金融数据共享利益冲突平衡的法律进路

数据是数字经济时代最重要的生产要素之一，流动的数据才能发挥数据价值，金融数据共享是数据流动的一种形式。金融活动中金融数据在不同金融机构之间、金融机构与科技企业之间、金融机构与金融监管机构之间共享，金融数据控制者对其所控制的数据的财产性权益期待是促进数据共享的动力。金融数据中有大量关涉个人信息的数据，而且金融数据安全关系到国家金融体系安全，在确保国家金融体系稳健和个人信息安全的前提下，将金融数据权利赋予金融数据控制者，有利于促进金融数据共享，最大限度地挖掘金融数据价值。为平衡金融数据共享主体之间的利益冲突，应当以确定数据权属为底层逻辑，优先保障个人信息权益，并通过引进侵权惩罚性赔偿机制，调整惩罚性赔偿金分配机制和个人信息侵权公益诉讼规则，完善个人信息侵权赔偿制度。     

基于数据仓库技术的大数据审计方法研究

近年来,大数据技术的发展和广泛应用给国家经济社会带来了深刻的影响,也给审计工作带来了新的机遇.2014年《国务院关于加强审计工作的意见》明确指出"探索在审计实践中运用大数据技术的途径,加大数据综合利用力度,提高运用信息化技术查核问题、评价判断、宏观分析的能力".在此背景下,学术界和实务界关于大数据技术在审计工作中的应用...     

数据科技：智慧金融的技术基础

数据科技是开发网络空间（CYBER SPACE）数据资源所用到的科技,是发展包括智慧金融在内的智慧产业的技术基础。本文介绍了数据科技和数据产业的内涵,探讨了数据在各类智慧系统中的核心作用,以数据科技为技术基础的智慧金融,提出了发展金融数据共享规范、金融数据产品、绿色存储、高可信环境等建议,通过发展数据科技推进智慧金融的进步。     

大数据时代宏观数据与微观数据的融合

在大数据时代,现有的数据质量参差不齐、缺少统一的代码和口径,使得宏观数据与微观数据之间的融合存在问题。以宏观数据库为基准,将微观数据库匹配到宏观数据库是解决数据融合难题的一个方法。建议设置宏观数据库与微观数据库,并鼓励学者上传数据、定期融合数据库、加强与科研机构和数据公司的合作,以此来完善我国数据库的建设工作。     

Factors influencing organizations to improve data quality in their information systems

Although managers consider accurate, timely and relevant information as critical to the quality of their decisions, evidence of large variations in data quality abounds. This research examines factors influencing the level of data quality within a target organization. The results indicate that management's commitment to data quality and the presence of data quality champions strongly influence data quality in the target organization. The results also show that the managers of the participating organization are committed to achieving and maintaining high data quality. However, changing work processes and establishing a data quality awareness culture are required to motivate further improvements to data quality.     

Eine goldene Regel für die Modellierung von Daten zur Erstellung von Data-Mining-Klassifikationsmodellen unter besonderer Berücksichtigung von Problemstellungen in der Versicherungswirtschaft

A golden rule for data modelling for data mining classification models with special considerations of problems in insurances. To create classification models to avoid contract cancellations and for cross selling purposes to be used in marketing and sales of insurance companies the necessary data modelling will be discussed. Starting from a binary classification variable — cancelled contracts and active contracts, customers of a branch and non-customers of a branch — we in particular focus on the importance of historical data: To be able to detect decision patterns for cancellations respectively for new contracts in the data with the help of data mining tools, it is necessary for such contracts respectively customers not to use actual data, but data as they were at the time of decision. This obvious, but rarely used principle, is presented in detail as a golden rule for correct data modelling in such situations. As a case study a project and results for nine branches in each case of the Gothaer Versicherungen is presented.     

A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits

This paper explores the application of data mining techniques to fraud detection in the audit of financial statements and proposes a taxonomy to support and guide future research. Currently, the application of data mining to auditing is at an early stage of development and researchers take a scatter-shot approach, investigating patterns in financial statement disclosures, text in annual reports and MD&As, and the nature of journal entries without appropriate guidance being drawn from lessons in known fraud patterns. To develop structure to research in data mining, we create a taxonomy that combines research on patterns of observed fraud schemes with an appreciation of areas that benefit from productive application of data mining. We encapsulate traditional views of data mining that operates primarily on quantitative data, such as financial statement and journal entry data. In addition, we draw on other forms of data mining, notably text and email mining.     

