The Local Government Budget and Financial Report Database provides the public access to current and official versions of budgets for Counties, County Ag Extensions, County/City Assessors, County Hospitals, & Townships, Benefited Fire, Lighting, Water Districts, and Misc Budgets (DART, EMC, E911, Rural Improvement Zones, Recreational Water, Sanitary Sewer Districts). The public has access to multiple forms that are part of the budget. The database also provides the public access to annual financial reports for Counties. The site provides both GAAP and Cash Financial Reports for multiple fiscal years.

The data sets below provide selected information extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

Comprehensive database of over 100,000 financial filings from 8,000+ European companies

Our Financial API provides access to a vast collection of historical financial statements for over 50,000+ companies listed on major exchanges. With this powerful tool, you can easily retrieve balance sheets, income statements, and cash flow statements for any company in our extensive database. Stay informed about the financial health of various organizations and make data-driven decisions with confidence. Our API is designed to deliver accurate and up-to-date financial information, enabling you to gain valuable insights and streamline your analysis process. Experience the convenience and reliability of our company financial API today.

Access historical and point-in-time financial statements, ratios, multiples, and press releases, with LSEG's S&P Compustat Database.

This is a compiled datasets comprising of data from various companies' 10-K annual reports and balance sheets. The data is a longitudinal or panel data, from year 2009-2022(/23) and also consists of a few bankrupt companies to help for investigating factors. The names of the companies are given according to their Stocks. Companies divided into specific categories.

Financial Times Interactive Data LLC offers a vast repository of economic and financial data, providing valuable insights into global markets and trading. With a focus on delivering timely and accurate information, the company has established itself as a go-to source for financial institutions, investors, and researchers seeking to stay ahead of the curve.

our vast database is comprised of historic financial statements, economic indicators, and proprietary data from leading sources, including government agencies, regulatory bodies, and industry associations. By providing access to this trove of information, Financial Times Interactive Data LLC enables its clients to make informed decisions, identify trends, and uncover new opportunities in the rapidly evolving world of finance.

This dataset includes financial information derived from adopted budgets reported by Connecticut municipalities to the Office of Policy and Management via OPM’s Fiscal Health Monitoring System (FHMS) portal. The dataset includes key summary data from the budgets for all 169 municipalities and the City of Groton dating back to FY22.

For questions regarding the budgetary information included in a municipality’s adopted budget, please contact the municipality directly. For questions or comments on the FHMS please contact us at opm-fhms@ct.gov

The complete adopted municipal budgets filed with OPM dating back to FY 2022 are available via the Municipal Budget Database System located at https://opm-mbds.ct.gov/ords/r/fhms/mbds/home.

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut.

Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. This database of information includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The most recent edition is for the Fiscal Years Ended 2014-2018 published in January, 2020.

This dataset is from the SEC's Financial Statements and Notes Data Set.
It was a personal project to see if I could make the queries efficient.
It's just been collecting dust ever since, maybe someone will make good use of it.
Data is up to about early-2024.
It doesn't differ from the source, other than it's compiled - so maybe you can try it out, then compile your own (with the link below).
Dataset was created using SEC Files and SQL Server on Docker.
For details on the SQL Server database this came from, see: "dataset-previous-life-info" folder, which will contain: - Row Counts - Primary/Foreign Keys - SQL Statements to recreate database tables - Example queries on how to join the data tables. - A pretty picture of the table associations. Source: https://www.sec.gov/data-research/financial-statement-notes-data-sets

Happy coding!

ES: Other Financial Corporation: Capital and Reserves data was reported at 433,344.000 EUR mn in 2014. This records an increase from the previous number of 367,706.000 EUR mn for 2013. ES: Other Financial Corporation: Capital and Reserves data is updated yearly, averaging 361,112.500 EUR mn from Dec 2005 (Median) to 2014, with 10 observations. The data reached an all-time high of 433,344.000 EUR mn in 2014 and a record low of 337,230.000 EUR mn in 2012. ES: Other Financial Corporation: Capital and Reserves data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Spain – Table ES.IMF.FSI: Sectoral Financial Statement: Balance Sheet: Annual.

