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Yahoo! Finance is a media property that is part of the Yahoo! network. It provides financial news, data and commentary including stock quotes, press releases, financial reports, and original content. It also offers some online tools for personal finance management. In addition to posting partner content from other web sites, it posts original stories by its team of staff journalists. It is ranked 20th by Similar Web on the list of largest news and media websites.

Description: This dataset contains financial information for companies listed on major stock exchanges around the world, as provided by Yahoo Finance. The data covers a range of industries and includes key financial metrics such as price, volume, market capitalization, P/E ratio, and more.

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The data is sourced from Yahoo Finance and is updated daily, providing users with the most up-to-date financial information for each company listed.

The dataset is suitable for anyone interested in analyzing or predicting stock market trends and is particularly useful for financial analysts, investors, and traders.

Dataset Card for Financial Fraud Labeled Dataset

  Dataset Details

This dataset collects financial filings from various companies submitted to the U.S. Securities and Exchange Commission (SEC). The dataset consists of 85 companies involved in fraudulent cases and an equal number of companies not involved in fraudulent activities. The Fillings column includes information such as the company's MD&A, and financial statement over the years the company stated on the SEC… See the full description on the dataset page: https://huggingface.co/datasets/amitkedia/Financial-Fraud-Dataset.

This dataset is about stocks. It has 2 rows and is filtered where the company is Lincoln Financial. It features 5 columns: company, exchange, exchange symbol, and currency.

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.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Techsalerator's Corporate Actions Dataset in South Korea 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 2445 companies traded on the Korea Stock Exchange (XKRX).

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

• 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 South Korea:

Mergers and Acquisitions (M&A): South Korea's business landscape has seen various corporate actions related to mergers, acquisitions, and corporate restructuring, contributing to industry consolidation and market dynamics.

Technological Innovation: Corporate actions involving investments in technology, research and development, and innovation have been prominent in South Korea's efforts to maintain its position as a global technology leader.

Global Expansion: South Korean companies have undertaken corporate actions to expand their global footprint, including entering new markets, forming strategic partnerships, and exploring joint ventures.

Renewable Energy Initiatives: Corporate actions related to the renewable energy sector, including investments in solar, wind, and other green technologies, align with South Korea's push for sustainable development.

Financial Sector Developments: Corporate actions involving financial institutions, fintech advancements, and regulatory changes contribute to the modernization and competitiveness of South Korea's financial industry.

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Top 5 financial instruments with corporate action Data in South Korea

Seoul Stock Exchange (SSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Seoul Stock Exchange. This index would provide insights into the performance of the South Korean stock market.

Seoul Stock Exchange (SSE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Seoul Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in South Korea.

KorMart: A South Korea-based online marketplace with operations in multiple regions. KorMart focuses on connecting buyers and sellers and contributing to the growth of e-commerce in South Korea.

FinanceKorea: A financial services provider in South Korea with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

TechInnovate Korea: A company dedicated to advancing technological innovation in South Korea, focusing on research and development, and fostering a culture of innovation to support the country's technology sector.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for South Korea, 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 South Korea?

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 South Korea?

Techsalerator provides comprehensive coverage of Corporate Act...

All Forms 10-K analysis filled in the years 1995 to 2008. The original filing text files were mapped with “edgar” R package version 1.0.8. The filing text files were coded with the CIK number and date of Forms 10-K’s filing. Both a positive and negative dictionary were applied as suggested by Loughran and McDonald Financial Sentiment Dictionaries and as implemented in R edgar package We imposed a 10% frequency limit for both dictionaries .

The European Business Performance database describes the performance of the largest enterprises in the twentieth century. It covers eight countries that together consistently account for above 80 per cent of western European GDP: Great Britain, Germany, France, Belgium, Italy, Spain, Sweden, and Finland. Data have been collected for five benchmark years, namely on the eve of WWI (1913), before the Great Depression (1927), at the extremes of the golden age (1954 and 1972), and in 2000.The database is comprised of two distinct datasets. The Small Sample (625 firms) includes the largest enterprises in each country across all industries (economy-wide). To avoid over-representation of certain countries and sectors, countries contribute a number of firms that is roughly proportionate to the size of the economy: 30 firms from Great Britain, 25 from Germany, 20 from France, 15 from Italy, 10 from Belgium, Spain, and Sweden, and 5 from Finland. By the same token, a cap has been set on the number of financial firms entering the sample, so that they range between up to 6 for Britain and 1 for Finland.The second dataset, or Large Sample (1,167 firms), is made up of the largest firms per industry. Here industries are so selected as to take into account long-term technological developments and the rise of entirely new products and services. Firms have been individually classified using the two-digit ISIC Rev. 3.1 codes, then grouped under a manageable number of industries. To some extent and broadly speaking, the two samples have a rather distinct focus: the Small Sample is biased in favour of sheer bigness, whereas the Large Sample emphasizes industries.As far as size and performance indicators are concerned, total assets has been picked as the main size measure in the first three benchmarks, turnover in 1972 and 2000 (financial intermediaries, though, are ranked by total assets throughout the database). Performance is gauged by means of two financial ratios, namely return on equity and shareholders’ return, i.e. the percentage year-on-year change in share price based on year-end values. In order to smooth out volatility, at each benchmark performance figures have been averaged over three consecutive years (for instance, performance in 1913 reflects average performance in 1911, 1912, and 1913).All figures were collected in national currency and converted to US dollars at current year-average exchange rates.

List of Financial Services Companies Granted a License to Deal at Foreign Stock Exchanges

Dataset

This dataset was created by Emmanuel ndahimana

Released under Apache 2.0

Contents

This dataset is about stocks. It has 1 row and is filtered where the company is Intercorp Financial Services. It features 8 columns including stock name, company, exchange, and exchange symbol.

