Electronic Data Gathering, Analysis, And Retrieval Research Articles

The effect of modern information technology on corporate payout policy. Evidence from EDGAR implementation

PurposeThis study uses the Electronic Data Gathering Analysis and Retrieval (EDGAR) implementation as an information shock to examine its effect on corporate payout policy.Design/methodology/approachThis study uses a generalized difference-in-differences approach to assess the causal impact of EDGAR implementation on the US publicly traded firms’ payout policy for a period from 1990 to 1999. The approach captures the difference between changes in the dividend policy of firms subjected to EDGAR implementation (treated firms) and those not subjected to the implementation (control firms).FindingsFirms increase payout ratios and the likelihood of paying dividends after the implementation of EDGAR. Notably, these effects are more pronounced in firms characterized by high agency problems ex-ante.Practical implicationsPolicies designed to improve a firm’s information environment may yield divergent effects on corporate payout policy. Consequently, in countries aiming to promote cash dividends, policymakers seeking to enhance the firm information environment should carefully consider initiatives that will improve minority investors’ access to corporate information.Originality/valueThe findings contribute to the real effects of EDGAR implementation on firm policies, addressing the ambiguity surrounding the economic consequences of EDGAR adoption. This paper also contributes to the existing literature on the impact of information shock on corporate payouts. The findings emphasize the multifaceted influence of information shock on corporate payouts.

Fragmented Securities Regulation, Information-Processing Costs, and Insider Trading

Using a unique setting where stand-alone banks submit filings to bank regulators instead of the U.S. Securities and Exchange Commission (SEC), we examine the consequences of fragmented securities regulation for information-processing costs and opportunistic insider trading. We find the market reaction to insider-trading filings on FDICconnect is less timely than to those on SEC’s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, suggesting FDICconnect generates higher information-processing costs. We also find only large investors trade more on insider-trading filings on FDICconnect than on insider-trading filings on SEC EDGAR, thus extracting benefits from the delayed market reaction to insider-trading filings on FDICconnect. Finally, we find increased insider selling in stand-alone banks prior to negative earnings news, suggesting insiders’ opportunistic use of private information. These findings collectively suggest regulatory fragmentation undermines market efficiency and distorts the level playing field. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by the Eugene Lang Junior Faculty Fellowship. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4903 .

Demonstrating Retrieval of Financial Information in XBRL

The Securities and Exchange Commission (SEC) has upgraded the existing system of Electronic Data Gathering, Analysis, and Retrieval (EDGAR), to Interactive Data Electronic Applications (IDEA) platform, using eXtensible Business Reporting Language (XBRL). In January 2009, the SEC issued its final mandate for XBRL adoption and the conversion target dates for all firms. This conversion enables users to retrieve listed companies’ financial statement information at two levels, document and the data element, compared to the document level alone under the existing system of EDGAR. Beneficial to all users, this change is particularly important to resource-strapped entities such as small businesses, and universities. The result is user friendly for students researching information. With simply the industry standard tool of Microsoft Excel, without expensive proprietary XBRL modules, detailed information is now available, quickly, efficiently and at minimum cost. This paper explains the basic concept of XBRL and demonstrates the ease of the retrieval process in Microsoft Excel.

The Real Effects of Modern Information Technologies: Evidence from the EDGAR Implementation

ABSTRACTUsing the implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system from 1993 to 1996 as a shock to information dissemination technologies, we examine how a significant reduction in disclosure processing costs affects the real economy. We find that the EDGAR implementation leads to an increase in corporate investment and that this effect is concentrated in value firms. We provide evidence that improved equity financing and enhanced managerial incentives are likely the underlying mechanisms. Specifically, the EDGAR implementation leads to an increase in a firm's stock liquidity, a decrease in the cost of equity capital, and an increase in the level of equity financing. Consistent with the monitoring effect of broad information dissemination, the EDGAR implementation leads to an increase in a firm's operating performance. Our findings suggest that it is important to consider information dissemination beyond information production when examining the real effects of corporate disclosures.

