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

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.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

Update

Unfortunately, the API this dataset used to pull the stock data isn't free anymore. Instead of having this auto-updating, I dropped the last version of the data files in here, so at least the historic data is still usable.

Content

This dataset provides free end of day data for all stocks currently in the Dow Jones Industrial Average. For each of the 30 components of the index, there is one CSV file named by the stock's symbol (e.g. AAPL for Apple). Each file provides historically adjusted market-wide data (daily, max. 5 years back). See here for description of the columns: https://iextrading.com/developer/docs/#chart

Since this dataset uses remote URLs as files, it is automatically updated daily by the Kaggle platform and automatically represents the latest data.

Acknowledgements

List of stocks and symbols as per https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average

Thanks to https://iextrading.com for providing this data for free!

Terms of Use

Data provided for free by IEX. View IEX’s Terms of Use.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-07-23 to 2025-07-22 about stock market, average, industry, and USA.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-07-18 about NASDAQ, composite, stock market, indexes, and USA.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

Context

With the sole mission to democratize financial data, Finnhub is excited to release the new Financials as Reported dataset for bulk download. The data is cleaned and sourced directly from SEC filings from 2010-2020.

If you don't need bulk download, you can query this data for free on our website: https://finnhub.io/docs/api#financials-reported. We also provide various type of financial data such as global fundamentals, deep historical tick data, estimates and alternative data.

Finnhub Stock API https://finnhub.io/terms-of-service

List of companies in the S&P 500 (Standard and Poor's 500). The S&P 500 is a free-float, capitalization-weighted index of the top 500 publicly listed stocks in the US (top 500 by market cap). The ...

Comprehensive dataset of 1 Finance brokers in Free municipal consortium of Syracuse, Italy as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-07-21 about VIX, volatility, stock market, and USA.

Official Course URL: udemy.com/course/sql-for-financial-data-analysis/ Course Overview: Unlock the power of SQL for financial data analysis and reporting. This course is tailored for non-tech professionals who want to streamline their analytics and reporting capabilities. Learn to extract and process financial data, prepare detailed reports like Profit & Loss Statements and Balance Sheets, and calculate critical financial ratios through practical exercises. What You ll Learn: - SQL Basics: Master database querying techniques for financial data. - Report Preparation: Create Profit & Loss Statements, Balance Sheets, and Cash Flow Statements. - Key Analytics: Calculate and interpret profitability, efficiency, and liquidity ratios. - Database Skills: Gain hands-on experience without prior technical expertise. Course Benefits: - Practical Applications: Apply SQL to real-world financial scenarios. - Independent Reporting: Reduce reliance on system-generated reports. - Career Advancem

1 INTRODUCTION 1.1 Study Assumptions and Market Definition 1.2 Scope of the Study

   2 RESEARCH METHODOLOGY

   3 EXECUTIVE SUMMARY

   4 MARKET DYNAMICS
   4.1 Market Drivers
   4.1.1 Rise in Duty-Free Retailing Stores with Technology Integration will accelerate market growth
   4.1.2 Rise in Foreign Tourists to Boost the Market Growth
   4.2 Market Restraints
   4.2.1 Convenience and Variety of Online Shopping as more and more Customers Purchase Online
   4.2.2 Usage of Unsustainable Goods for Storage Affecting Market Growth
   4.3 Market Opportunities
   4.3.1 Increased Digitalization To Boost the Demand for Duty Free Products
   4.4 Porter's Five Forces Analysis
   4.4.1 Bargaining Power of Suppliers
   4.4.2 Bargaining Power of Buyers/Consumers
   4.4.3 Threat of New Entrants
   4.4.4 Threat of Substitute Products
   4.4.5 Intensity of Competitive Rivalry
   4.5 Impact of COVID-19 on the market

