Historical Dividends API gives you right away data on dividend payments and dividend calendar. Dividend-paying stocks are often interpreted as a signal for a company's profitability. Successfully performing companies are said to pay dividends to shareholders. The dividend amount of the payment is split into smaller payments made throughout the fiscal year. This happen annually, semi-annually or quarterly. Our historical dividends data is what you need to complete the financial analysis you do on the companies of your choice. It is a valuable tool for making investing decisions and streamlining financial projects. In the upcoming months, ex-dividend date, declaration date and payment date will be added to the data.

If you are interested to learn more, check out the company website: https://tradefeeds.com/historical-dividends-api/

Agora common stock dividends paid from 2019 to 2025. Common stock dividends paid can be defined as the cash outflow for dividends paid on a company's common stock

Global Shares Data Reference data on more than 80K stocks worldwide. Historical data from 2000 onwards. Pay only for the parameters you need. Flexible in customizing our product to the customer's needs. Free test access as long as you need for integration. Reliable sources: issues documents, disclosure website, global depositories data and other open sources. The cost depends on the amount of required parameters and re-distribution right.

Databento provides upcoming and historical corporate actions impacting over 310,000 global securities, including every company announcement and 61 events like dividends, splits, mergers & acquisitions, listings, and more.

Dividends: Upcoming and past dividends, declaration, ex-dividend, record, and payment dates.

Forward and reverse splits: Capital changes like forward splits and reverse splits with effective dates.

Adjustment factors: To back-adjust end-of-day prices, EPS, P/E and other prices for all corporate actions.

Mergers and acquisitions: Ticker changes caused by mergers, acquisitions, demergers, spinoffs, and more.

IPOs and new listings: Upcoming and historical listings like initial public offerings (IPOs), with listing dates.

Listing continuity: Listing continuity events like name changes, delistings, and description changes.

Capital changes: Such as share buybacks, redemptions, bonus issues, and rights issues.

Legal actions: Legal issues like bankruptcy and class action lawsuits, with filing and notice dates.

Announcements: Machine-readable announcements from over 400 sources, timestamped to the second.

Our reference API has the following structure:

Corporate actions provides point-in-time (PIT) corporate actions events with global coverage.

Adjustment factors provides end-of-day price adjustment factors for capital events, spanning multiple currencies for the same event.

Woodseer is the alternative data source for dividend forecasts / estimate data.

Our dataset comprises 5 years of backtestable history (as at Jan 2022), with coverage of 32000+ securities including ADRs and ETFs.

Available via API, FTP and/or login data is highly structured and machine-readable, handling all relevant dates, amounts, currencies, types, frequencies etc.

Clients for our data include some of the world's largest investment banks, market makers, index providers, custodians, hedge funds and asset managers.

United Kingdom PC: Uses: sa: API: PI: Income of Corporations: Dividend Payments data was reported at 42,616.000 GBP mn in Mar 2018. This records an increase from the previous number of 39,899.000 GBP mn for Dec 2017. United Kingdom PC: Uses: sa: API: PI: Income of Corporations: Dividend Payments data is updated quarterly, averaging 15,819.000 GBP mn from Mar 1987 (Median) to Mar 2018, with 125 observations. The data reached an all-time high of 47,285.000 GBP mn in Sep 2015 and a record low of 3,167.000 GBP mn in Dec 1987. United Kingdom PC: Uses: sa: API: PI: Income of Corporations: Dividend Payments data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.AB034: ESA10: Resources and Uses: Private Non Financial Corporations: Primary Income.

United Kingdom NFC: Resources: sa: API: PI: OI: CI: Dividends data was reported at 1.000 GBP mn in Jun 2018. This stayed constant from the previous number of 1.000 GBP mn for Mar 2018. United Kingdom NFC: Resources: sa: API: PI: OI: CI: Dividends data is updated quarterly, averaging 1.000 GBP mn from Mar 1987 (Median) to Jun 2018, with 126 observations. The data reached an all-time high of 2.000 GBP mn in Dec 2010 and a record low of 0.000 GBP mn in Dec 2012. United Kingdom NFC: Resources: sa: API: PI: OI: CI: Dividends data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.AB028: ESA10: Resources and Uses: Non Financial Corporations: Primary Income.

