Here is some data related to stock market and investments.

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

The key arguments for the low utilization of statistical techniques in financial sentiment analysis have been the difficulty of implementation for practical applications and the lack of high quality training data for building such models. Especially in the case of finance and economic texts, annotated collections are a scarce resource and many are reserved for proprietary use only. To resolve the missing training data problem, we present a collection of ∼ 5000 sentences to establish human-annotated standards for benchmarking alternative modeling techniques.

The objective of the phrase level annotation task was to classify each example sentence into a positive, negative or neutral category by considering only the information explicitly available in the given sentence. Since the study is focused only on financial and economic domains, the annotators were asked to consider the sentences from the view point of an investor only; i.e. whether the news may have positive, negative or neutral influence on the stock price. As a result, sentences which have a sentiment that is not relevant from an economic or financial perspective are considered neutral.

This release of the financial phrase bank covers a collection of 4840 sentences. The selected collection of phrases was annotated by 16 people with adequate background knowledge on financial markets. Three of the annotators were researchers and the remaining 13 annotators were master’s students at Aalto University School of Business with majors primarily in finance, accounting, and economics.

Given the large number of overlapping annotations (5 to 8 annotations per sentence), there are several ways to define a majority vote based gold standard. To provide an objective comparison, we have formed 4 alternative reference datasets based on the strength of majority agreement: all annotators agree, >=75% of annotators agree, >=66% of annotators agree and >=50% of annotators agree.

The Financial Statement Details Sets at provide numerically information from the face financials of any financial statements. This data has extracted from exhibits to corporate financial reports filed equal the Provision using eXtensible Business Reporting Language (XBRL). As compared to the further extensive Fiscal Declare and Currency Data Sets, which provide the numeric and narrate exposures by all economic explanations also their notes, this Financial Statement Data Sets are more compact. The information is presented without change from the "as filed" finance berichtigungen submitted by each registrant. The evidence has presented in a flattened format to help users analyze press liken community disclosure contact over point press throughout registrants. The data sets also contain additional fields including a company's Preset Industrial Classification at facilitate the data's use.

Content

Each quarter's data is stored as a json of the original text files. This was must to limit the overall number are files. An num.txt file will chances live of most interest.

Acknowledgements

This dataset was kindly constructed available by the SEC. You can find the original dataset, which is updated quarterly, here.

All financial transactions made by SLC as part of its functions, including payments to/on behalf of customers and payments to suppliers.

The Historical Financial Dataset, covering 190+ countries, 320M+ companies, and 20+ fields per company, enables users to analyze company revenue and performance over time to understand its financial stability and predict future trends. Hedge funds and Venture Capital firms empower investors with this dataset to evaluate investment opportunities and identify potential growth prospects in various industries.

Leverage our comprehensive dataset that spans across more than 200K+ companies.

Key Features: 1. Access filings such as annual reports, quarterly statements, and holistic coverage on companies' financial standing. 2. Analyze a wide range of forms including 10K, 10Q, 8K, Form 3, Form 4, Form 5, 13F, 13G, and more to understand disclosure nuances. 3. Leverage Intelligent analytics like machine learning (ML) to extract relevant information. 4. Stay ahead with real-time access and updates.

InfoTrie SEC Filings Data helps in dissecting regulatory nuances. Whether you're assessing investment risks, conducting due diligence, or uncovering market-moving trends, our platform equips you with the resources to stay informed and navigate the intricacies of financial disclosures.

Unlock the world of regulatory insights. Request access to InfoTrie SEC Filings Data now to shape your understanding of regulatory landscapes, investment strategies, and market trends. Trust InfoTrie's established expertise to streamline your business projects with accurate, real-time regulatory and financial datasets.

More information on https://infotrie.com/sec-and-regulatory-filings/

CNBC business and financial news dataset

30% discount on precrawled datasets Visit https://crawlfeeds.com/datasets before it expires

Numbers of fields: 11

title, url, published_at, author, publisher, short_description, keywords, header_image, raw_description, description, scraped_at

Download complete dataset with 450K+ records from crawl feeds.

