United States Avg Weekly Hours: Financial Activities data was reported at 37.500 Hour in Oct 2018. This records a decrease from the previous number of 38.200 Hour for Sep 2018. United States Avg Weekly Hours: Financial Activities data is updated monthly, averaging 37.100 Hour from Mar 2006 (Median) to Oct 2018, with 152 observations. The data reached an all-time high of 38.400 Hour in Aug 2015 and a record low of 36.100 Hour in Nov 2007. United States Avg Weekly Hours: Financial Activities data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G037: Current Employment Statistics Survey: Average Weekly Hours.

This dataset aggregates real-time sentiment scores and metadata for financial news headlines, enabling rapid detection of market-moving events and trends. It includes headline text, publication details, sentiment analysis, relevance to financial markets, and links to affected stocks and sectors. Ideal for quantitative trading, risk monitoring, and financial news analytics.

Stay informed with our comprehensive Financial News Dataset, designed for investors, analysts, and businesses to track market trends, monitor financial events, and make data-driven decisions.

Dataset Features

Financial News Articles: Access structured financial news data, including headlines, summaries, full articles, publication dates, and source details. Market & Economic Indicators: Track financial reports, stock market updates, economic forecasts, and corporate earnings announcements. Sentiment & Trend Analysis: Analyze news sentiment, categorize articles by financial topics, and monitor emerging trends in global markets. Historical & Real-Time Data: Retrieve historical financial news archives or access continuously updated feeds for real-time insights.

Customizable Subsets for Specific Needs Our Financial News Dataset is fully customizable, allowing you to filter data based on publication date, region, financial topics, sentiment, or specific news sources. Whether you need broad coverage for market research or focused data for investment analysis, we tailor the dataset to your needs.

Popular Use Cases

Investment Strategy & Risk Management: Monitor financial news to assess market risks, identify investment opportunities, and optimize trading strategies. Market & Competitive Intelligence: Track industry trends, competitor financial performance, and economic developments. AI & Machine Learning Training: Use structured financial news data to train AI models for sentiment analysis, stock prediction, and automated trading. Regulatory & Compliance Monitoring: Stay updated on financial regulations, policy changes, and corporate governance news. Economic Research & Forecasting: Analyze financial news trends to predict economic shifts and market movements.

Whether you're tracking stock market trends, analyzing financial sentiment, or training AI models, our Financial News Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Browse LSEG's Events , discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

The data represent the daily market return of Sensex, a group 30 top-performing companies in Indian financial market, as well as individual company return.

The Economic Indicator Service (EIS) aims to deliver economic content to financial institutions on both buy and sell-side and service providers. This new service currently covers 34,351 recurring macro-economic indicators from 135 countries ( as of December 16, 2019 ) such as GDP data, unemployment releases, PMI numbers etc.

Economic Indicator Service gathers the major economic events from a variety of regions and countries around the globe and provides an Economic Events Data feed and Economic Calendar service to our clients. This service includes all previous historic data on economic indicators that are currently available on the database.

Depending on availability, information regarding economic indicators, including the details of the issuing agency as well as historical data series can be made accessible for the client. Key information about EIS: • Cloud-based service for Live Calendar – delivered via HTML/JavaScript application formats, which can then be embedded onto any website using iFrames • Alternatives methods available – such as API and JSON feed for the economic calendar that can be integrated into the company’s system • Live data – updated 24/5, immediately after the data has been released • Historical data – includes a feed of all previous economic indicators available We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. The calendar includes the following. • Recurring & Non-recurring indicators covering 136 countries across 21 regions. • Indicators showing high, medium, and low impact data. • Indicators showing actual, previous, and forecast data. • Indicators can be filtered across 16 subtypes. • News generation for selected high-impact data. • Indicator description and historical data up to the latest eight historical points with a chart.

📹Project Video available on YouTube - https://youtu.be/WmJYHz_qn5s

Realistic Synthetic - AI Financial & Market Data for Gemini(Google), ChatGPT(OpenAI), Llama(Meta)

This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI). There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc. Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating. The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

These are the main Features/Columns available in the dataset :

1) Date: This column indicates the specific calendar day for which the financial and AI-related data is recorded. It allows for time-series analysis of the trends and impacts.

2) Company: This column specifies the name of the company to which the data in that particular row belongs. Examples include "OpenAI" and "Meta".

3) R&D_Spending_USD_Mn: This column represents the Research and Development (R&D) spending of the company, measured in Millions of USD. It serves as an indicator of a company's investment in innovation and future growth, particularly in the AI sector.

