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.     

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.     

Consumption,asset wealth,equity premium,term spread,and flight to quality

We link transitory deviations of consumption from its equilibrium relationship with aggregate wealth and labor income to equity returns on the one hand, and to two characteristics of bond investors—the premium demanded to hold long‐term assets, and “flight to quality” behavior—on the other hand. Using a panel of 10 euro area countries over the period 1984Q1–2017Q4, we show that a rise in the consumption–wealth ratio predicts both higher equity returns and the future term spread, while a fall in the consumption–wealth ratio explains a large fraction of the rise in the spread between the “risky” and the “safe‐haven” bond.     

The risk-based approach under the new EU data protection regulation: a critical perspective

Abstract

The first broad reform of personal data protection legislation in the European Union entered into force in May 2018 (Regulation (EU) 2016/679, the General Data Protection Regulation). Remarkably, with this reform a risk-based approach has been introduced as the core data protection enforcement model, while data protection authorities see their regulatory role significantly weakened. The risk-based approach is to be implemented by the data controllers (i.e. the operators) via data protection impact assessments (evoking the established environmental impact assessment procedure) and notification of breaches, among other procedures. Hence the scope of both the concepts of risk and risk regulation spread beyond conventional domains, namely the environment, public health or safety, i.e. physical risks, to encompass risks to intangible values, i.e. individual rights and freedoms, presumably harder to assess and manage. Strikingly, the reform has been accompanied by a confident discourse by EU institutions, and their avowed belief in the reform’s ability to safeguard the fundamental right to data protection in the face of evolving data processing techniques, specifically, big data, the Internet of Things, and related algorithmic decision-making. However, one may wonder whether there isn’t cause for concern in view of the way the risk-based approach has been designed in the data protection legislation. In this article, the risk-based approach to data protection is analysed in the light of the reform’s underlying rationality. Comparison with the risk regulatory experience in environmental law, particularly the environmental impact assessment procedure, is drawn upon to assist us in pondering the shortcomings, as well as the opportunities of the novel risk-based approach.     

Global uncertainties and portfolio flow dynamics of the BRICS countries

This paper investigates the dynamics of bond and stock market capital flows to BRICS countries under uncertainties such as global economic policy uncertainty and the US trade policy uncertainty. We use a time-varying Granger causality framework over the January 2008-November 2019 period to analyze the predictive power of uncertainties on capital flows in the form of bond and equity. The results show that the effects are heterogeneous across countries and stronger during the Global Financial Crisis period and post-2018 period while it lost its significance in the subsequent period. The negative influence of uncertainties on capital flows directed to BRICS countries is also evident in the results of non-parametric time-varying panel models. Overall, it is thought that the heterogeneous structure of the causality between uncertainty and portfolio flows into BRICS may present portfolio diversification benefits for global investors.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.