论统计信息的生产主体与使用主体的行为关系

虚假统计信息产生的根源在于统计信息的“生产”主体与“使用”主体之间的利益关联关系与隶属关系以及统计体制。“生产”主体与“使用”主体是相对的,两者是可以相互转化的。因此,在对“生产”主体与“使用”主体进行较详细地分类之后,不论哪一类“生产”主体与任何一类“使用”主体之间,只要存在利益关联关系或隶属关系,就可能有虚假统计信息的产生。这是统计制度所带来的,“考核依据”是由被考核对象提供所造成的。     

A Note on Capital IQ's Credit Line Data

Empirical work in finance is increasingly using Capital IQ's detailed data on capital structure. We compare the Capital IQ credit line data to hand‐collected data for a random sample of firms. Missing values in Capital IQ are prevalent, so the data set underreports the importance of corporate credit lines. When data are reported, Capital IQ often differs from hand‐collected credit line activity. We suggest methods for correcting the errors in the Capital IQ data, note which portions of the data are most reliable, and quantify the effects of Capital IQ's underreporting by examining the tradeoff between cash and lines of credit.     

论个人征信信息的法律适用

个人征信信息的法律适用,主要探讨数据采集、数据使用、提供数据合理的保存和使用时限、合理的数据质量保证制度、科学的数据安全保障制度等范畴均纳入到法律规制的范围.本文立足个人征信信息的关联性和相时性等特征,具体分析个人数据采集和隐私权保护等法律制度的冲突和协调,探讨个人征信信息适用中的合法与不合法情形.     

我国银行金融数据跨境流动的法律规制

随着全球范围内银行金融数据跨境流动日益频繁,欧盟和美国在整体数据跨境流动规则下,形成了各自不同的银行金融数据跨境流动规制模式。我国也在自身国情基础上,建立了将"数据主权、数据保护与发展数字经济相统一"的整体数据跨境流动规制理念,但仍存在不同部门的规制思路有待进一步协调,以及对不同类型的银行金融数据跨境流动进行规制的针对性有待进一步提高等问题。本文认为,未来对银行金融数据的跨境流动的规制应在明确金融监管部门特殊规制权限的基础上,建立以下有区别的规制模式:针对基于商业需求而产生的银行金融数据跨境流动,应当在"知情、同意"的前提下,原则上允许跨境流动,并实行备案管理;针对基于数据流入国监管机构要求而产生的跨境流动,除存在国际协定或其他国际合作形式外,原则上不予认可。     

Data breaches and corporate liquidity management

This paper investigates the effects of data breach disclosure laws and the subsequent disclosure of data breaches on the cash policies of corporations in the United States. Exploiting a series of natural experiments regarding staggered state-level data breach disclosure laws, we find that the passage of mandatory disclosure laws leads to an increase in cash holdings. Our finding suggests that mandatory data breach disclosure laws increase the risks related to data breaches. Further, we find firms that suffer data breaches adjust their financial policies by holding more cash as well as decreasing external finance and investment.     

New development: Leveraging ‘big data’ analytics in the public sector

This article examines the opportunities presented by effectively harnessing big data in the public sector context. The article is exploratory and reviews both academic- and practitioner–oriented literature related to big data developments. The findings suggest that big data will have an impact on the future role of public sector organizations in functional areas. However, the author also reveals that there are challenges to be addressed by governments in adopting big data applications. To realize the benefits of big data, policy-makers need to: invest in research; create incentives for private and public sector entities to share data; and set up programmes to develop appropriate skills.     

The time difference effect of a measurement unit in the lead-lag relationship analysis of Korean financial market

This paper investigates Korean financial markets for the study of market microstructure of price discovery in the KOSPI 200 stock index and its related derivatives markets using different time-interval price data. The Granger causality test and vector error correction model are used to analyze the empirical relationship between markets. The lead-lag relationship between the KOSPI 200 stock index and its derivatives markets can be supported by the trading cost hypothesis and leverage effect hypothesis. This paper also shows the congruent lead-lag results in various time-intervals, but as the time-interval becomes large, more information loss and spurious results are induced. The correlations among 1-minute data, 5-minute data, and 10-minute data are significant under a 1% significance level, however, in the case of 60-minute data, the correlations with any other time-interval data are not significant. The 60-minute data even have negative correlations with others. These results are consistent regardless of the raw data or the innovation data. Therefore, we can conclude that the previous research using the 60-minute data due to an insufficiency of trading volume can be biased considerably.     

