Spatial Autoregression Techniques for Real Estate Data

This paper describes how spatial techniques can be used to improve the accuracy of market value estimates obtained using multiple regression analysis. Rather than eliminating the problem of spatial residual dependencies through the inclusion of many independent variables, spatial statistical methods typically keep fewer independent variables and augment these with a simple model of the spatial error dependence. We discuss alternative spatial autoregression model specifications, estimation methods, and prediction procedures. An empirical example is provided in the appendix.     

Institutionalising XBRL for financial reporting: resorting to regulation

By integrating and streamlining financial information within and among various organisations, eXtensible Business Reporting Language (XBRL) has been developed with a view to enhancing the efficiency, accuracy, and transparency of corporate accounting information. Taking an inter-organisational focus, this paper investigates the process of how XBRL was institutionalised. It explains and offers insights on how institutional arrangements emerge and become relevant as heterogeneous organisations consider adopting accounting innovations while evidence concerning their benefits is unavailable. The original and overall contribution of this study is that it improves current understanding of coal-face actors' perceptions, behaviours, and strategies as they interact in the organisational field and become engaged in developing accounting innovations to produce the macro-level observations documented in existing institutional theory studies.     

XBRL for Financial Reporting: Evidence on Italian GAAP versus IFRS

The systematic adoption of the eXtensible Business Reporting Language (XBRL) for financial reporting represents a great challenge. Worldwide, a large number of regulators are making an effort to promote the adoption of this standard to simplify and enhance the communication of financial information. This requires the definition of well‐structured taxonomies that can standardize and accommodate the content of financial reports prepared by firms. This study aims to analyze the regulator‐led adoption of XBRL for financial reporting. It examines the XBRL taxonomies used by Italian firms to reflect their financial reporting under rule‐based Italian GAAP and principles‐based International Financial Reporting Standards (IFRS). We compare the alignment of the Italian GAAP taxonomy and the IFRS taxonomy with Italian companies' financial statements and find two different levels of fit. The results offer useful insights for regulators and policy makers in prescribing or establishing appropriate taxonomies. We illustrate the potential impacts of the different taxonomies on the quality of financial reporting in terms of comparability and potential loss of information.     

Defining personalized concepts for XBRL using iPAD‐drawn fuzzy sets

An efficient and effective analysis of business data requires a better understanding of what the data represents, and to what degree. A human‐like way of accomplishing that without being too detailed yet learning more about data content is to summarize and map the data into concepts familiar to a person performing analysis. Processes of summarization help identify the most essential facts that are embedded in the data. All this is of significant importance for analysis of large amounts of business data required to make good and sound financial decisions. There are two aspects enabling more comprehensive yet easier processing of data: a standardized representation format of financial data; and a human‐friendly way of defining concepts and using them for building personalized models representing processing data. The first of the aspects has been addressed by the eXtensible Business Reporting Language (XBRL)—a standardized format of defining, representing and exchanging corporate and financial information. The second aspect is related to providing individuals with the ability to gain understanding of data content via determining a degree of truth of statements summarizing data based on their own perception of concepts they are looking for. In this paper, we introduce a tablet application—Tablet‐based input of Fuzzy Sets (TiFS)—and demonstrate its usefulness for entering personalized definitions of concepts and terms that enable a quick analysis of financial data. Such analysis means utilization of soft queries and operations of aggregation that extract and summarize the data and present it in a form familiar to analysts. The application allows for defining concepts and terms with ‘finger‐made’ drawings representing a person's perception of concepts. Further, these definitions are used to build summarization statements for exploring XBRL data. They are equipped with ‘drawn’ definitions of linguistic terms (e.g. LARGE, SMALL, FAST) and linguistic quantifiers (e.g. ALL, MOSTLY), and enable summarization of data content from the perspective of a user's interests. The ‘drawn’ linguistic terms and quantifiers represent membership functions of fuzzy sets. Utilization of fuzzy sets allows for performing operations of data summarization in a human‐like way. The application of TiFS illustrates ease of inputting personalized definitions of concepts and their influence on the interpretation of data. This introduces aspects of personalization and adaptation of artificial intelligence systems to perceptions and views of individuals. The proposed application is used to perform a basic analysis of an XBRL document.     