   5 MARKET SEGMENTATION
   5.1 By Product Type
   5.1.1 Fashion and Accessories
   5.1.2 Jewellery and Watches
   5.1.3 Wine and Spirits
   5.1.4 Food and Confectionery
   5.1.5 Fragrances and Cosmetics
   5.1.6 Tobacco
   5.1.7 Other Product Types
   5.2 By Distribution Channel
   5.2.1 Airports
   5.2.2 Airlines
   5.2.3 Ferries
   5.2.4 Other Distribution Channels
   5.3 By Geography
   5.3.1 North America
   5.3.2 South America
   5.3.3 Europe
   5.3.4 Asia-Pacific
   5.3.5 Middle East & Africa

   6 COMPETITIVE LANDSCAPE
   6.1 Market Concentration Overview
   6.2 Company Profiles
   6.2.1 Dufry
   6.2.2 Lotte Duty Free
   6.2.3 Lagardere Travel Retail
   6.2.4 DFS Group
   6.2.5 The Shilla Duty Free
   6.2.6 King Power International Group
   6.2.7 China Duty Free Group
   6.2.8 Dubai Duty Free
   6.2.9 Duty Free Americas
   6.2.10 Sinsegae Duty Free
   6.2.11 WH Smith*

   7 FUTURE MARKET TRENDS

   8 DISCLAIMER AND ABOUT US

   The Global Duty Free & Travel Retail Market Report is segmented by product type and distribution channel, offering a comprehensive industry analysis. The market is a significant revenue generator for aviation, tourism, and other travel-related industries. Airports, in particular, derive a considerable portion of their income from duty-free and travel retailing. Despite challenges such as trade tensions and protectionism between countries, the market is seeing an increased demand for duty-free alcohol, spurred by diversifying consumer buying habits and rising spending among the middle-class population.<br><br>The market's growth is fueled by the rapidly expanding international tourism market and the increasing number of new air routes in Asian countries. However, global currency fluctuations could potentially hamper product demand. The market is segmented by type, with perfumes expected to dominate the global duty-free retail market share. The rising popularity of premium beauty products is also fueling demand in the cosmetics space. In terms of sales channels, airports dominate product sales worldwide.<br><br>The Asia Pacific market is anticipated to witness significant growth over the forecast period, with Europe and North America also expected to see growth. The South America and Middle East and Africa markets are likely to experience an upward trend due to rising consumer demand for premium/luxury perfumes. Duty-Free & Travel Retail market share, size, and revenue growth rate statistics provide a comprehensive market overview, including market forecast and market trends. A sample of this industry report is available as a free report PDF download.<br><br>The industry outlook remains positive, with market leaders driving the market growth. Market segmentation by product type and distribution channel offers detailed market data. The market value is projected to rise, supported by industry statistics and market predictions. Research companies provide valuable industry information and industry research, contributing to market review and market analysis. The report example highlights the importance of understanding market dynamics to capitalize on growth opportunities.

   The Global Duty Free & Travel Retail Report Covers the Following Regions: NA, North America, North American, Northern America, Northern American, SA, South America, South American, EU, Europe, European, APAC, Asia-Pacific, Asian, MEA, Middle East and Africa, Middle Eastern and African, MENA, Middle East, Middle Eastern, Africa, African, Americas, American

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This is a spreadsheet of 1 of 10 companies in the shoe industry. Highlighting COGS, Total Revenue, Market share and Industry share.

Graph and download economic data for Financial Market: Share Prices for China (SPASTT01CNM661N) from Jan 1999 to May 2025 about stock market and China.

Comprehensive dataset of 86 Financial consultants in Free municipal consortium of Syracuse, Italy as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Financial Access Survey (FAS) indicators are expressed as ratios to GDP, land area, or adult population to facilitate cross-economy comparisons. Provision of FAS data is voluntary.

The Financial Access Survey draws on the IMF's Monetary and Financial Statistics Manual and Compilation Guide (http://data.imf.org/api/document/download?key=61061648)

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