Association of the chemical composition and nutritional value of forage resources in Colombia with methane emissions by enteric fermentation

In the livestock sector, strategies are available to mitigate gas emissions, such as methane, one of the alternatives that have shown potential correspondence to changes in the composition of the diet. The main aim of this study was to analyze the influence of methane emissions with data on enteric fermentation obtained from the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database and based on forecasts of methane emissions by enteric fermentation with an autoregressive integrated moving average (ARIMA) model and the application of statistical tests to identify the association between methane emissions from enteric fermentation and the variables of the chemical composition and nutritional value of forage resources in Colombia. The results reported positive correlations between methane emissions and the variables ash content, ethereal extract, neutral detergent fiber (NDF), and acid detergent fiber (ADF) and negative correlations between methane emissions and the variables percentage of unstructured carbohydrates, total digestible nutrients (TDN), digestibility of dry matter, metabolizable energy (MERuminants), net maintenance energy (NEm), net energy gain (NEg), and net lactation energy (NEI). The variables with the most significant influence on the reduction of methane emissions by enteric fermentation are the percentage of unstructured carbohydrates and the percentage of starch. In conclusion, the analysis of variance and the correlations between the chemical composition and the nutritive value of forage resources in Colombia help to understand the influence of diet variables on methane emissions of a particular family and with it in the application of strategies of mitigation.

RegTech Adoption and the Cost of Capital

This paper studies the cost of capital effect of a major regulatory technology, or RegTech, event: the staggered implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system of the Securities and Exchange Commission in the period from 1993 to 1996. This event represents a largely exogenous shock to corporate information dissemination technologies, resulting in a considerable reduction in information acquisition costs for investors. Using a difference-in-differences research design, we show that the cost of equity capital declines substantially after a firm switches from paper filing to mandatory electronic filing in EDGAR. The effect is stronger for small firms and firms with low analyst coverage and low institutional ownership. We identify three channels through which EDGAR affects a firm’s cost of capital: the liquidity, risk-taking, and corporate governance channels. EDGAR implementation also improves a firm’s investment efficiency significantly. We find evidence that the marginal value of a firm’s capital investment and cash is higher during the post-EDGAR period. This paper was accepted by Suraj Srinivasan, accounting. Funding: C. Lin acknowledges financial support from the National Science Foundation of China [Grant 72192841] and the Research Grants Council of the Hong Kong Special Administration Region, China [Project HKU R7054-18 and T35/710/20R]. S. Lai acknowledges financial support from the Ministry of Science and Technology, Taiwan [Grants 107-2410-H-002-038-MY3 and 110-2410-H-002-079-MY2] and the E. Sun Academic Award. X. Ma acknowledges research support from the University of Macau [Grants SRG2019-00158-FBA and MYRG2020-00259-FBA]. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022.4660 .

Testing Disagreement Models

ABSTRACTWe provide plausibly identified evidence for the role of investor disagreement in asset pricing. Our natural experiment exploits the staggered implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, which induces a reduction in investor disagreement. Consistent with models of investor disagreement, EDGAR inclusion helps resolve disagreement around information events, leading to stock price corrections. The reduction in disagreement following EDGAR inclusion also reduces stock price crash risk, especially among stocks with binding short‐sale constraints and high investor optimism.

Search-Based Peer Groups and Commonality in Liquidity

Abstract We examine search-based peer (SBP) groups proposed by Lee, Ma, and Wang (2015) and their relationship with commonality in liquidity. Our results confirm that SBP affiliation is a significant determinant of commonality in liquidity and, unlike market- and industry-commonality, SBP-commonality has been increasing over the past 15 years. We separate retail from institutional investor queries by tracing the IP locations of Electronic Data Gathering, Analysis, and Retrieval (EDGAR) searches. Our results show that retail investors are responsible for roughly 85% of the EDGAR searches that generate SBP groups. Overall, our study provides new evidence of a significant demand-side commonality associated with SBP affiliations.

Background Noise? TV Advertising Affects Real-Time Investor Behavior

Using minute-by-minute TV advertising data covering some 300 firms, 327,000 ads, and $20 billion in ad spending, we study the real-time effects of TV advertising on investors’ searches for online financial information and subsequent trading activity. Our identification strategy exploits the fact that viewers in different U.S. time zones are exposed to the same programming and national advertising at different times, allowing us to control for contemporaneous confounding events. We find that an average TV ad leads to a 3% increase in EDGAR (Electronic Data Gathering, Analysis, and Retrieval) system queries and an 8% increase in Google searches for financial information within 15 minutes of the airing of that ad. These searches translate into larger trading volume on the advertiser’s stock, driven primarily by retail investors. The findings on retail investor ad-induced trading are corroborated with hourly data from Robinhood, a popular retail trading platform. We also show that ads induce searches and trading of companies other than the advertiser, including of close rivals. Altogether, our findings suggest that advertising originally intended for consumers has a nonnegligible effect on financial markets. This paper was accepted by Karl Diether, finance.