This dictionary gathers different disciplines and topics such as: finance, economy, trade, business, stock-exchange, banking, firms, negotiation, mailing, telephone conversation, values, etc. It also includes many phrases relevant for business, impersonal expressions, conjugated sentences, relevant sentences, standard sentences, synonyms, abbreviations. The DISCIPLINE field gives a subdivision into sectors : stock exchange, trade, export, business, values, economy, banking, etc. Single words are associated with the meaning or event which they apply to.Languages : French - English (GB, US), English (GB, US) - FrenchNumber of entries: 91,300. Number of terms per language: about -10% with respect to the number of entries (i.e. ca. 82,000 terms)Disciplines: about 105Format: .DBF files, sorted alphabetically in French and EnglishA viewer is also available upon demand. This software enables a spontaneous search French => English and English => French in the database according to different criteria:- by beginning of term, - by included word,- by discipline,- by abbreviation.Terms, phrases and conjugated sentences are sorted alphabetically.Examples : phrases beginning with "à" : à terme, à titre gracieux, à titre onéreux, à vue...; "en" : en compte, en vigueur..., "prix" : prix abordable, prix choc, prix exorbitant...Viewing format: .FIC (Windev)Please note that the prices indicated here are dependent from the number of entries available which is growing constantly. Please contact us for further details.

This dataset is about stocks, has 1 rows and is filtered where the company is Sequoia Financial Group. It features 8 columns including stock, stock name, company, exchange, and exchange symbol. The preview is ordered by stock (ascending).

This dataset is about stocks. It has 2 rows and is filtered where the company is Axos Financial. It features 8 columns including stock name, company, exchange, and exchange symbol.

Yahoo Finance Business Information dataset to access comprehensive details on companies, including financial data and business profiles. Popular use cases include market analysis, investment research, and competitive benchmarking.

Use our Yahoo Finance Business Information dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape.

Dataset Features

• name: Represents the company name.
• company_id: Unique identifier assigned to each company.
• entity_type: Denotes the type/category of the business entity.
• summary: A brief description or summary of the company.
• stock_ticker: The ticker symbol used for trading on stock exchanges.
• currency: The currency in which financial values are expressed.
• earnings_date: The date for the reported earnings.
• exchange: The stock exchange on which the company is listed.
• closing_price: The final stock price at the end of the trading day.
• previous_close: The stock price at the close of the previous trading day.
• open: The price at which the stock opened for the trading day.
• bid: The current highest price that a buyer is willing to pay for the stock.
• ask: The current lowest price that a seller is willing to accept.
• day_range: The range between the lowest and highest prices during the trading day.
• week_range: A broader price range over the past week.
• volume: Number of shares that traded in the session.
• avg_volume: Average daily share volume over a specific period.
• market_cap: Total market capitalization of the company.
• beta: A measure of the stock's volatility in comparison to the market.
• pe_ratio: Price-to-earnings ratio for valuation.
• eps: Earnings per share.
• dividend_yield: Dividend yield percentage.
• ex_dividend_date: The date on which the stock trades without the right to the declared dividend.
• target_est: The analyst's target price estimate.
• url: The URL to more detailed company information.
• people_also_watch: Companies frequently watched alongside this company.
• similar: Other companies with similar profiles.
• risk_score: A quantified risk score.
• risk_score_text: A textual interpretation of the risk score.
• risk_score_percentile: The risk score expressed in percentile terms.
• recommendation_rating: Analyst recommendation ratings.
• analyst_price_target: Analyst provided stock price target.
• company_profile_address: Company address from the profile.
• company_profile_website: URL for the company’s website.
• company_profile_phone: Contact phone number.
• company_profile_sector: The sector in which the company operates.
• company_profile_industry: Industry classification of the company.
• company_profile_employees: Number of employees in the company.
• company_profile_description: A detailed profile description of the company.
• valuation_measures: Contains key valuation ratios and metrics such as enterprise value, price-to-book, and price-to-sales ratios.
• Financial_highlights: Offers summary financial statistics including EPS, profit margin, revenue, and cash flow indicators.
• financials: This column appears to provide financial statement data.
• financials_quarterly: Similar to the previous field but intended to capture quarterly financial figures.
• earnings_estimate: Contains consensus earnings estimates including average, high, and low estimates along with the number of analysts involved.
• revenue_estimate: Provides revenue estimates with details such as average estimate, high and low values, and sales growth factors.
• earnings_history: This field tracks historical earnings and surprises by comparing actual EPS with estimates.
• eps_trend: Contains information on how the EPS has trended over various recent time intervals.
• eps_revisions: Captures recent changes in EPS forecasts.
• growth_estimates: Offers projections related to growth prospects over different time horizons.
• top_analysts: Intended to list the top analysts covering the company.
• upgrades_and_downgrades: This field shows recent analyst upgrades or downgrades.
• recent_news: Meant to contain recent news articles related to the company.
• fanacials_currency: Appears to indicate the currency used for financial reporting or valuation in the dataset.
• **company_profile_he

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.

This dataset is about stocks, has 1 rows. and is filtered where the company is Financial Street Property. It features 8 columns including stock, stock name, company, exchange, and exchange symbol. The preview is ordered by stock (ascending).

This dataset is about stocks. It has 1 row and is filtered where the company is Bread Financial. It features 8 columns including stock name, company, exchange, and exchange symbol.

This dataset is about stocks, has 1 rows. and is filtered where the company is Wall Financial. It features 8 columns including stock, stock name, company, exchange, and exchange symbol. The preview is ordered by stock (ascending).

This dataset is for a sample of 50 non-financial firms listed on the Johannesburg Stock Exchange.