United Kingdom PC: Resources: sa: API: PI: OI: CI: Dividends data was reported at 1.000 GBP mn in Mar 2018. This stayed constant from the previous number of 1.000 GBP mn for Dec 2017. United Kingdom PC: Resources: sa: API: PI: OI: CI: Dividends data is updated quarterly, averaging 1.000 GBP mn from Mar 1987 (Median) to Mar 2018, with 125 observations. The data reached an all-time high of 2.000 GBP mn in Dec 2010 and a record low of 0.000 GBP mn in Dec 2012. United Kingdom PC: Resources: sa: API: PI: OI: CI: Dividends data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.AB034: ESA10: Resources and Uses: Private Non Financial Corporations: Primary Income.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(API) Abrdn Property Income: Renting Out the Future?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

API Group Soaring: (APG) Stock Forecast

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Context

According to Investopedia:

FAANG is an acronym referring to the stocks of the five most popular and best-performing American technology companies: Facebook, Amazon, Apple, Netflix and Alphabet (formerly known as Google). In addition to being widely known among consumers, the five FAANG stocks are among the largest companies in the world, with a combined market capitalization of over $4.1 trillion as of January 2020. Some have raised concerns that the FAANG stocks may be in the midst of a bubble, whereas others argue that their growth is justified by the stellar financial and operational performance they have shown in recent years.

Regardless of the myriad of accolades, comments, and even controversies surrounding the FAANG stocks, they are nevertheless a data science/mining treasure and the bellwether of the NASDAQ index, if not the entire US technology sector.

This Kaggle dataset contains over 20 years of daily historical data for the five FAANG constituents, as retrieved from this free stock API. It is a public-domain dataset that gives the data science practitioners (a.k.a., you!) the full flexibility to derive second-order insights and investment heuristics from it.

Content

Over 20 years of daily historical data (2000-01-01 to 2020-10-01) for the five FAANG stocks: Facebook, Amazon, Apple, Netflix, and Alphabet/Google. For completeness, both raw and adjusted prices are included, along with historical split events and dividend payouts (check out here for how stock market API providers perform price adjustments).

Acknowledgements

Data source: https://www.alphavantage.co/

United Kingdom TE: RC: API: PI: OI: CI: Dividends data was reported at 1,758.000 GBP mn in Jun 2018. This records an increase from the previous number of 1,745.000 GBP mn for Mar 2018. United Kingdom TE: RC: API: PI: OI: CI: Dividends data is updated quarterly, averaging 1,046.500 GBP mn from Mar 1987 (Median) to Jun 2018, with 126 observations. The data reached an all-time high of 2,483.000 GBP mn in Dec 2007 and a record low of 208.000 GBP mn in Sep 1987. United Kingdom TE: RC: API: PI: OI: CI: Dividends data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.AB023: ESA10: Resources and Uses: Total Economy: Primary Income.

Context

Predicting the stock market is one of the most commonly performed projects when someone is learning about ML and Data Science. After all, who wouldn't want to delegate the task of picking stocks to a model and reap the rewards for themselves? However, one of the most difficult and tedious steps to predict what stocks to invest in is actually gathering the data to use. There are so many options and it is important to get sufficient information for each. But, what if you can skip this step and just download a dataset that has all that information easily available for you? Look no further as this is the answer to this problem.

Content

This dataset contains information of 4447 stocks traded under Nasdaq across various exchanges. There is a file that contains information for all 4447 stocks but also has several null fields, which is why I labeled it as full_financial_stocks_raw.csv --it has minimal modifications to the values inside the rows. The second file, dividend_stocks_only.csv, is still a raw-ish style dataset but it only contains stocks that pay out dividends to its shareholders. Interestingly, it seems dividend-paying stocks have more information about them, which explains why this file has significantly fewer rows with null values.

