Big Data Analytics for Improving Financial Performance and Sustainability

In this study, the key drivers of sustainability commitment, green supply chain management, big data integration and green human resource practice are explored, and the impact of these sustainable capabilities on the environmental and financial performance of banks is also elaborated. In addition, the influence of green management practices on integrating big data technology into operations is presented. As for the concept of dynamic ability, it has been used to recommend and empirically test conceptual models. Data were collected through a self-administrated survey questionnaire on 317 people working in 37 banks in six Asian countries. Research suggests that big data analytics strategies have an impact on internal processes and on the stability and financial performance of banks. Besides,it is indicated that banks are committed to proper data monitoring of their customers to complete operational efficiency and sustainability goals. Furthermore, our result proved that banks practicing Green Innovation strategies experience better environmental and economic performance because their employees are already trained in Green HR. Finally, from our study, it was found that internal and external green supply chain management practices have a positive effect on the environmental and financial performance of banks, thus ensuring that the bank of Association of Southeast Asian Nations(ASEAN) mitigates the environmental impact through its operations and ultimately experiences an increase in financial performance.     

Call for papers:Big Data and Fintech 2019 China Finance Review International Conference

<正>June 28-29,2019,Shanghai,ChinaBig datahas fundamentally changed the ways in which business collects,processes,analyzes and disseminates information and supports decision making.The volume,variety,velocity and veracity of big data pose new challenges for both business practitioners and academic researchers.Meanwhile,the technology-enabled innovation in financial services (FinTech)is developing rapidly,presenting opportunities for financial inclusion and risks to financial stability that should concern policy makers and regulators.Fintech is reshaping certain areas of financial services and weakening the profitability of banking.Policy     

金融危机背景下中美投资者情绪的传染性分析

投资者负面情绪在市场之间的传染将会加速金融危机的蔓延.因此,随着我国资本市场的逐步开放,研究投资者情绪传染对我国资本市场的影响已变得越来越迫切.本文分别在中国和美国市场构建投资者情绪代理指标,研究金融危机背景下中美市场投资者情绪的传染效应.通过格兰杰因果检验发现美国市场投资者情绪对中国市场投资者情绪存在单向传导,并以反映金融市场之间的关联程度的Copula传染指数进行进一步的传染性分析,结果表明:美国市场投资者情绪对中国市场投资者情绪的传染性在不同时期呈现出不同的大小关系,随着中国资本市场不断开放,其传染性随之增强,尤其是在次贷危机发生时,投资者的传染性达到最大.     

A non-linear model for estimating the cost of achieving emission reduction targets: The case of the U.S., China and India

With the world talking about climate change,the United States(U.S.),China and India have announced their carbon emission reduction targets.For these three countries to achieve their targets, significant questions arise,such as what will be the annual emission reduction efforts to achieve those targets,how much it would cost and what would be the economic effects.This paper puts the carbon intensity reduction targets of China and India together with the absolute emission reduction target of the U.S.into the same non-linear model to quantitatively study the optimal emission control strategies and associated total cost for achieving those targets by the year 2020,and estimate and compare the minimized total costs of the three countries to reach their targets.Our results show that the total cost for the U.S.to achieve its emission reduction target is greater than those of China and India in terms of absolute amount.However,in terms of proportion of total cost to GDP,China and India’s ratios are significantly greater than that of the U.S.,indicating that for the developing countries such as China and India,the achievement of emission reduction targets needs relatively greater effort.     

Exploring the Linear and Nonlinear Causality Between Internet Big Data and Stock Markets

In the era of big data, stock markets are closely connected with Internet big data from diverse sources. This paper makes the first attempt to compare the linkage between stock markets and various Internet big data collected from search engines, public media and social media. To achieve this purpose, a big data-based causality testing framework is proposed with three steps, i.e., data crawling,data mining and causality testing. Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets, web search data, news, and microblogs as samples of Internet big data, some interesting findings can be obtained. 1) There is a strong bi-directional, linear and nonlinear Granger causality between stock markets and investors' web search behaviors due to some similar trends and uncertain factors. 2) News sentiments from public media have Granger causality with stock markets in a bi-directional linear way, while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way, running from stock markets to microblog sentiments.3) News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority. The results of this paper might provide some valuable information for both stock market investors and modelers.     