Localized Information Acquisition

his study identifies previously undocumented geography-related diversity in investors’ information acquisition behavior around days with earnings announcements. IP geo-localization of the server log of the SEC’s EDGAR (Electronic Data Gathering, Analysis, and Retrieval) system shows that, around these days, local (non-local) investors’ information acquisition behavior is largely anticipatory (reactionary) in nature. Specifically, local investors are found to sharply increase their information acquisition activities on days leading up to earnings announcements, triggered by intertemporal changes in a firm’s earnings uncertainty and varying predictably with exogenous firm relocation. Their non-local counterparts, on the other hand, generally hold off until the day of the earnings announcement, when new filings information becomes available. Greater pre-announcement information acquisition diversity is predicted to elicit stronger stock price reactions on earnings announcement days. Consistent with this prediction, we find that the earnings announcement-day premium is higher when pre-announcement information acquisition by local investors is higher, controlling for time-variant and -invariant firm characteristics.

Is Image Encoding Beneficial for Deep Learning in Finance?

In 2012, Securities and Exchange Commission (SEC) mandated all corporate filings for any company doing business in the U.S. be entered into the electronic data gathering, analysis, and retrieval (EDGAR) system. In this work, we are investigating ways to analyze the data available through the EDGAR database. This may serve portfolio managers (pension funds, mutual funds, insurance, and hedge funds) to get automated insights into companies they invest in, to better manage their portfolios. The analysis is based on artificial neural networks applied to the data. In particular, one of the most popular machine learning methods, the convolutional neural network (CNN) architecture, originally developed to interpret and classify images, is now being used to interpret financial data. This work investigates the best way to input data collected from the SEC filings into a CNN architecture. We incorporate accounting principles and mathematical methods into the design of three image encoding methods. Specifically, two methods are derived from accounting principles (sequential arrangement, category chunk arrangement) and one is using a purely mathematical technique [the Hilbert vector arrangement (HVA)]. In this work, we analyze fundamental financial data as well as financial ratio data and study companies from the financial, healthcare, and information technology sectors in the United States. We find that using imaging techniques to input data for CNN works better for financial ratio data but is not significantly better than simply using the 1-D input directly for fundamental data. We do not find the HVA technique to be significantly better than other imaging techniques.

More is Less: Publicizing Information and Market Feedback*

Abstract We study whether and how publicizing internal information affects the value of financial markets to the real economy. By publicizing corporate filings, the Securities and Exchange Commission’s EDGAR (Electronic Data Gathering, Analysis, and Retrieval) web platform reduces the cost of acquiring internal information for outsiders and so makes it relatively less attractive to gather external information. We find that the staggered introduction of EDGAR reduced the sensitivity of firm investment to prices, consistent with prices being less informative to managers due to the crowding out of external information gathering. This crowding out effect is stronger when outsiders’ incentives for gathering information are stronger and for firms that rely more on external information. Our findings suggest that policies designed to “level the playing field” by publicizing internal information can have significant unintended consequences by reducing the informativeness of prices for real decisions.

Investor Attention, Divergence of Opinions, and Stock Returns

Using a direct measure of investor attention generated from the Securities and Exchange Commission’s EDGAR (Electronic Data Gathering, Analysis, and Retrieval) log files, the authors revisit the stock return predictability of the divergence of opinions in the presence of varying degree of investor attention and information acquisition. They document a positive relationship between the divergence of opinions and future stock returns, consistent with the risk hypothesis, as opposed to the overvaluation hypothesis. More importantly, the authors find that the predictive power of divergence of opinions is more pronounced in stocks with lower investor attention. They further document the construction and profitability of divergence of opinions portfolios augmented with investor attention. A portfolio that goes long on stocks with low investor attention and the highest divergence of opinions and short on stocks with low attention and the lowest divergence of opinions generates a Fama-French 5-factor monthly alpha of 1.14%.

Analyst Information Acquisition via EDGAR

We identify analysts’ information acquisition patterns by linking EDGAR (Electronic Data Gathering, Analysis, and Retrieval) server activity to analysts’ brokerage houses. Analysts rely on EDGAR in 24% of their estimate updates with an average of eight filings viewed. We document that analysts’ attention to public information is driven by the demand for information and the analysts’ incentives and career concerns. We find that information acquisition via EDGAR is associated with a significant reduction in analysts’ forecasting error relative to their peers. This relationship is likewise present when we focus on the intensity of analyst research. Attention to public information further enables analysts to provide forecasts for more time periods and more financial metrics. Informed recommendation updates are associated with substantial and persistent abnormal returns, even when the analyst accesses historical filings. Analysts’ use of EDGAR is associated with longer and more informative analysis within recommendation reports. This paper was accepted by Shiva Rajgopal, accounting.

