Finnhub is the ultimate stock api in the market, providing real-time and historical price for global stocks with Rest API and websocket. We also support a tons of other financial data like stock fundamentals, analyst estimates, fundamental data and more. Download the file to access balance sheet of Amazon.

Yahoo Finance dataset provides information on top traded companies. It contains financial information on each company including stock ticker and risk scores and general company information such as company location and industry. Each record in the dataset is a unique stock, where multiple stocks can be related to the same company. Yahoo Finance dataset attributes include: company name, company ID, entity type, summary, stock ticker, currency, earnings, exchange, closing price, previous close, open, bid, ask, day range, week range, volume, and much more.

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

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

Dataset Description

The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.

The dataset holds 11,932 documents annotated with 3 labels:

sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" }

The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

We offer three easy-to-understand equity data packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD equity pricing data, standardized financial statement data, and supplementary fundamental datasets.

When you’re ready for launch, it’s a seamless transition to our Silver package for additional data sets, 15-minute delayed equity pricing data, expanded history, and more.

• Historical EOD equity prices & technicals (10 years history)
• Security reference data
• Standardized & as-reported financial statements (5 years history)
• 7 supplementary fundamental data sets

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

When you’re ready to scale, easily move up to the Gold package for our full range of data sets and full history, real-time equity pricing data, premium support options, and much more.

• 15-minute delayed & historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (10 years history)
• 9 supplementary fundamental data sets

Silver Benefits:

• Web API access
• 2,000 API calls/minute limit
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers our complete collection of equity pricing data feeds, from real-time to historical EOD, plus standardized financial statement data and nine supplementary feeds.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time equity prices
• Historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (full history)
• 9 supplementary fundamental data sets

Gold Benefits:

• No exchange fees
• No user reporting or variable per-user exchange fees
• High liquidity (6%+)
• Web API & WebSocket access
• 2,000 API calls/minute limit
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Access to engineering team
• Concierge customer success team
• Comarketing & promotional initiatives

Platinum

Don’t see a package that fits your needs? Our team can design premium custom packages for institutions.

Content

The Global Financial Inclusion Database provides 800 country-level indicators of financial inclusion summarized for all adults and disaggregated by key demographic characteristics-gender, age, education, income, and rural residence. Covering more than 140 economies, the indicators of financial inclusion measure how people save, borrow, make payments and manage risk.

The reference citation for the data is: Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden. 2015. “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, DC.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by ZACHARY STAINES on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset showcases the current operating budget for each department in the town. It is updated monthly to provide better insights into expenditures throughout the town.

Update July 2024

From 1 July 2024, the dataset will no longer publicly distinguish between relevant qualifications or training courses or approved qualifications or training courses.

Update March 2024

From 1 March 2024, the dataset will be updated to include 5 new fields and 1 existing field will also be updated (see help file for details).

Update August 2023

From 24 August 2023, the dataset will be updated to include 1 new field, ABLE_TO_PROVIDE_TFAS, (see help file for details).

Update April 2022

We have replaced the .xlsx file resources for all our datasets. This was required due to the API and web page search functionality no longer being supported for .xlsx files on the Data.Gov platform.

Update January 2022

From 10 January 2022, the field ADV_FASEA _APPROVED_QUAL will be renamed to ADV_APPROVED_QUAL.

Update November 2019 - additional fields

From 21 November 2019, the dataset will be updated to include 7 new fields (see help file for details)

These fields are included in conjunction with the professional standards reforms for financial advisers. More information can be found on the ASIC website https://asic.gov.au/regulatory-resources/financial-services/professional-standards-for-financial-advisers-reforms/.

Note: For most advisers the new fields will be unpopulated on 21 November 2019. As advisers provide this data to ASIC it will appear in the dataset.

Dataset summary

ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and wellbeing by ensuring that Australia’s financial markets are fair and transparent, supported by confident and informed investors and consumers.

Australian Financial Services Licensees are required to keep the details of their financial advisers up to date on ASIC's Financial Advisers Register. Information contained in the register is made available to the public to search via ASIC's Moneysmart website.

Select data from the Financial Advisers Register will be uploaded each week to www.data.gov.au. The data made available will be a snapshot of the register at a point in time. Legislation prescribes the type of information ASIC is allowed to disclose to the public.

The information included in the downloadable dataset is:

• Adviser name
• Adviser number
• Adviser role
• Adviser sub type
• Adviser role status
• Adviser ABN
• Year first provided advice
• Licence name
• Licence number
• Licence ABN
• Licence controlled by
• Adviser start date
• Adviser end date
• Overall registration status
• Registration status under financial licence
• Registration start date under financial licence
• Registration end date under financial licence
• Adviser CPD failure year
• Adviser principal business address suburb
• Adviser principal business address State/Territory
• Adviser principal business address postcode
• Adviser principal business address Country
• Appointing representative name
• Appointing representative number
• Appointing representative ABN
• Disciplinary action start date
• Disciplinary action end date
• Disciplinary action type
• Disciplinary action description (Financial Services and Credit Panel (FSCP) decision)
• Product authorisations (for a full list see the Financial Adviser Register – Help File)
• Ability to provide tax financial advice
• Qualifications and Training
• Memberships
• Further restrictions

Additional information about financial advisers can be found via ASIC's website. Accessing some information may attract a fee.

More information about searching ASIC's registers.

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

This dataset provides the whole set of OECD Quarterly National Accounts data and is recommended for users who wish to query a large amount of data. It is not designed for visualising results using the Table and Chart buttons. To access the ‘Developer API query builder’, click on the ‘Developer API’ button above.

