Abstract ：

This paper reveals that the rest-of-day return has positively significant prediction on the last 30-min return in the Chinese SSE 50 exchange-traded fund market. Its predictive power is economically significant and will decay in the next three days. Moreover, it reveals the relationship between intraday return prediction and short gamma hedging demand from option market makers, demonstrating that intraday hedging could be postponed till the end of a trading day. Intraday momentum caused by rebalancing traders and information investors cannot be completely offset by option market makers on days with positive net gamma exposure. Our results are robust to alternative exchange-traded fund index option and its underlying asset. © 2022 Elsevier B.V.

Keyword ：

Commerce Financial markets Investments Momentum

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Participatory quantitative Health Impact Assessments (HIAs) in developing countries are rare partly due to data scarcity. This paper reports on primary data collected in the city of Port Louis to complete a HIA of urban transport planning in Mauritius. We conducted a full-chain participatory HIA to assess health impacts on the basis of a transport mode shift in Port Louis, Mauritius [1]. By applying mixed-methods, we estimated averted deaths per year and economic outcomes by assessing the health determinants of air pollution, traffic deaths and physical activity. The participatory quantitative HIA included [1] baseline data collection [2] co-validation of transport policy scenarios with stakeholders and [3] quantitative modelling of health impacts. We used the risk assessment method for HIA appraisal. The data can be reused for epidemiological analysis and different types of impact assessments.

Keyword ：

Africa Air pollution Health impact assessments Physical activity Traffic deaths Urban transport planning

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疫情暴发以来,美联储实施了一系列强力的刺激措施,更是启动了有史以来最大规模的量化宽松政策,对美国经济乃至全球经济带来了巨大影响。文章基于量化宽松的理论描述了本次疫情中的美联储货币政策,并比较了同期的中国央行在货币政策实施和货币政策传导机制的差异,总结了本次疫情中的美联储货币政策的特点及中国的应对方式。

Keyword ：

货币政策传导机制 量化宽松 美联储

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With the rapid development of network technology and database management system, enterprises in different fields have accumulated a large amount of data, which makes the previous data analysis tools and technologies have been unable to meet the needs of data information processing, resulting in a huge waste of data resources. In this regard, this paper proposes a portfolio optimization model based on decision tree algorithm and its algorithm. At present, decision tree algorithm is the most commonly used method in data mining classification technology because of its high classification accuracy, fast speed and easy understanding of classification rules. The most fundamental purpose of securities investment is to gain benefits. However, in investment activities, returns are always accompanied by risks. Generally, the higher the income, the greater the risk. In order to diversify risks, investors combine many kinds of securities to invest, the so-called investment portfolio, in order to obtain the maximum return. This makes the research of investment portfolio one of the major issues in the financial world. © 2021 ACM.

Keyword ：

Data handling Data mining Decision trees Electronic trading Financial markets Information management Investments Optimization

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Background: Obesity prevalence has become one of the most prominent issues in global public health. Physical activity has been recognized as a key player in the obesity epidemic. Objectives: The objectives of this study are to (1) examine the relationship between physical activity and weight status and (2) assess the performance and predictive power of a set of popular machine learning and traditional statistical methods. Methods: National Health and Nutrition Examination Survey (NHANES, 2003 to 2006) data were used. A total of 7162 participants met our inclusion criteria (3682 males and 3480 females), with average age ranging from 48.6 (normal weight) to 52.1 years old (overweight). Eleven classifying algorithms-including logistic regression, naive Bayes, Radial Basis Function (RBF), local k-nearest neighbors (k-NN), classification via regression (CVR), random subspace, decision table, multiobjective evolutionary fuzzy classifier, random tree, J48, and multilayer perceptron-were implemented and evaluated, and they were compared with traditional logistic regression model estimates. Results: With physical activity and basic demographic status, of all methods analyzed, the random subspace classifier algorithm achieved the highest overall accuracy and area under the receiver operating characteristic (ROC) curve (AUC). The duration of vigorous-intensity activity in one week and the duration of moderate-intensity activity in one week were important attributes. In general, most algorithms showed similar performance. Logistic regression was middle-ranking in terms of overall accuracy, sensitivity, specificity, and AUC among all methods. Conclusions: Physical activity was an important factor in predicting weight status, with gender, age, and race/ethnicity being less but still essential factors associated with weight outcomes. Tailored intervention policies and programs should target the differences rooted in these demographic factors to curb the increase in the prevalence of obesity and reduce disparities among sub-demographic populations.