Update: In the next 24 hours, I will be uploading an optimized, feature-engineered dataset that has fewer columns overall and fewer rows with null values. This dataset is intended to be a fully cleaned option to directly feed into ML/DL models.

Acknowledgements

I would like to thank the sources where I obtained my data, which are the FTP Nasdaq Trader website and the Yahoo Finance API.

Inspiration

Analyzing the stock market is one of the most intriguing endeavors I could think of as the ways it can be influenced are so broad and distinct from one another. A news article can influence how investors view a particular company, social media can directly fluctuate a company's share price, and there are numerous calculations and formulas that can show what stocks are worth investing in.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

LON:API Target Price Prediction

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group

Analysis of ‘📊 Financial market screener’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/pierrelouisdanieau/financial-market-screener on 28 January 2022.

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

Context

In this dataset you will find several characteristics on global companies listed on the stock exchange. These characteristics are analyzed by millions of investors before they invest their money.

Analyze the stock market performance of thousands of companies ! This is the objective of this dataset !

Content

Among thse charateristics you will find :

• The symbol : The stock symbol is a unique series of letters assigned to a security for trading purposes.
• The shortname : The name of the company
• The sector : The sector of the company (Technology, Financial services, consumer cyclical...)
• The country : The location of the head office.
• The market capitalisation : Market capitalization refers to the total dollar market value of a company's outstanding shares of stock. It is calculated by multiplying the total number of a company's outstanding shares by the current market price of one share.
• The current ratio : The current ratio is a liquidity ratio that measures a company’s ability to pay short-term obligations. A current ratio that is in line with the industry average or slightly higher is generally considered acceptable. A current ratio that is lower than the industry average may indicate a higher risk of distress or default.
• The beta : Beta is a measure of a stock's volatility in relation to the overall market. A beta greater than 1.0 suggests that the stock is more volatile than the broader market, and a beta less than 1.0 indicates a stock with lower volatility.
• The dividend rate : Represents the ratio of a company's annual dividend compared to its share price. (%)

All this data is public data, obtained from the annual financial reports of these companies. They have been retrieved from the Yahoo Finance API and have been checked beforehand.

Inspiration

This dataset has been designed so that it is possible to build a recommendation engine. For example, from an existing position in a portfolio, recommend an alternative with similar characteristics (sector, market capitalization, current ratio,...) but more in line with an investor's expectations (may be with less risk or with more dividends etc...)

If you have question about this dataset you can contact me

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

The Open API for boat racing odds provided by the Seoul Olympic Memorial National Sports Promotion Foundation is a service that allows you to search for odds information such as single wins and consecutive wins based on the race date and race number. Through this API, users can easily search for odds information for specific dates and races, and can use it to analyze odds change trends or predict games. This data is used for boat racing customers' game predictions, analysis data, and betting trend identification, and is also used as important basic data for ensuring fairness and transparency in racing. Since it is provided as public data, anyone can access it, and it is contributing to increasing the value of data utilization in the sports industry.

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.

FC: RC: sa: API: PR: IF: Dividends data was reported at 1,368.000 GBP mn in Jun 2018. This records an increase from the previous number of 1,365.000 GBP mn for Mar 2018. FC: RC: sa: API: PR: IF: Dividends data is updated quarterly, averaging 604.500 GBP mn from Mar 1987 (Median) to Jun 2018, with 126 observations. The data reached an all-time high of 1,780.000 GBP mn in Dec 2007 and a record low of 100.000 GBP mn in Mar 1987. FC: RC: sa: API: PR: IF: Dividends data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.AB037: ESA10: Resources and Uses: Financial Corporations: Primary Income.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you determine buy or sell? (LON:API Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data