OpenEDGAR: Open Source Software for SEC EDGAR Analysis

OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC). OpenEDGAR is built on the Django application framework, supports distributed compute across one or more servers, and includes functionality to (i) retrieve and parse index and filing data from EDGAR, (ii) build tables for key metadata like form type and filer, (iii) retrieve, parse, and update CIK to ticker and industry mappings, (iv) extract content and metadata from filing documents, and (v) search filing document contents. OpenEDGAR is designed for use in both academic research and industrial applications, and is distributed under MIT License.

The Annual Report Algorithm: Retrieval of Financial Statements and Extraction of Textual Information

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.

Retrieving Financial Information in XBRL on the Next-Generation Edgar

The Securities and Exchange Commission (SEC) has upgraded the old Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, to the Interactive Data Electronic Applications (IDEA) platform, or the Next-Generation EDGAR (New EDGAR). In January 2009, the SEC issued its final mandate for XBRL adoption and the conversion target dates for all firms. This conversion enables users to retrieve listed companies’ financial statement information at both document level and data element level. This paper reviews the basic concepts of XBRL, reports the current compliance status of the SEC XBRL conversion mandate. The main contribution of this article is to demonstrate how to retrieve data from the New EDGAR and process them with Microsoft Excel 2013.

Automating the Process of Taxonomy Creation and Comparison of Taxonomy Structures

The ability to automatically extract information from the footnotes of financial statements simplifies access to critical information concerning public companies. However, extraction can be particularly challenging due to great variations in the filing structure and terminologies used. Hierarchical formalization of text becomes a necessity in such circumstances. This is facilitated by the creation of a valid taxonomy. The objectives of this paper are threefold: (1) to develop a semiautomatic method of taxonomy creation; (2) to compare the structure of the taxonomy created with the XBRL US GAAP taxonomy and (3) to demonstrate how the tool developed as a part of this process can be used for more exploratory research. Pension plan footnotes of 10K statements have been used to demonstrate the use of this method. To create a taxonomy, we first collected 10K statements from SEC EDGAR (Electronic Data Gathering, Analysis and Retrieval) then extracted pension footnotes, and restructured the data. We then applied the Hierarchical clustering algorithm to this data to create the taxonomy structure. Comparison of the taxonomy developed with the XBRL taxonomy reveals some differences. In general the reporting trends of companies reveal a greater level of aggregation. Pension footnote structures of forty five randomly selected companies were compared across ten years. Several instances were found where the company has added new terms or a completely new section to the footnote or a term is missing. The contribution of this paper are: (i) a method to formalize and partially automate the complex and time consuming process of taxonomy creation using historical data has been proposed; (ii) a generic parsing tool as a part of the taxonomy creation process is developed; (iii) structural differences between the official XBRL US GAAP taxonomy and taxonomy using historical data are demonstrated (iv) potential use of the parsing and matching tool for other exploratory research in accounting is shown.

EDGAR: the investor's guide to making better investments

This book includes techniques of the pros for getting fast, free, and valuable data using the SEC's Web-based EDGAR system. More than one million investors each week rely on the SEC's EDGAR (Electronic Data Gathering, Analysis, and Retrieval) system to learn what is really going on inside publicly traded companies. But because the dense boilerplate and legalese is often more arcane and confusing than informative, many come away with little useful information. EDGAR: The Investor's Guide to Making Better Investments shows investors how to quickly find and interpret the data they need. Explaining how to look for essential facts in obtuse SEC filings, this do-it-yourself investor's handbook discusses: methods for starting out and using EDGAR; how to understand what is - and isn't - being said; and methods for quickly finding key information.

EDGAR-Analyzer: automating the analysis of corporate data contained in the SEC's EDGAR database

Publicly owned companies, their officers and major investors are required to file regular disclosures with the Securities and Exchange Commission (SEC). To improve accessibility to these public documents, the SEC began developed the EDGAR (Electronic Data Gathering, Analysis and Retrieval) electronic disclosure system. This system provides ready, free access to all electronic filings made since 1994. The paper describes a tool that automates the analysis of SEC filings, emphasizing the unstructured text sections of these documents. To illustrate the capabilities of the EDGAR-Analyzer program, results of a large-scale case study of corporate Y2K disclosures in 18,595 10K filings made from 1997 to 1999 is presented.