This dataset provides a structured and machine-readable collection of financial statements filed with the Companies Registration Office (CRO) in Ireland. It currently includes financial statements for the year 2022, with additional years to be added as they become available. The dataset aligns with the European Union’s Open Data Directive (Directive (EU) 2019/1024) and the Implementing Regulation (EU) 2023/138, which designates company and company ownership data as a high-value dataset. It is available for bulk download and API access under the Creative Commons Attribution 4.0 (CC BY 4.0) licence, allowing unrestricted reuse with appropriate attribution. By increasing transparency and enabling data-driven insights, this dataset supports public sector initiatives, financial analysis, and digital services development. The API endpoints can be accessed using these links - Query - https://opendata.cro.ie/api/3/action/datastore_search Query (via SQL) - https://opendata.cro.ie/api/3/action/datastore_search_sql

The Corporate Financial Fraud project is a study of company and top-executive characteristics of firms that ultimately violated Securities and Exchange Commission (SEC) financial accounting and securities fraud provisions compared to a sample of public companies that did not. The fraud firm sample was identified through systematic review of SEC accounting enforcement releases from 2005-2010, which included administrative and civil actions, and referrals for criminal prosecution that were identified through mentions in enforcement release, indictments, and news searches. The non-fraud firms were randomly selected from among nearly 10,000 US public companies censused and active during at least one year between 2005-2010 in Standard and Poor's Compustat data. The Company and Top-Executive (CEO) databases combine information from numerous publicly available sources, many in raw form that were hand-coded (e.g., for fraud firms: Accounting and Auditing Enforcement Releases (AAER) enforcement releases, investigation summaries, SEC-filed complaints, litigation proceedings and case outcomes). Financial and structural information on companies for the year leading up to the financial fraud (or around year 2000 for non-fraud firms) was collected from Compustat financial statement data on Form 10-Ks, and supplemented by hand-collected data from original company 10-Ks, proxy statements, or other financial reports accessed via Electronic Data Gathering, Analysis, and Retrieval (EDGAR), SEC's data-gathering search tool. For CEOs, data on personal background characteristics were collected from Execucomp and BoardEx databases, supplemented by hand-collection from proxy-statement biographies.

Compare financial information of companies from different industries around the globe with Worldscope Fundamentals, providing essential insights and analysis.

The COFI database includes power-generation projects in Belt and Road Initiative (BRI) countries financed by Chinese corporations and banks that reached financial closure from 2000 to 2020. Types of financing include debt and equity investment, with the latter including greenfield foreign direct investments (FDI) and cross-border mergers and acquisitions (M&As). COFI is consolidated using nine source databases using both automated join method in R Studio, and manual joining by analysts. The database includes power plant characteristics data and investment detail data. It captures 430 power plants in 76 BRI countries, including 220 equity investment transactions and 253 debt investment transactions made by Chinese investors. Key data points for financial transactions in COFI include the financial instrument (equity or debt), investor name, amount, and financial close year. Key technical characteristics tracked for projects in COFI include name, installed capacity, commissioning year, country, and primary fuel type. This project is a collaboration among the Boston University Global Development Policy Center, the Inter-American Dialogue, the China-Africa Research Initiative at the Johns Hopkins University (CARI), and the World Resources Institute (WRI). The detailed methodology is given in the World Resources Institute publication “China Overseas Finance Inventory”.

This online database contains the quarterly financial reports for regulated banks in Iowa, starting in 1999.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in the majority of economies was 1,000 individuals.

Mode of data collection

Landline and cellular telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

This document is part of the source library for NRGI's National Oil Company Database, an open database of facts and figures on more than 70 national oil companies worldwide. See the full database at https://nationaloilcompanydata.org/.

ES: Non Financial Corporations: Liabilities: Debt: Debt Securities data was reported at 21,958.000 EUR mn in 2014. This records an increase from the previous number of 20,465.000 EUR mn for 2013. ES: Non Financial Corporations: Liabilities: Debt: Debt Securities data is updated yearly, averaging 13,798.500 EUR mn from Dec 2005 (Median) to 2014, with 10 observations. The data reached an all-time high of 21,958.000 EUR mn in 2014 and a record low of 11,036.000 EUR mn in 2005. ES: Non Financial Corporations: Liabilities: Debt: Debt Securities data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Spain – Table ES.IMF.FSI: Sectoral Financial Statement: Balance Sheet: Annual.

This document is part of the source library for NRGI's National Oil Company Database, an open database of facts and figures on more than 70 national oil companies worldwide. See the full database at https://nationaloilcompanydata.org/.