2006-based national population projections for the U.K. and constituent countries

The 2006-based national population projections, carried out by the Office for National Statistics in consultation with the devolved administrations, show the population of the UK rising from 60.6 million in 2006, passing 65 million in 2016 and 70 million in 2028, to reach 71.1 million by 2031. In the longer-term, the projections suggest that the population will continue rising beyond 2031 for the full length of the projection period. The population will become older with the median age expected to rise from 39.0 years in 2006 to 41.8 years by 2031. Since the last projection round legislation has been passed to increase the state pension age from 65 to 66 for both sexes between 2024 and 2026. Despite this change, the number of people of working age for every person of state pensionable age will reduce from 3.32 in 2006 to 2.91 by 2031. The legislation includes further increases in the state pension age to 68 for both sexes by 2046.     

我国大城市用地形态与交通发展模式的研究

我国加入WTO后，新一轮城市化的进程将进一步加快，城市交通的问题也将越来越突出。城市交通发展模式的决定因素是城市用地形态，通过计算机模拟试验的方法，对城市不同用地形态下出行距离分布的特性进行研究；并借鉴国际大城市的交通发展模式，提出我国大城市相对应的交通发展模式。     

大数据“杀熟”如何破？基于政府-消费者协同监管的演化博弈

电商市场频频被披露隐性存在的大数据“杀熟”现象,暴露出电商市场存在平台道德风险问题的隐患。以政府监管为主导,建立电商平台“杀熟”定价行为及用户消费渠道选择的演化博弈模型,结果表明：政府的惩罚力度对平台道德风险问题的规避具有决定性的作用,当政府惩罚力度足够大时,系统稳定于（网络消费,公平定价）的理想情形;但如果政府惩罚力度不足,消费者的公平关切水平在一定程度上可以影响电商平台的定价策略,系统均衡结果将趋向于（网络消费,“杀熟”定价）或不存在演化稳定策略,电商市场陷入平台大数据“杀熟”的困境。那么,电商市场应如何破除大数据“杀熟”所造成的信任危机？基于此,建立政府和消费者协同监管机制下的演化博弈模型,研究得到：社会收益与监督成本是影响消费者监督策略的关键因素,社会收益与监督成本的差值越大,消费者选择“积极监督”行为的演化速度越快;而且,当政府惩罚力度不足时,消费者监督带来的舆论压力和声誉损失可以有效约束平台的“杀熟”行为,倒逼平台进行“公平定价”。研究结果对电商市场大数据“杀熟”行为监管机制的设计具有一定的启示。     

中国股市高频数据中的周期性和长记忆性

采用Andersen和Bollerslev(1997)的FFF回归方法,对上证综合指数高频数据中的周期性进行了分析,并分析了剔除周期后的绝对收益的长记忆性.结果表明:日内收益的周期性相对来说不如日内绝对收益明显;FFF回归可以较好的确定日内绝对收益的周期性;与使用日收益数据的结果相比,利用高频数据更好揭示了股市中更强的长记忆性.     

DEA数据标准化方法及其在方向距离函数模型中的应用

方向距离函数是对径向DEA模型的推广,它能够方便地处理存在非期望产出的情况,但其效率测量未解决单位不变性问题,这是制约方向距离函数在实践中应用的一个障碍。DEA数据标准化方法为效率测量方法提供了保持单位不变性的通用方法,同时采用标准化数据后,径向和非径向模型的效率测量结果保持不变。基于DEA数据标准化方法建立了具有单位不变性的方向距离函数效率测量方法。     

统计数据质量检查与异常点识别的模型与方法

证明在社会经济系统中,反映研究对象规模大小的统计数据近似服从对数正态分布。基于此,提出对这类数据的质量进行检查和评价,对数据中的异常点进行识别的对数正态分布检验法。     

中国股市回报波动性分析--高频数据揭示股市的特征

以MDH假设为基础．采用高频数据建立我国股市日内波动特征的理论模型。利用频域分析工具实证得出上海股票市场波动性的日内周期特征和长记忆特征。对滤波之后自相关函数进行回归，计算得到表征上海股市长记忆大小的d值。     

