Challenges Ahead for Converging Financial Data

U.S. corporations are obligated to file financial statements with the U.S. Securities and Exchange Commission (SEC). The SEC´s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system containing millions of financial statements is one of the most important sources of corporate information available. The paper illustrates which financial statements are publicly available by analyzing the entire SEC EDGAR database since its implementation in 1993. It shows how to retrieve financial statements in a fast and efficient way from EDGAR. The key contribution however is a platform-independent algorithm for business and research purposes designed to extract textual information embedded in financial statements. The dynamic extraction algorithm capable of identifying structural changes within financial statements is applied to more than 180,000 annual reports on Form 10-K filed with the SEC for descriptive statistics and validation purposes.

2003

We present a system to extract, visualize, and analyze inter-corporation relationships disclosed by public companies in their annual reports to the U.S. Securities and Exchange Commission (SEC). In improving the transparency of these disclosures, we allow policy makers, analysts, investors, and the general public to analyze these relationships at both the firm level and the industry level. Using probabilistic information retrieval and extraction techniques, we automatically extract a dataset of 45,000 relationships between 26,000 companies from over 15 gigabytes of SEC 10-K documents. These relationships range from ownerships, agreements, and personal connections to competition and legal disagreements. Information visualization and social network analytic techniques can then be applied to explore and analyze the dataset. 7 1

Proceedings of the eighth international conference on Information and knowledge management - CIKM '99, 1999

The proliferation of electronically available data within large organizations as well as publicly available data (e.g. over the World Wide Web) poses challenges for userS who wish to efficiently interact with and integrate multiple heterogeneous sources. This paper presents CI', a corporate information integrator, which applies XML as a tool to facilitate data mediation and integration amongst heterogeneous sources in the context of financial analysts creating corporate profiles. Sources include Lotus Notes, relational databases, and the World Wide Web. C13 applies a unified XML data model to automate integration. By preserving metadata about the source of each datum in the integrated result set, CI" supports source attribution. Users may trace the attribution metadata from the result back to the underlying sources and leverage their expertise in interpreting the data and, if necessary, use their judgment in assessing the authenticity and veracity of results. We present a functional overview of CI', its system architecture including the XML data model, and the integration procedures. We conclude by reflecting on lessons learned.

International Journal of Accounting Information …, 2001

Most major corporations in the U.S. (and a growing number of companies around the world) are reporting some level of financial information on their Web sites. However, it is not clear that the stakeholders are fully satisfied with this Web-based data. The time and effort allocated to the mechanics of Web retrieval are actually increasing because of the difficulty of finding pages and specific data within the enormity of the public Web (over 1 billion pages) or of many corporate intranets. One way to deal with this vast information source would be to automate the Web search mechanics by developing and using intelligent software agents. However, developing these agents in the current Web environment is very problematic. Three factors are preconditions for effective utilization of the Web. First, appropriate metadata representation of financial reporting information on the Web is required that could improve the accuracy of searches (the resource discovery problem). Second, accounting data points within Web pages should be able to be reliably parsed (the attribute recognition problem). Third, standard mechanisms are required that will encourage or require corporations to report in a consistent fashion. The reality of the Web is that it falls far short of a reliable communication medium for accounting and financial information on all three of these factors. The eXtensible Markup Language (XML) provides a method to tag financial information to greatly improve the automation of information location and retrieval, and provides technical solutions to the resource discovery and attribute recognition problems. However, if every company were free to develop its own labels for its XML tags, then the searching for financial information would be only marginally improved. The recent development by a consortium lead by the American Institute of CPAs (AICPA) of the so-called``eXtensible Business Reporting Language'' (XBRL) is an initiative to develop an XML-based Web-based business reporting specification. The widespread adoption of /$ ± see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 1 4 6 7 -0 8 9 5 ( 0 0 ) 0 0 0 1 2 -9 XBRL would mean that both humans and intelligent software agents could operate on financial information disseminated on the Web with a high degree of accuracy and reliability. XBRL provides rich research opportunities, including new taxonomies, database accounting, financial statement assurance, intelligent agents, human/computer interfaces, standard development process, adoption incentives, global adoption, and formal ontologies. D

International Journal of Accounting Information Systems, 2012

Lecture Notes in Computer Science, 2013

Big data, 2017

Significant research challenges must be addressed in the cleaning, transformation, integration, modeling, and analytics of Big Data sources for finance. This article surveys the progress made so far in this direction and obstacles yet to be overcome. These are issues that are of interest to data-driven financial institutions in both corporate finance and consumer finance. These challenges are also of interest to the legal profession as well as to regulators. The discussion is relevant to technology firms that support the growing field of FinTech.

2016

This report presents the motivation and challenge tasks for Year One of the Financial Entity Identification and Information Integration (FEIII) Challenge. It summarizes the process and outcomes as well as lessons learned and future plans.

2007

In the paper we present a design of a decision support system for the fully automated controlled retrieval of fiscal information, which is being continuously collected from a variety of sources, mainly from the global computer network. This system extends the idea of data mining to the notion of knowledge retrieval. The collected information is processed in a knowledge-intensive way using knowledge models of the application domain and can subsequently be used to support various decision-making processes.