The application programming interface (API), based on the SDMX standard, allows a developer to access the data using simple RESTful URL and HTTP header options for various choices of response formats including JSON. The query filter is generated according to the current data selection. To change the data selection, use the filters on the left.

To get started check the API documentation. For any question contact us

The mapping table between old OECD.Stat and new OECD Data Explorer codes is available here.

These indicators were presented in the previous dissemination system in the QNA dataset.
See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

This dataset represents all purchase orders that were invoiced from July 1, 2011 to present. It does not include other payment types. Purchase orders may be repeated in the table, which indicates that multiple invoices were created for the respective purchase order.The Financial and Administrative Services Department (formerly known as the Finance Department) is made up of two divisions: the Financial Services Division and the Administrative Services Division.The Financial Services Division includes General Accounting, Payroll, Collections, Financial Reporting and Treasury Management. The Administrative Services Division includes Procurement Services and Equipment Services.The Department Director serves as the Chief Financial Officer/Advisor to the City Manager and City Council and is responsible for planning and directing the financial affairs of the City in compliance with federal, state and local financial laws, ordinances, and regulations.The main office of the Financial and Administrative Services Department is located in room 290 on the second floor of the Melvin Municipal Office Building, 300 West Washington Street.Mailing address:PO Box 3136Greensboro, NC 27402-3136

https://upload.wikimedia.org/wikipedia/commons/thumb/a/a4/Mastercard_2019_logo.svg/195px-Mastercard_2019_logo.svg.png" alt="Mastercard">

Mastercard Inc. (stylized as MasterCard from 1979 to 2016 and MasterCard since 2016) is an American multinational financial services corporation headquartered in the Mastercard International Global Headquarters in Purchase, New York. The Global Operations Headquarters is located in O'Fallon, Missouri, a municipality of St. Charles County, Missouri. Throughout the world, its principal business is to process payments between the banks of merchants and the card-issuing banks or credit unions of the purchasers who use the "Mastercard" brand debit, credit, and prepaid cards to make purchases. Mastercard Worldwide has been a publicly traded company since 2006. Prior to its initial public offering, Mastercard Worldwide was a cooperative owned by the more than 25,000 financial institutions that issue its branded cards.

Mastercard, originally known as Interbank from 1966 to 1969 and Master Charge from 1969 to 1979, was created by an alliance of several regional bank card associations in response to the BankAmericard issued by Bank of America, which later became the Visa credit card issued by Visa Inc.

Mastercard is one of the best performing stocks of the decade of 2011-2020

Description

This dataset provides detailed stock price data for IBM and Oracle over a 20-year period from January 2004, to January, 2024. This dataset contains historical stock data for Oracle Corporation (ORCL) and International Business Machines Corporation (IBM). This comprehensive dataset is ideal for financial analysis, investment research, and time series forecasting. The data is recorded at a 5-day interval, offering a comprehensive view of the stock performance of these two technology giants.

Data Overview

The dataset includes the following columns: - Date: The date of the stock price record. - Open: The opening price of the stock on that date. - High: The highest price of the stock on that date. - Low: The lowest price of the stock on that date. - Close: The closing price of the stock on that date. - Adj Close: The adjusted closing price of the stock on that date, accounting for corporate actions. - Volume: The number of shares traded on that date. - Stock: The ticker symbol (IBM or ORCL) indicating which company's stock data is represented.

Use Cases

This dataset is ideal for: - Financial Analysis: Conducting historical performance analysis of IBM and Oracle stocks. - Machine Learning: Training models for stock price prediction. - Comparative Studies: Comparing the market behavior and trends of IBM and Oracle over two decades. - Investment Strategies: Backtesting investment strategies using historical data.

Data Source

The stock data was fetched using the Yahoo Finance API through the yfinance library in Python.

This dataset contains transaction-level data where each point corresponds to the zip code location of the vendor/contractor that has provided products and/or services to the City of Johns Creek since 2010. No longer updated as of fall of 2021.

This data shows a summary of annual unemployment rates for cities within the metro Phoenix area and supports Tempe's Unemployment Rate performance measure.The performance measure page is available at 5.13 Unemployment Rate. Additional InformationSource: https://www.bls.gov/Contact (author): Madalaine McConvilleContact E-Mail (author): madalaine_mcconville@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Extracted for selected citiesPublish Frequency: AnnualPublish Method: ManualData Dictionary

Financial statements: Notes to Financial Statements

This page provides data for the Internal Audit Plan performance measure.Descriptions and status for audit plan projects.The performance measure dashboard is available at 5.14 Audit Completion Rate.Additional InformationSource: Department Data https://www.tempe.gov/government/internal-auditContact: Keith SmithContact E-Mail: keith_smith@tempe.govData Source Type: ExcelPreparation Method: ManualPublish Frequency: AnnuallyPublish Method: Manual

Data Dictionary

List of decisions on the allocation and disallocation of financial aid, taken during the calendar years 2016 and 2017 for the benefit of companies, for schemes managed directly or whose instruction and/or management are delegated by the Île-de-France Region to third parties. This dataset is not exhaustive and is intended to be completed and enriched. The file metadata details the content of each field. Stay informed: subscribe to follow updates to this dataset (“track updates” button below).

Analysis of ‘Portfolio bills of exchange, Autonomous City or Community, province, financial institution. EI (API identifier: 10280)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-22-10280 on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Portfolio bills of exchange, Autonomous City or Community, province, financial institution. Monthly. Provinces. Unpaid Bills of Exchange Statistic

--- Original source retains full ownership of the source dataset ---