数据仓库与数据挖掘技术在高校决策中的应用

在高校已有的各部门业务数据库的基础上，根据高校管理的方式和特点，阐述利用数据挖掘技术建立高校数据仓库的思路和方法，并论述将其应用于高校决策的有效性和科学性。     

CGPSS-F仿真系统数据的采集与分析

本文详细地叙述了“医院门诊部为门诊病人服务的CGPSS-F仿真系统”数据采集与分析的方法.利用CGPSS-F仿真系统的驻留在队列中的实体数和资源处于繁忙状态的单元数,合理调整实体在系统的时间.根据资源容量的利用,实体排队的长度和实体在系统的时间,确定实用的系统参数.     

经济系统分形机制与股票市场R/S分析

应用非平衡态统计物理学及分形的有关理论分析了分数布朗运动 ,给出了经济系统产生分形的物理机制是 ,系统涨落力的关联函数是时间 t的幂函数 ,并在此基础上进一步讨论了分形经济时间序列耗散性的表现 .从分析 EMH及其为基础的现代投资理论的缺陷出发 ,通过 R/S分析对深圳股票市场作了研究 .研究表明深圳股票市场存在一个短周期和一个长周期 ,大致分别为 8个月和 4 .5年 .同时解释了不同 GZ序列的不同 H值产生的机理和原因 .     

自组织数据挖掘理论与经济系统的复杂性研究

通过对自组织数据挖掘理论与方法的发展过程、原理和算法基本步骤的分析 ,重点阐述了它所揭示经济对象构成因素的功能和较准确的预测功能 ,并指出 ,这是经济系统复杂性研究的一条有效途径.     

Fastrak数据滤波校正及在虚拟手术中的应用

电磁跟踪设备Fastrak在虚拟手术中医疗器械的定位中有很重要的应用,为此重点研究了Fastrak与OpenGL的编程接口,以及在复杂电磁场干扰环境下的数据滤波校正。利用快速傅立叶变换,设计了一个带通滤波器对Fastrak噪声数据进行过滤,确保了虚拟手术过程中医疗器械的平稳、准确实施。研究的滤波校正方法已成功应用于虚拟膝关节镜手术系统中,为虚拟手术系统提供了合理的Fastrak数据。     

Integrating OWA and Data Mining for Analyzing Customers Churn in E-Commerce

Customers are of great importance to E-commerce in intense competition.It is known that twenty percent customers produce eighty percent profiles.Thus,how to find these customers is very critical.Customer lifetime value(CLV) is presented to evaluate customers in terms of recency,frequency and monetary(RFM) variables.A novel model is proposed to analyze customers purchase data and RFM variables based on ordered weighting averaging(OWA) and K-Means cluster algorithm.OWA is employed to determine the weights of RFM variables in evaluating customer lifetime value or loyalty.K-Means algorithm is used to cluster customers according to RFM values.Churn customers could be found out by comparing RFM values of every cluster group with average RFM.Questionnaire is conducted to investigate which reasons cause customers dissatisfaction.Rank these reasons to help E-commerce improve services.The experimental results have demonstrated that the model is effective and reasonable.     

竞争环境下考虑供应链透明度的大数据投资决策

针对供应链中的大数据投资决策与激励问题,考虑两个制造商通过大数据投资预测需求,并通过集团采购获取产品部件,进而展开产品数量竞争.从纵向与横向刻画供应链透明度,构建分散决策下的大数据投资决策模型,分析供应链透明度、竞争强度和大数据技术水平等因素对均衡结果的影响,并与集中决策进行绩效对比.研究发现:纵向与横向透明对大数据投资均有负效应,因而'会降低其可行性和投资水平;当竞争足够激烈时,纵向透明的负效应小于横向透明,且能抑制横向透明的负效应,从而产生正的溢出效应;竞争导致大数据投资过度,横向透明导致大数据投资不足,而纵向透明却可能在竞争强度与大数据技术水平均较低或较高时实现大数据投资最优.     

我国城市人均医生数的统计建模与分析

介绍拟合非对称分布数据的一种方法,然后运用这种方法建立了我国每万人医生数的分布模型,并利用这一模型分析我国城市的就医状况