粤东西北与珠三角物流产业技术效率比较

运用DEA效率测度方法,通过对2013-2017年粤东西北与珠三角区域物流产业技术效率的对比研究,探索推动广东省区域物流协调发展的路径。结果显示:对比珠三角区域,粤东西北区域物流产业技术效率与珠三角区域存在较大差距,尤其是粤东与粤西地区,粤东区域物流产业技术效率值仅为0.4,粤西区域物流产业技术效率值为0.81,均远低于珠三角区域物流产业技术效率值0.94。纯技术效率低下是制约粤东西北地区物流产业技术效率提升的主要因素,应加强物流产业信息技术应用,推进物流技术装备现代化,提高技术创新能力水平。     

大数据背景下区域木材物流配送中心选址

指出了我国木材物流存在的主要问题以及解决方案,结合大数据技术,利用遗传算法进行木材物流配送中心的选址,建立了数学模型并进行了案例分析,以达到对木材物流成本和服务的优化。     

大数据背景下企业财务管理面临的挑战与变革

随着社会经济的快速发展,市场经济体制在不断地完善,科学技术水平也在不断地上升,这时我国进入了大数据时代。传统的财务管理模式已经无法再适应时代的发展变化,所以企业应该及时转变自身的财务管理模式,更新管理理念。论文针对大数据背景下企业财务管理面临的挑战和变革进行分析,介绍财务部门的主要作用和发展现状,并提出提高财务管理水平的具体方法和策略,希望能够为企业的发展起到一些参考作用。     

简析数据加密技术在计算机网络安全中的应用

随着我国科学技术的不断发展,计算机被广泛应用于各行各业的发展建设以及人们日常生活和学习中,在给人们带来较大便利的同时,也存在安全隐患。网络安全问题威胁到了计算机信息的保密性,给计算机的可持续发展带来了安全隐患,若用户的信息被泄露会给用户带来巨大的财产损失及心理伤害。因此,出现了数据加密技术,其为计算机网络安全提供了很好的保障。基于此,论文对数据加密技术在提高网络安全性中的应用方面作了简要阐述。     

区块链技术在旅游业中的应用价值与场景探析

目前,随着大众旅游需求的不断升级,“到此一游”的踩点式旅游模式已经逐渐变得不再风行,体现旅游者兴趣差异化的品质旅游比重正在提升。在“互联网+”发展背景下,区块链技术具有透明度高、数据可溯源、不可篡改、可追踪性等众多卓越优点,可对旅游业中无法提供差异化供给、商家信用低、价格不明确、旅客维权难、基础设施管理差等困局逐一突破,推动旅游业向智慧化、透明化方向发展,对营造一个信用良好、发展健康的旅游交易市场氛围颇有裨益。论文简要介绍了区块链的基本原理,剖析区块链在旅游业的应用场景,从而讨论区块链+旅游的实际意义,为未来相关的研究提供借鉴。     

The effects of mega-events on perceived corruption

In recent decades, the media have covered many cases of corruption related to the celebration of mega-events around the globe. In most of these cases, politicians and other high officials are involved. This paper analyses the effect of hosting mega-events on the level of perceived corruption in 34 OECD countries, during 1996–2017. Summer and Winter Olympic Games, FIFA World Cups, and Universal Expositions are considered. Results show that, when we take the year of the celebration of the event as the turning point, there is no robust evidence in favour of a positive impact on perceived corruption. However, when we take the election date of the host country as the threshold, the magnitude of the effect is lasting, reaching its maximum value 1–2 years before the celebration itself, and increasing the perceived level of corruption by about 4%.     

General or central government? Empirical evidence on political cycles in budget composition using new data for OECD countries

Previous studies used general government data to examine whether national governments’ electoral motives and ideology influenced budget composition in OECD countries. General government data includes, however, the state and local level. Using new data for general and central government over the period 1995–2016, I reexamine political cycles in budget composition. The results suggest that, both at the general and central government level, leftwing governments spent more on education and less on public services than rightwing governments. Defense expenditure was somewhat lower under leftwing than rightwing governments and in election years; especially in federal states. Effects of government ideology on the individual expenditure categories are larger at the central than general government level. Scholars need to re-examine results on ideology-induced effects that have been derived from general government data where central government data should have been used.     

Are high–frequency traders informed?

Are high–frequency traders (HFTs) informed? To address this question, we examine HFTs' activity in the call auction environment, where speed-related trading is limited and signal processing capacity becomes more relevant. To model the call market, we consider the Kyle (1989) rational expectations framework for strategic trading. The test we propose for detecting informed HFTs in this market assesses potential deviations of the informativeness of HFTs' aggregate (net) demand, from the informativeness of the aggregate demand submitted by the rest of the traders. Data from the Euronext Paris preopening phase indicate that informed HFTs are present in the market just before the opening. Our results provide useful guidance for the assessment of the influence of HFTs’ quotes on price quality, an important issue for market regulators and policy makers.     

Economic forecasting with evolved confidence indicators

We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms’ production expectations and consumers’ expectations to spend on home improvements are the most frequently selected variables – both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides.     

Public acceptance of and heterogeneity in behavioral beach trip responses to offshore wind farm development in Catalonia (Spain)

A combined travel cost – contingent behaviour survey of residents and tourists in Catalonia is conducted on-site to examine the effects on beach recreational demand of developing an offshore wind farm (OWF) project. The survey considers four potential OWF scenarios with different degrees of visual impact. We allow for heterogeneity in trip preferences among individuals and control for on-site sampling through the use of a random parameters negative binomial (RPNB) model and a Multivariate Poisson log-normal (MPLN) model, respectively. The welfare measures derived from the RPNB model relate to the current beach users only, whereas those from the MPLN model refer to the general population of residents and tourists in Catalonia. The results show the importance of the specific place of location of the OWF project and how the installation of wind turbines would significantly decrease the demand for trips, depending on their degree of visual impacts, leading to a substantial welfare loss. However, the results also show that the project mainly would cause a displacement of trips to other beaches within Catalonia rather than outside Catalonia and that the welfare per trip measures generated by the RPNB and MPLN models substantially differ. Policy implications of these findings are discussed.