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 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.

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.

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.

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.

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.

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.

Kidney transplantation outcomes: Is it possible to improve when good results are falling down?

Famure et al describe that close to 50%of their patients needed early or very early hospital readmissions after their kidney transplantation.As they taught us the variables related to those outcomes,we describe eight teaching capsules that may go beyond what they describe in their article.First two capsules talk about the ideal donors and recipients we should choose for avoiding the risk of an early readmission.The third and fourth capsules tell us about the reality of cadaveric donors and recipients with comorbidities,and the way transplant physicians should choose them to maximize survival.Fifth capsule shows that any mistake can result in an early readmission,and thus,in poorer outcomes.Sixth capsule talks about economic losses of early readmissions,cost-effectiveness of tran-splantation,and how to improve outcomes and reduce costs by managing a risky patient-portfolio.Seventh capsule argues about knowing your risk behavior to better manage your portfolio;and Eighth capsule about the importance of the center experience in transplanting complex patients.We finish with some lessons of the importance of the transplantation process and the collaboration with other disciplines in order to prevent the conditions that lead to early readmissions.

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.

Risk Management for International Portfolios with Basket Options:A Multi-Stage Stochastic Programming Approach

The authors consider the problem of active international portfolio management with basket options to achieve optimal asset allocation and combined market risk and currency risk management via multi-stage stochastic programming(MSSP). The authors note particularly the novel consideration and signi?cant bene?t of basket options in the context of portfolio optimization and risk management.Extensive empirical tests strongly demonstrate that basket options consistently have more clearly improvement on portfolio performances than a portfolio of vanilla options written on the same underlying assets. The authors further show that the MSSP model provides as a supportive tool for asset allocation,and a suitable test bed to empirically investigate the performance of alternative strategies.

Does Health Insurance Coverage Influence Household Financial Portfolios？ A Case Study in Urban China

Health insurance lowers the medical financial burden of the insured through a risk-sharing mechanism, and more importantly, reduces the motivation for precautionary saving. This paper explores the relationship between health insurance coverage and household financial portfolios. We choose 2002 urban China as a case study when the health insurance system had a problem of limited adverse selection. Using data from the 2002 Chinese Household Income Project Survey, we find that health insurance coverage influences households＇ preference for financial assets, especially for the risky financial assets. These effects become more pronounced as the coverage rate of health insurance in the family increases. Our results are consistent with precautionary saving theory which suggests that future expenditure risk could affect household asset portfolios. Therefore, development of social security or a health insurance system could effectively promote the development of financial markets, especially riskier aspects of financial markets.

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.

A Novel Smart Beta Optimization Based on Probabilistic Forecast

Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanagement costs, and better long-term performance, but are at the risk ofsevere short-term declines due to a lack of Risk Control tools. Although thereare some methods to use historical volatility for Risk Control, it is still difficultto adapt to the rapid switch of market styles. How to strengthen the RiskControl management of the portfolio while maintaining the original advantagesof smart beta has become a new issue of concern in the industry. Thispaper demonstrates the scientific validity of using a probability prediction forposition optimization through an optimization theory and proposes a novelnatural gradient boosting (NGBoost)-based portfolio optimization method,which predicts stock prices and their probability distributions based on non-Bayesian methods and maximizes the Sharpe ratio expectation of positionoptimization. This paper validates the effectiveness and practicality of themodel by using the Chinese stock market, and the experimental results showthat the proposed method in this paper can reduce the volatility by 0.08 andincrease the expected portfolio cumulative return (reaching a maximum of67.1%) compared with the mainstream methods in the industry.

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.

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.

Analysis of Strategy for Extending Patent Protection of Rucaparib

As a knowledge-intensive and promising strategic emerging industry, the biomedical industry has high entry thresholds, large R&D investment, long cycle, high risk and high return. In all technical fields, the biomedical industry has the highest dependence on intellectual property rights, and the protection of pharmaceutical intellectual property rights by domestic and foreign biomedical enterprises also runs through the whole process of drug research and development. Extending the patent protection period of drugs as well as forming and strengthening patent fortresses requires a patent network that surrounds drugs to maximize the value of intellectual property protection, which is also the focus of every pharmaceutical company with patent rights. By analyzing the patent portfolio of Clovis Oncology Company in the United States on Rucaparib and the patent portfolio of other companies or applicants on Rucaparib, we can have a clearer understanding of the strategy of extending the patent protection period of a new drug product.

Value investing or investing in illiquidity?The profitability of contrarian investment strategies, revisited

Background:We investigate whether the success of contrarian investment strategies can be attributed to differences in the relative illiquidity of stocks categorized as value investments versus those categorized as glamour portfolios.Methods:Following Lakonishok et al.(J Financ 49:1541–1578,1994),we assess the illiquidity characteristics of portfolios that underlie contrarian investment strategies that are based on the level of stock’s book to market.Results:We find strong evidence that those portfolios characterized as value investments are associated with dramatically greater levels of illiquidity than glamour portfolios.We further demonstrate that strategies based on the illiquidity in the year prior to portfolio formation result in return characteristic of ostensibly contrarian strategies.Conclusions:These results suggest that the higher returns associated with contrarian investment strategies are the result of the higher illiquidity associated with value portfolios and represent compensation that the investor receives for accepting illiquidity.They also suggest that researchers should be cautious before attributing apparent anomalies to behavior-driven expectational errors rather than to other attributes unrelated to behavior,such as illiquidity.