon systems,providing a new idea for weapon equipment portfolio selection.Firstly,we analyze the WEPS problem and the concept of the contribution rate under the systems background.Secondly,we propose a combat network modeling method for weapon equipment systems based on the function chain.Thirdly,we propose a WEPS method based on the contribution rate,fully considering the correlation relationships between potential weapons and the old weapon systems by the combat network model,under the limitation of capability demands and budget resources,with the objective to maximally increasing the combat ability of weapon systems.Finally,we make a case study with a specific WEPS problem where the whole calculation processes and results are analyzed and exhibited to verify the feasibility and effectiveness of the proposed method model.

The Study of the Effect of "The Process Writing Based on Reading-writing Portfolios" on Students'Writing Ability

In order to deal with the problem that exists in current teaching of English writing,this thesis aims to explore a new process writing approach which combines process-based approach with portfolios assessment.

STUDY ON THE INTERRELATION OF EFFICIENT PORTFOLIOS AND THEIR FRONTIER UNDER t DISTRIBUTION AND VARIOUS RISK MEASURES

In order to study the effect of different risk measures on the efficient portfolios （fron- tier） while properly describing the characteristic of return distributions in the stock market, it is assumed in this paper that the joint return distribution of risky assets obeys the multivariate t-distribution. Under the mean-risk analysis framework, the interrelationship of efficient portfolios （frontier） based on risk measures such as variance, value at risk （VaR）, and expected shortfall （ES） is analyzed and compared. It is proved that, when there is no riskless asset in the market, the efficient frontier under VaR or ES is a subset of the mean-variance （MV） efficient frontier, and the efficient portfolios under VaR or ES are also MV efficient; when there exists a riskless asset in the market, a portfolio is MV efficient if and only if it is a VaR or ES efficient portfolio. The obtained results generalize relevant conclusions about investment theory, and can better guide investors to make their investment decision.

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.

Research on Efficient Frontier of Portfolios with Transaction Cost

This paper is concerned with a study on the efficient frontier characters of portfolio with transaction cost. The conclusion is drawn that all portfolios are of positive correlation on the efficient frontier with transaction cost; the sufficient condition for the derivable efficient frontier has also been achieved. Meanwhile, in comparison with the position of the efficient frontier without transaction cost in the plane (σ 2,R), the conclusion has been made that the efficient frontiers with transaction cost drift and its opening shrinks correspondingly. With this study, the content of the efficient frontier is further enriched. It’s very constructive and important for the practical portfolio investment strategy.

The Experimental Study on the Risk Convergence of the Entrusted Portfolios of NSSF

This paper makes use of statistical tools of parameter correlation, multi-parameter regression, and does experimental analysis on issues of risk diversification of portfolios entrusted by National Social Security Fund (NSSF). The issues are industry related investment fields distribution, the trend of capitalization movement, and investment style factors in stock selection. The results show that there are risk problems with portfolios entrusted by NSSF, which include similar investment fields distribution trend, little difference among portfolios, and high risk preference degree.

Group Risk Parity Strategies for ETFs Portfolios

This research aims to compare different strategies that a non-professional investor in exchange-traded funds (ETFs) could employ to reach a good performance both from profits and from a risk perspective. In recent years, especially after the 2008 crisis, a new technique to evaluate the risk has become more popular, the so-called risk parity, which seeks to equalise the contributions to risk of the portfolio constituents. Our study analyses 17 variants of risk parity portfolio design for groups with the minimum variance strategy and equally weighted portfolio over a pool of 56 ETFs—listed on the Italian Stock Exchange—of eight different categories of specialisation. Empirical results confirm the usefulness of the group risk parity strategies in improving outcomes regarding diversification of risks among classes with good out-of-sample performance with respects to the target models.

Study on Portfolios Models with Evolutionary Programming

This paper is trying to make some improvement to Markowitz＇s Mean-Variance Model. In this paper, we try to solve the model of portfolio by using Evolutionary Programming under the condition of the covariance matrix which is a non-positive matrix, and design a new method which can improve Markowitz＇s model. At last, we give an illustrative example with the new method.

Building potential patent portfolios: An integrated approach based on topic identification and correlation analysis

Purpose： This paper suggests a framework to identify important patents for building potential patent portfolios based on patents owned by different assignees so as to highlight the value of individual patents in technology transfer and identify potential collaborators for patent assignees. Design/methodology/approach： The analysis framework includes the following steps： l） co-classification analysis based on the International Patent Classification （IPC） codes and Derwent Manual Codes （DMC） to detect sub-tech fields, 2） keyword co-occurrence analysis aiming to understand the core technology information in each patent, and 3） social network analysis used for identifying important technologies and partnerships of key assignees. A case study was conducted with 27,401 chemistry patents filed by a Chinese national research institute. Findings： The results show that this framework is effective in building potential technological patent portfolios based on patents owned by different assignees and identifying future collaborators for the assignees. This integrated approach based on topic identification and correlation analysis that combines network-based analysis with keyword-based analysis can reveal important patented technologies and their connections and help understand detailed technological information mentioned in patents. Research limitations： In keywords analysis, only titles and abstracts of patent documents were used and weights of keywords in different parts of the documents were not considered.Practical implications： The analysis framework provides valuable information for decision- makers of large institutions which have many patents with broad application prospects. Originality/value： Different from previous patent portfolio studies based on the use of a combination of patent analysis indicators, this study provides insights into a method of building patent portfolios to discover the potential of individual patents in technology transfer and promote cooperation among different patent assignees.

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.

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。

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。