软计算技术在金融预测应用进展研究

金融时间序列本质上是复杂的、高度噪声的,呈现出非线性行为,使得传统的统计方法难以有效地预测。随着计算技术的发展,已经出现了一些软计算技术用以支持金融时间序列预测。通过综述软计算技术在金融时间序列预测的数据准备、算法模型、自我训练学习、预测评估及优化方面的最新进展,从而便于研究者对已有软计算技术进行改进和研究新的预测算法。     

XBRL and open data for global financial ecosystems: A linked data approach

Information professionals performing business activity related investigative analysis must routinely associate data from a diverse range of Web based general-interest business and financial information sources. XBRL has become an integral part of the financial data landscape. At the same time, Open Data initiatives have contributed relevant financial, economic, and business data to the pool of publicly available information on the Web but the use of XBRL in combination with Open Data remains at an early state of realisation. In this paper we argue that Linked Data technology, created for Web scale information integration, can accommodate XBRL data and make it easier to combine it with open datasets. This can provide the foundations for a global data ecosystem of interlinked and interoperable financial and business information with the potential to leverage XBRL beyond its current regulatory and disclosure role. We outline the uses of Linked Data technologies to facilitate XBRL consumption in conjunction with non-XBRL Open Data, report on current activities and highlight remaining challenges in terms of information consolidation faced by both XBRL and Web technologies.     

The impact of tagging qualitative financial information on investor decision making: Implications for XBRL

This study extends prior research by investigating whether investors benefit from tagging complex narrative disclosures. We examine how tagging facilitates professional and nonprofessional investors' acquisition and assimilation of narrative disclosures in assessing company risk and predicting future performance. Participants were randomly assigned to a standard or tagged presentation of Management's Discussion and Analysis (MD&A), the U.S. equivalent to Management's Commentary. The two versions contain identical overall information. The standard version works similar to a PDF file where the user can access sections of the MD&A by selecting titled sections. The tagged version uses an extended hierarchical structure proposed for the MD&A and as a conceptual foundation for developing a related XBRL taxonomy. As theorized, results indicate that nonprofessional investors use a more directive search strategy under the tagged condition while professional investors' search strategies are unaffected by the tagged condition. Saliency of risk information increases for both investor groups in the tagged condition (i.e., risk assessments and stock price predictions are more associated). Our findings have important implications in light of the U.S. SEC's decision to preclude inclusion of XBRL-tagged MD&A information in mandatory filings due to taxonomy limitations.     

Computing optimal rebalance frequency for log-optimal portfolios

Log-optimal investment portfolio is deemed to be impractical and cost-prohibitive due to inherent need for continuous rebalancing and significant overhead of trading cost. We study the question of how often a log-optimal portfolio should be rebalanced for any given finite investment horizon. We develop an analytical framework to compute the expected log of portfolio growth when a given discrete-time periodic rebalance frequency is used. For a certain class of portfolio assets, we compute the optimal rebalance frequency. We show that it is possible to improve investor log utility using this quasi-passive or hybrid rebalancing strategy. Simulation studies show that an investor shall gain significantly by rebalancing periodically in discrete time, overcoming the limitations of continuous rebalancing.     

The Hard Argument for China''s Soft Landing

The world has at last embraced the Chinese growth miracle. But in doing so it may be missing an important reality check. Two problems have recently emerged in the Chinese economy: a credit bubble and inflation. The good news is that China is already coping with these issues. The bad news is that the world may be unprepared for the impact of a Chinese slowdown.     

云计算对我国商业银行的发展启示

云计算采用了一种资源共享的IT技术支持和管理方式,提供了一种更高效的数据存储和处理能力.面对这一新技术的出现和发展,银行业在信息技术应用、业务流程再造和产品服务创新等方面面临革命性变革.云计算解决方案从硬件、软件和业务服务方面,为银行业的快速发展提供了有利条件.本文将针对这一现状,以我国商业银行为出发点,探讨云计算对我国银行业的发展启示.     

Techniques for customer modelling in CRM

A central tenet of CRM is the importance of understanding and predicting customer behaviour. Customer modelling is the process which generates this understanding. This paper explores the techniques and tools used to develop and deploy customer models.     