Other News datasets:

News datasets from crawl feeds

The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

Dataset Creation

This dataset combines financial phrasebank dataset and a financial text dataset from Kaggle. Given the financial phrasebank dataset does not have a validation split, I thought this might help to validate finance models and also capture the impact of COVID on financial earnings with the more recent Kaggle dataset.

This dataset comprises a hand collected market overview of the FinTech market in Germany as of December 2021. It includes various verified properties of 978 unique firms, which can be attributed to the financial technology sector and are operating in Germany. Each observation represents one company with 24 variables, including name, address, legal form, founders with corresponding LinkedIn accounts, register number or company-ID, attribution to FinTech segments and subsegments, bank cooperation, URL address, local court, former name, operating status. The dataset contains established companies as well as start-ups. Since the market in Germany and the nature of FinTech companies itself are dynamic as well as changing there is no complete overview of the market. Furthermore, the total number, the operating status as well as specific properties of FinTechs cannot be found in one accumulated data base. The dataset contains valuable information for researchers, practitioners as well as for supervising authorities. We provide the description of variables as well as a taxonomy for categorizing FinTechs. The nature of the dataset enables further cross-sectional and the possibility of longitudinal analyses of the complete market. The aim of the collection procedure was to find and identify all relevant FinTechs operating in Germany with a structured approach. Different databases and websites (see below) were used to obtain an overview of the market. The dataset was repeatedly updated and verified throughout the years within this process. An association to the segment of operations was conducted. Through structured Google searches the operating status was checked.

The corresponding paper with a detailed description of the variables and volume estimates can be downloaded here:
https://elibrary.duncker-humblot.com/article/72485/german-fintech-companies-a-market-overview-and-volume-estimates

All financial transactions made by Companies House as part of the Government’s commitment to transparency in expenditure

A new fraud detection dataset FDCompCN for detecting financial statement fraud of companies in China. We construct a multi-relation graph based on the supplier, customer, shareholder, and financial information disclosed in the financial statements of Chinese companies. These data are obtained from the China Stock Market and Accounting Research (CSMAR) database. We select samples between 2020 and 2023, including 5,317 publicly listed Chinese companies traded on the Shanghai, Shenzhen, and Beijing Stock Exchanges.

The sustainable finance reseach & publication dataset, which was indexed by Scopus from 1992 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.

Dataset

This dataset was created by Calvintseng001

Contents

This dataset collects data on China's A-share listed companies in 2018 and 2019.

Symbol Meaning Calculation formula Stkcd Stock code -- CSRPI corporate social responsibility participation index a weighted average of criteria and weights EVAr relative EVA the ratio of EVA to total capital MA company mature stage Dummy variable：for a mature company 1, otherwise 0. SIZE company size the natural log of total assets of a company LEV financial leverage the ratio of total debt to total assets EOC equity ownership concentration the ratio of the largest shareholder ownership（%） CH cash holding rate the ratio is the sum of cash balance and marketable securities divided by total assets R&D R&D expense rate the ratio of R&D expense to total assets AGE company age indicator the natural logarithm of company age Industry the company's industry categorical variable with two-digit codes for each industry Year years of analysis the 2018 sample is 2018, and the 2019 sample is 2019 ROE Return on Equity the ratio of net profit to equity CSRPIe corporate social responsibility participation index a simple average with equal weight for each criterion Mature5 company mature stage Dummy variable：for a mature company 1, otherwise 0.

This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5 Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora GitHub repo with performance analyses, training and data generation scripts, and inference notebooks: https://github.com/gaurangbharti1/wealth-alpaca… See the full description on the dataset page: https://huggingface.co/datasets/gbharti/finance-alpaca.

Techsalerator's Corporate Actions Dataset in Italy offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 174 companies traded on the Italian Exchange (SEDX).

