Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade

This paper examines the correlation and the dependence patterns of the Qatar stock market with other markets using copula statistical theory and exploiting new datasets covering the period August 1998 to June 2018. To examine the crisis –specific change in the average degree of dependence we decomposed the data into the time periods before and after oil price shocks and the 2017 political crisis among the Gulf Cooperation Council members (i.e. the Qatari blockade). Our findings from the static copula modelling show that the correlations between the Qatari and the other stock markets significantly change after the oil price and the blockade crisis as well. The degree of change in the correlation is time varying and differs from county-group to another. Moreover, our findings reveals that the 2008 global financial crisis has a stronger impact than the price shocks and political crisis. The findings of the paper are of interest and allow for formulating a reliable and dynamic portfolio design framework for investors and risk managers.     

The price of going green: the role of greenness in green bond markets

In this paper, we empirically investigate how greenness information is priced in the green bond market. Our comparison of liquidity-adjusted yield premiums of green bonds versus synthetic conventional bonds indicates that, on average, there is no robust and significant yield premium or discount on green bonds. However, green bonds certified by an external reviewer enjoy a discount of about 6 bps. Furthermore, green bonds that obtain a Climate Bonds Initiative certificate show a discount of around 15 bps. The findings suggest that a universally accepted greenness measure can benefit the development of the green bond market.     

How efficient is the market for Australian firms’ earnings information?

We construct a measure of the speed with which forecasts issued by sell-side analysts accurately forecast future annual earnings. Following Marshall, we label this measure earnings information flow timeliness (EIFT). This measure avoids the aggregation problem inherent in price-based measures of information efficiency. We document large variation in EIFT across firm-years, and show that EIFT is positively associated with the extent of analyst following, consistent with increased analyst coverage improving the speed with which earnings-related information is recognised. We also find that EIFT is higher for firm-years classified as ‘bad news’ (i.e., where analysts’ forecasts at the start of the financial period exceed the reported outcome). However, when we separately consider instances where analysts appear to forecast non-GAAP (or ‘street’) earnings rather than GAAP earnings, we find that the greater timeliness of bad news is concentrated among observations where analysts forecast non-GAAP earnings, where unusual items are typically excluded. We conclude that the market for accounting information is more efficient for negative operating outcomes than for negative outcomes reflecting unusual items.     

基于SCP范式的中国乳业市场绩效的实证分析

基于SCP范式的理论对我国乳业市场结构和市场行为的各项指标以及对市场绩效的影响进行实证分析,发现市场结构作用于市场行为并最终影响市场绩效,为企业在调整市场结构以提高市场绩效方面提供可借鉴的理论和政策建议。研究结果表明:①我国乳业行业规模经济效益越来越显著,行业进入壁垒提高,市场结构逐渐向中高度寡占型转变;②我国乳业的市场绩效与市场集中度、行业规模之间的相关关系通过显著性检验;③乳制品行业内各乳企之间的产品差异化不明显,技术程度有待加强。     

创业失败经验对连续创业意愿的影响——创业失败学习与市场动荡性的作用

基于经验学习理论，探究创业失败经验如何通过创业失败学习影响创业者连续创业意愿，以及市场动荡性的调节作用。对176位有失败经历的创业者进行问卷调查，结果表明：创业失败经验对创业失败学习和连续创业意愿均有显著正向影响；创业失败学习在创业失败经验与连续创业意愿关系间起中介作用；市场动荡性负向调节创业失败学习与连续创业意愿的关系，即市场动荡性越高，创业失败学习与连续创业意愿间的关系越弱。研究结论对于拓展创业失败经验与连续创业意愿研究、帮助创业者从失败中复原，以及激励连续创业具有重要理论价值和实践意义。     

非洲铁路发展需求分析与市场开发策略

中资企业面向非洲铁路市场,多以"广覆盖"的形式投入开发,一定程度上造成了资源浪费。为优化中资企业在非洲铁路市场布局以及资源配置,在分析非洲铁路发展现状、铁路网发展影响因素基础上,基于铁路系统需求度、铁路发展需求度分析非洲铁路发展需求,提出了非洲大陆49个国家高、较高、中、低4个等级的铁路发展需求度,同时提出非洲铁路市场开发策略建议,即:优化非洲铁路市场开发资源配置,分区域实施铁路市场开发及项目建设,创新合作模式、拓宽资金来源。     

制造企业组织柔性、动态服务创新能力与服务创新绩效

随着制造业服务化转型的逐渐深入，服务成为价值交换的核心要素，企业研发工作的重心从产品创新延伸到服务创新。在服务创新背景下，市场需求、关键资源和创新模式的内涵都发生了根本改变，促使企业不断提高自身适应性以应对挑战。然而，目前关于该主题的研究尚不充分。分析并验证了制造企业组织柔性对动态服务创新能力和服务创新绩效的影响。从支撑服务创新的角度出发，将组织柔性划分为组织文化柔性、组织结构柔性和人力资源柔性，分析了组织柔性通过增强企业动态服务创新能力（服务需求感知能力、服务方案开发能力和服务系统重构能力）提升服务创新绩效的作用机制，构建了反映上述概念之间关系的理论模型。以组织市场（B2B）作为研究背景，面向制造企业管理者收集数据，通过实证分析对理论模型进行检验。研究结果表明：组织柔性对服务创新绩效具有积极影响，动态服务创新能力和市场动态性分别在二者间关系中起中介作用和调节作用。最后，就企业如何提高组织柔性，增强动态服务创新能力，进而改善服务创新绩效提出若干建议和对策。     

Are betting returns a useful measure of accuracy in (sports) forecasting?

In an economic context, forecasting models are judged in terms not only of accuracy, but also of profitability. The present paper analyses the counterintuitive relationship between accuracy and profitability in probabilistic (sports) forecasts in relation to betting markets. By making use of theoretical considerations, a simulation model, and real-world datasets from three different sports, we demonstrate the possibility of systematically or randomly generating positive betting returns in the absence of a superior model accuracy. The results have methodological implications for sports forecasting and other domains related to betting markets. Betting returns should not be treated as a valid measure of model accuracy, even though they can be regarded as an adequate measure of profitability. Hence, an improved predictive performance might be achieved by carefully considering the roles of both accuracy and profitability when designing models, or, more specifically, when assessing the in-sample fit of data and evaluating out-of-sample forecasting performances.     

Geographies of Algorithmic Violence: Redlining the Smart City

City governments are embracing data-driven and algorithmic planning to tackle urban problems. Data-driven analytics have an unprecedented capacity to call urban futures into being. At the same time, they can depoliticize planning decisions. I argue that this shift calls urban studies scholars to investigate geographies of algorithmic violence—a repetitive and standardized form of violence that contributes to the racialization of space and spatialization of poverty. This article examines this broader phenomenon through the case of a proprietary market value assessment that is being used to guide development in cities across the United States. The assessment employs an algorithm that helps city officials make critical decisions about which neighborhoods to target for investment, disinvestment and public service upgrades or disconnections. I argue that the racial, infrastructural, and epistemological violence associated with this evaluation can potentially lead to a new kind of municipal redlining. The article brings insights from critical race theory into conversation with critical scholarship on algorithms by analyzing how algorithmic violence works through data-driven planning technologies to depoliticize and leverage power while further entrenching racism and inequality.