The false prosperity and promising future: Effects of data resources on bank efficiency

This study investigates the impact of data resources on bank efficiency. Based on data management theory, we measure bank data resources and find that higher data resources are associated with lower cost efficiency using data on Chinese banks over the period 2013 to 2019. However, data resources have no significant impact on profit efficiency. We discover that data resources reduce cost efficiency possibly due to increased overheads and decreased capital adequacy ratio. Furthermore, we conclude that emerging data technologies can reduce the cost burden of data resources on commercial banks, resulting in synergistic technical benefits. The findings are of timely policy importance and practical relevance for regulators, policy-makers, and bank managers. Our main results hold for a battery of endogeneity and robustness tests.     

INDIVIDUAL EQUITY RETURN DATA FROM THOMSON DATASTREAM: HANDLE WITH CARE!

We compare individual U.S. equity return data from Thomson Datastream (TDS) with similar data from the Center for Research in Security Prices (CRSP) to evaluate TDS for use in studies involving large numbers of individual equities in markets outside the United States. We document important issues of coverage, classification, and data integrity and find that naive use of TDS data can have a large impact on economic inferences. We show that after careful screening of the TDS data, inferences drawn from TDS data are similar to those drawn from CRSP. We illustrate the importance of the screens we develop using U.S. TDS data by applying the screens to TDS data from four European equity markets.     

A new data model for XML databases

The widespread activity involving the Internet and the Web causes large amounts of electronic data to be generated every day. This includes, in particular, semi-structured textual data such as electronic documents, computer programs, log files, transaction records, literature citations, and emails. Storing and manipulating the data thus produced has proven difficult. As conventional DBMSs are not suitable for handling semi-structured data, there is a strong demand for systems that are capable of handling large volumes of complex data in an efficient and reliable way. The Extensible Markup Language (XML) provides such solution. In this paper, we present the concept of a ‘vertical view model’ and its uses as a mapping mechanism for converting complex XML data to relational database tables, and as a standalone data model for storing complex XML data. Copyright © 2003 John Wiley & Sons, Ltd.     

要素市场化配置视域下数据要素交易平台发展研究

从所在地区、业务模式、数据来源、服务领域和产品特点五个方面,对国内政府参与型数据交易平台、企业主导型数据交易平台进行比较分析.政府参与型数据交易平台更多分布于经济发达地区,第三方数据平台多数为政府参与型数据交易平台,政府特色鲜明;企业主导型数据交易平台的优势领域各异.针对我国数据交易平台存在的定位不清晰、缺乏监管和指导...     

Using Survey Data of Inflation Expectations in the Estimation of Learning and Rational Expectations Models

Does survey data contain useful information for estimating macroeconomic models? We address this question by using survey data of inflation expectations to estimate the New Keynesian model by Smets and Wouters ( 2007 ) and compare its performance under rational expectations and adaptive learning. The survey information serves as an additional moment restriction and helps us to determine the learning agents' forecasting model for inflation. Adaptive learning fares similarly to rational expectations in fitting macro data, but clearly outperforms rational expectations in fitting macro and survey data simultaneously. In other words, survey data contain additional information that is not present in the macro data alone.     

The effect of holdings data frequency on conclusions about mutual fund behavior

A number of articles in financial economics have used quarterly or semi-annual mutual fund holdings data to test hypotheses about investment manager behavior. This article reexamines four well-known hypotheses in finance to determine whether the results of prior tests of these hypotheses remain valid when higher frequency (monthly) holdings data are employed. The areas examined are: momentum trading, tax-motivated trading, window dressing, and tournament behavior. We find that the use of monthly holdings data rather than quarterly holdings data or, in the case of tournament behavior, holdings data rather than monthly return data, change, and in some cases reverse, previous results. This occurs because monthly holdings data capture a large number of trades missed by quarterly data (18.5% of the trades) and permit a more precise estimation of the timing of trades.