Keyword ：

disparity machine learning obesity physical activity

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User experience has garnered increasing interest from researchers in the field of sustainable human-computer interaction (HCI) design. One of the key pillars to building sustainable user experience for digital applications is to be able to identify powerful triggers of detracting behavior and especially those of radical detraction. In the past, traditional methods of user experience analysis have often led to the assumption that pragmatic aspects constitute the priority issues for user experience improvement given the prevalence of these aspects in user feedback surveys. However, our econometric analysis based on net promoter score (NPS) survey results defeats this assumption and reveals that the most powerful degraders of the detractors' experience are in fact the emotive aspects of experience such as transparency of transactions and customer service interaction. Based on our analysis, we arrive at several insights regarding the building of a sustainable HCI strategy. First, hedonic aspects of user experience are the most significant determinants of the degree of user detraction among the detractors. Second, membership cannot be taken for granted as a token of customer loyalty. Building on the theoretical framework of Hassenzahl, Haines-Gadd, and others, we generate new evidence for the importance of servicing hedonic aspects of user experience for digital applications businesses to form a sustainable customer relation and product strategy.

Keyword ：

hedonic/pragmatic model of user experience human-centered design (HCD) net promoter score (NPS) sustainable business model sustainable human-computer interaction (HCI) user experience

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According to in-depth research, a wide range of problems in applied science involve estimating the probability of compound stochastic sums of heavy-tailed risks over a large threshold. Many researchers have explored this issue from different aspects in recent times. There are two main difficulties here: one is how to deal with the heavy tail of risk, and another is how to handle the dependence of the aggregated processes. Aimed at these two main problems, we investigate the asymptotic properties of the tail of compound stochastic sums of heavy-tailed risks in a general dependence framework, and some approximate bounds and key characteristics related to value-at-risk are also derived. Several practical examples are given to demonstrate the effectiveness of the approximation results. Furthermore, the main results in this paper can be applied to studies of stochastic models in finance and econometrics and studies of dependent netput processes of the M/G/1 queuing systems, etc.

Keyword ：

Multivariate dependence operational risk stochastic models subexponential distribution value-at-risk

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The bias of technological progress, particularly relating to energy saving and carbon emissions reduction, plays a significant role in the sustainable development of transportation, and has not yet received sufficient attention. The objectives of this paper were to examine the bias of technological change (BTC), input-biased technological change (IBTC), and output-biased technological change (OBTC), and their influencing factors in the sustainable development of China's regional transportation industry from 2005 to 2017. A slack-based measure (SBM) Malmquist productivity index was adopted to measure the BTC, IBTC, and OBTC by decomposing green total factor productivity. The results revealed that: (1) Continuous technological bias progress and input-biased technological progress existed in China's transportation development from 2005 to 2017, making an important contribution to green total factor productivity. The output-biased technological change was close to 1, indicating a slight impact on the sustainable development of the transportation industry; (2) The bias of technological progress in eastern regions was slightly greater than that in central regions, and obviously greater than that in western regions. Moreover, different provinces experienced different types of technological bias change, with four major types observed during the research period; (3) The input-biased technology of a majority of provinces tended to invest more capital relative to labor, using more capital comparing to energy, and consume more energy relative to labor, while the output-biased technology of most provinces tended to produce desirable outputs (value added in transportation) and reduce the byproduct of CO(2)relatively; (4) Average years of education, green patents in transportation, industrial scale, and local government fiscal expenditure in transportation significantly contributed to promoting the bias of technological progress, which was inhibited by the R&D investment. This study provides further insight into the improvement of sustainable development for China's transportation, thereby helping to guide the government to promote green-biased technological progress and optimize the allocation of resources.

Keyword ：

China sustainable development technological bias transportation

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文章以互联网改变传统经济活动的实践进展脉络为逻辑主线,研究互联网创造经济价值的内在机理,指出随着以互联网为主的新一代信息技术的发展和广泛使用,互联网主要通过产业融合的方式,从自身价值创造、创新市场组织、改变市场主体行为、重塑生产方式和价值网络四个方面,融入实体经济,形成经济网络,最终创造出经济价值.文章认为,为推动互联网与实体经济的深度融合,还需深入研究“互联网+”产业生态系统的关键要素及其内在关系,引导互联网更全面地从虚拟走向现实,创造出更多的经济价值并解决中国面临的经济问题.

Keyword ：

互联网+ 价值创造 新产业革命

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We introduce an approach that supports researchers and practitioners to determine the quality of first-time user experience (FTUX) and long-term user experience (LTUX), as well as to identify critical issues with these two types of UX. The product we chose to study is a mobile fitness application. Mobile apps tend to have a much shorter service life than most other products; thus, the developers/designers need to pay great attention to both first-time and long-term user experience. This study is based on a multi-method approach. We employed the AttrakDiff questionnaire to assess users' first impressions of the app, and the UX Curve method to evaluate how users' experience of the app has changed over time. Besides the quantitative data, which helped to determine the quality of user experience, we also collected qualitative data during two interviews with participants, and focused on the issues that predominantly deteriorated user experience. A four-coordinate plane tool was designed later in the data analysis process that combined the two kinds of user experience data at the same time, which led to a qualitative positioning of the user experience status of a certain product. The model was further successfully adopted in the identification of user experience issues of an online fitness application.

Keyword ：

AttrakDiff questionnaire mobile application development sustainability the UX Curve method user experience evaluation

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