大数据背景下的农险反欺诈检测:国际经验与技术选择

中国农业保险市场中欺诈骗保等违法行为不容忽视,亟需运用数据挖掘技术提高农业保险发展质量。首先,本文对反欺诈检测的常用方法进行了梳理,包括异常值检测、聚类法、线性回归法、社会关系网络分析法等方法。其次,本文总结美国运用数据挖掘技术开展农业保险反欺诈检测的基本经验。美国利用以政府主导、研究机构参与的模式,开发出多种欺诈检测项目,为美国农业保险节约巨额资金。再者,基于国际经验,本文提出适用于中国农业保险反欺诈检测的相关性异常值检测法、合谋关系检测法和机器学习法。最后,为进一步推动数据挖掘技术在中国农业保险反欺诈检测中的运用,本文提出建立农业保险大数据库、建立数据挖掘合作平台、建立常态化的数据利用机制、以及培育和激励数据挖掘人才等建议。     

Computing optimal rebalance frequency for log-optimal portfolios in linear time

The pure form of log-optimal investment strategies are often considered to be impractical due to the inherent need for continuous rebalancing. It is however possible to improve investor log utility by adopting a discrete-time periodic rebalancing strategy. Under the assumptions of geometric Brownian motion for assets and approximate log-normality for a sum of log-normal random variables, we find that the optimum rebalance frequency is a piecewise continuous function of investment horizon. One can construct this rebalance strategy function, called the optimal rebalance frequency function, up to a specified investment horizon given a limited trajectory of the expected log of portfolio growth when the initial portfolio is never rebalanced. We develop the analytical framework to compute the optimal rebalance strategy in linear time, a significant improvement from the previously proposed search-based quadratic time algorithm.     

Computing a Multivariate Normal Integral for Valuing Compound Real Options

We extend the Geske (1979) model to a multivariate normal integral for the valuation of a compound real option. We compared the computing speeds and errors of three numerical integration methods, namely, Drezner's improved Gauss quadrature method, Monte Carlo method and Lattice method, together with appropriate critical value finding methods. It is found that secant method for finding critical values combined with Lattice method and run by Fortran took merely one second, Monte Carlo method 120 seconds. It is also found that the real option decreases with interest rate, not necessarily positively correlated with volatility , a result different from that anticipated under financial option theory. This is mainly because the underlying of real option is a non-traded asset, which brings dividend-like yield into the formula of compound real options. Dividend-like yield rises with the multiplication of correlation coefficient and . High indicates the poor diversification advantage of the new investment project in relation to the existing market portfolio, and the value of real call option decreases with . Conversely, when is low, the proposed project provides better diversification advantage and the real call option rises with . Irrespective of the value of , when interest rate increases, the value of real call option drops, especially when is high, the value of the project is dominated by interest rate.     

XBRL财务报告分类标准评价——基于财务报告分类与公司偏好的报告实务的匹配性研究

XBRL是网络财务报告向高级阶段发展的技术基础,可以改进信息的编报和使用。然而,XBRL财务报告生成和应用的一个关键问题是采用的财务报告分类与公司偏好的报告实务间能否很好的匹配,匹配性差将导致信息损失。本文仅针对财务报表附注项目,将12个行业117个上市公司2005年年报中披露的项目与上交所制定的《中国上市公司信息披露分类》标准中定义的相应标记匹配,发现二者间存在着较大的差异,并且行业间的差异不显著。我们认为XBRL网络财务报告目前还不适宜全面应用,当务之急是进一步修改完善分类标准。     

Cusum Techniques for Technical Trading in Financial Markets

It is discovered that the CUSUM techniques often used in the manufacturing industry can be adapted to yield a trading strategy in the financial market. The familiar filter trading strategy in finance is found to be a particular case of CUSUM procedures. A more general form of the CUSUM techniques will yield new trading strategies which have intuitive appeals. Trading characteristics of such strategies will be investigated using CUSUM techniques.     

Case-Based Reasoning: Application Techniques for Decision Support

Decision-support systems can be improved by enabling them to use past decisions to assist in making present ones. Reasoning from relevant past cases is appealing because it corresponds to some of the processes an expert uses to solve problems quickly and accurately. All this depends on an effective method of organizing cases for retrieval. This paper investigates the use of inductive networks as a means for case organization and outlines an approach to determining the desired number of cases—or assessing the reliability of a given number. Our method is demonstrated by application to decision making on corporate tax audits.