IT集成项目的风险评价与控制

本文就 IT集成项目的风险问题进行了系统研究和探讨 ,其内容包括 IT集成项目的特点、风险的产生与特征、预测和评价、防范和控制等。     

企业价值创新能力评价指标体系研究

从企业价值创新的全新经营理念和战略思维出发,探究了企业价值创新能力评价定位与评价指标体系设立的原则,最后从提供新产品和服务的能力、技术与管理创新能力、开拓市场与创新产品能力、产品功能创新能力以及突破现有市场边界能力等五个方面来构建企业价值创新能力评价的指标体系。     

房地产投资项目融资风险的灰色模糊评判研究

房地产投资项目的融资风险问题具有灰色性(信息不完全)和模糊(概念不明确)性。考虑到灰色性和模糊性的影响，采用适用性更广的灰色模糊多级综合评判理论，对房地产投资项目融资风险的多级指标体系进行综合评判，可以有效地、全面地和系统地评价房地产投资项目融资风险的大小，为项目融资项目决策提供准确依据。     

“丝路经济带”九省市经济金融竞争力评价

深入剖析丝绸之路沿线国内段九省市的综合发展实力,探索不同发展战略定位下省 区市竞合协同发展路径,对全面发展丝绸之路经济带具有重要意义。本文从经济发展竞争力、 环境设施与可持续发展竞争力、科技创新与财富竞争力、金融发展竞争力、对外开放竞争力五 个维度建立指标评价体系,运用分类主成分分析法全面衡量九个省市经济金融发展的分项和综 合竞争力,将九省市分为三个发展梯度,形成资源聚集与要素流动的直观剖析。在此基础上,利 用聚类分析法得出丝绸之路经济带区域经济关联的着力点与经济发展的增长极,并最终得出处 于不同梯度省区市间开展基于优势竞争力的产业间合作的三大有效路径。     

Systemic event prediction by an aggregate early warning system: An application to the Czech Republic

This work develops an early warning framework for assessing systemic risks and predicting systemic events over a short horizon of six quarters and a long horizon of 12 quarters on a panel of 14 countries, both advanced and developing. First, we build a financial stress index to identify the starting dates of systemic financial crises for each country in the panel. Second, early warning indicators for the assessment and prediction of systemic risk are selected in a two-step approach; we find relevant prediction horizons for each indicator by a univariate logit model followed by the application of Bayesian model averaging to identify the most useful indicators. Finally, we observe the performance of the constructed EWS over both horizons on the Czech data and find that the model over the long horizon outperforms the EWS over the short horizon. For both horizons, out-of-sample probability estimates do not deviate substantially from their in-sample estimates, indicating a good out-of-sample performance for the Czech Republic.     

Addressing the social landscape dimensions: The need for reconciling cross scale assessments for capturing Cultural Ecosystem Services (CES)

This special issue integrates eleven papers focussing on indicators able to convey the multiple expectations that society has concerning agricultural land. As scale issues have been so far overlooked within this research topic, a particular focus of this themed issue is to highlight the need for reconciling assessments across scales. The shared purpose of the contributions is to examine how the multiple societal expectations concerning agrarian landscapes might be incorporated into land use policy at different governance levels.A core set of these papers were presented in the symposium “Linking social indicators across scales”, to the European Congress of the International Association of Landscape Ecology (IALE) in September 2013 in Manchester, UK. This symposium was organized to create the time and space for a discussion on the issues of scale when capturing societal preferences concerning agrarian landscapes. While the landscape scale was specifically addressed by four papers, seven papers examined broader geographic units in addition to up-scaling and downscaling issues.     

Using templates and linguistic patterns to define process performance indicators

Process performance management (PPM) aims at measuring, monitoring and analysing the performance of business processes (BPs), in order to check the achievement of strategic and operational goals and to support decision-making for their optimisation. PPM is based on process performance indicators (PPIs), so having an appropriate definition of them is crucial. One of the main problems of PPIs definition is to express them in an unambiguous, complete, understandable, traceable and verifiable manner. In practice, PPIs are defined informally – usually in ad hoc, natural language, with its well-known problems – or they are defined from an implementation perspective, hardly understandable to non-technical people. In order to solve this problem, in this article we propose a novel approach to improve the definition of PPIs using templates and linguistic patterns. This approach promotes reuse, reduces both ambiguities and missing information, is understandable to all stakeholders and maintains traceability with the process model. Furthermore, it enables the automated processing of PPI definitions by its straightforward translation into the PPINOT metamodel, allowing the gathering of the required information for their computation as well as the analysis of the relationships between them and with BP elements.     

Momentum in the Chinese Stock Market: Evidence from Stochastic Oscillator Indicators

ABSTRACT

We explore whether investors earn profits through the use of stochastic oscillator indicators (SOI) for trading stocks. The results reveal that investors might use momentum strategies when trading constituent stocks of SSE 50 as the overbought trading signals emitted by SOI. We infer that the results might be caused by herding behaviors of Chinese investors since overoptimistic moods are likely to exist as evidenced by the 80 percent trading volume traded by individual investors in the Chinese stock market.     

The globalization indicators-tourism development nexus: a dynamic panel-data analysis

This study investigates how economic, social, and political globalization indicators influence tourism development. We applied different approaches of panel regression to panel data from a sample including 133 countries between 1995 and 2014. The results indicate that economic, social, and political globalization are significant factors for tourism development. Thus, this study proves that economic, social, and political integration of countries are significant driving forces behind their tourism development.     

Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we significantly expand the data coverage by providing democracy indices for 186 countries in the period from 1919 to 2019.