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Top 5 used data fields in the Corporate Actions Dataset for Italy:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Italy:

Mergers and Acquisitions (M&A): Mergers, acquisitions, and corporate restructurings are significant in Italy, impacting various industries and contributing to market changes.

Dividend Declarations: Italian companies often declare dividends to distribute profits to shareholders. Dividend announcements can influence stock prices and investor sentiment.

Banking and Financial Sector Developments: Italy has a strong banking and financial sector. Corporate actions related to banking mergers, financial products, and regulatory changes can impact the financial industry.

Fashion and Luxury Industry Initiatives: Italy is known for its fashion and luxury brands. Corporate actions in this sector might involve product launches, collaborations, and expansions into new markets.

Renewable Energy Projects: Like many European countries, Italy is investing in renewable energy sources. Corporate actions related to solar, wind, and other sustainable energy projects are important for the country's energy transition.

Top 5 financial instruments with corporate action Data in Italy

Milan Stock Exchange (Borsa Italiana) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Milan Stock Exchange. This index would provide insights into the performance of the Italian stock market.

Milan Stock Exchange (Borsa Italiana) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Milan Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in the Italian market.

ItalGroceries: An Italy-based supermarket chain with operations in multiple regions. ItalGroceries focuses on providing high-quality products and convenience to consumers across Italy.

ItalFinance Group: A financial services provider in Italy with a focus on inclusive finance, offering banking and financial solutions to individuals and businesses across the country.

ItalSeed Co: A leading producer and distributor of certified crop seeds in various regions of Italy, contributing to the country's agriculture and food production.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Italy, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Italy?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset coverage in Italy?

Techsalerator provides comprehensive coverage of Corporate Actions Data for various companies and securities traded on the Italy Stock Exchange. The dataset encompasses major corporate actions announced by entities in the Italy market.

How does Techsalerator collect this data?

Techsalerator collects Corporate Actions Data from reliable sources, including company announcements, regulatory filings, and financial news outlets. The data is carefully curated to ensure accuracy and reliability.

Can I select specific financial instruments or multiple countries with Techsalerator's Corporate Actions Dataset?

Techsalerator offers the flexibility to select specific financial instruments or focus on corporate actions in Italy. While the dataset primarily covers Italy market, Techsalerator may provide data for other countries and international markets upon request.

How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

How do I receive the data?

Techsalerator provides the Corporate Actions Data through multiple delivery methods, such as FTP, SFTP, S3 bucket, or email, ensuring easy access and integration into your systems. The dataset is available in formats like JSON, CSV, TXT, or XLS, allowing seamless data processing.

This CD-ROM product is an authoritative reference source of 15 key financial ratios by industry groupings compiled from the North American Industry Classification System (NAICS 2007). It is based on up-to-date, reliable and comprehensive data on Canadian businesses, derived from Statistics Canada databases of financial statements for three reference years. The CD-ROM enables users to compare their enterprise's performance to that of their industry and to address issues such as profitability, efficiency and business risk. Financial Performance Indicators can also be used for inter-industry comparisons. Volume 1 covers large enterprises in both the financial and non-financial sectors, at the national level, with annual operating revenue of $25 million or more. Volume 2 covers medium-sized enterprises in the non-financial sector, at the national level, with annual operating revenue of $5 million to less than $25 million. Volume 3 covers small enterprises in the non-financial sector, at the national, provincial, territorial, Atlantic region and Prairie region levels, with annual operating revenue of $30,000 to less than $5 million. Note: FPICB has been discontinued as of 2/23/2015. Statistics Canada continues to provide information on Canadian businesses through alternative data sources. Information on specific financial ratios will continue to be available through the annual Financial and Taxation Statistics for Enterprises program: CANSIM table 180-0003 ; the Quarterly Survey of Financial Statements: CANSIM tables 187-0001 and 187-0002 ; and the Small Business Profiles, which present financial data for small businesses in Canada, available on Industry Canada's website: Financial Performance Data.