4) AI_Revenue_USD_Mn: This column denotes the revenue generated specifically from AI-related products or services, also measured in Millions of USD. This metric highlights the direct financial success derived from AI initiatives.

5) AI_Revenue_Growth_%: This column shows the percentage growth of AI-related revenue for the company on a daily basis. It indicates the pace at which a company's AI business is expanding or contracting.

6) Event: This column captures any significant events or announcements made by the company that could potentially influence its financial performance or market perception. Examples include "Cloud AI launch," "AI partnership deal," "AI ethics policy update," and "AI speech recognition release." These events are crucial for understanding sudden shifts in stock impact.

7) Stock_Impact_%: This column quantifies the percentage change in the company's stock price on a given day, likely in response to the recorded financial metrics or events. It serves as a direct measure of market reaction.

The ChFinAnn dataset is a publicly available financial event dataset that includes five types of financial events: Equity Freeze, Equity Pledge, Equity Repurchase, Equity Overweight, and Equity Underweight. This dataset consists of 32040 samples and 48000 event records, of which approximately 29% of the documents contain more than one record. In addition, 98% of the records have arguments scattered across different sentences, which poses additional challenges for event extraction. This dataset was built to support document level event extraction asks, particularly in the financial field. It is not only large in scale, but also has the characteristics of multi event recording and argument dispersion, which make it a valuable resource for researching document level event extraction.

This dataset provides a listing of Structure Change Events.

Techsalerator's News Events Data for Georgia: A Comprehensive Overview

Techsalerator's News Events Data for Georgia offers a valuable resource for businesses, researchers, and media organizations. This dataset compiles information on significant news events across Georgia, drawing from a wide range of media sources, including news outlets, online publications, and social platforms. It provides essential insights for those tracking trends, analyzing public sentiment, or monitoring developments in specific industries.

Key Data Fields
Event Date: Records the exact date of the news event, crucial for analysts monitoring trends over time or businesses responding to changes in the market.

Event Title: A brief headline that describes the event, allowing users to quickly categorize and assess the content based on relevance to their interests.

Source: Identifies the news outlet or platform where the event was reported, helping users track credible sources and evaluate the reach and influence of the event.

Location: Provides geographic information, indicating where the event took place within Georgia. This is particularly valuable for regional analysis or localized marketing efforts.

Event Description: A detailed summary outlining key developments, participants, and potential impact. Businesses and researchers use this to understand the context and implications of the event.

Top 5 News Categories in Georgia
Politics: News coverage on government decisions, political movements, elections, and policy changes shaping the national landscape.

Economy: Focuses on Georgia's economic indicators, trade, corporate activities, and financial developments that influence the business and finance sectors.

Social Issues: News covering societal concerns such as public health, education, and protests that drive public discourse.

Sports: Highlights major sports events, particularly in rugby, football, and wrestling, which are widely followed across the country.

Technology and Innovation: Reports on tech advancements, startups, and innovations in Georgia's growing tech ecosystem, including contributions from local and international companies.

Top 5 News Sources in Georgia
Agenda.ge: A leading news portal offering updates on politics, economy, and social issues.

Interpressnews: A prominent online news platform providing real-time updates on breaking news and major developments across Georgia.

Georgia Today: An English-language publication focused on Georgia’s political, economic, and business landscape.

Civil.ge: A well-respected source for in-depth analysis of political and social issues in Georgia.

Radio Tavisupleba: A major news network that broadcasts updates on current affairs, culture, and live events throughout the country.

Accessing Techsalerator’s News Events Data for Georgia
To access Techsalerator’s News Events Data for Georgia, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields
Event Date
Event Title
Source
Location
Event Description
Event Category (Politics, Economy, Sports, etc.)
Participants (if applicable)
Event Impact (Social, Economic, etc.)

Techsalerator’s dataset is a vital tool for tracking significant events in Georgia. It supports informed decision-making for business strategies, market analysis, or academic research by providing a clear picture of the country's news landscape.

DocFEE: A Document-Level Chinese Financial Event Extraction DatasetDocFEE is a large-scale, document-level dataset designed for financial event extraction in Chinese. It contains ​19,044 annotated documents spanning nine critical financial event types, including Bankruptcy Liquidation, Major Safety Incident, Equity Pledge, and Senior Executive Death, among others. Each document is annotated with ​38 distinct argument types to capture intricate event details, such as dates, amounts, stakeholders, and contextual impacts.The dataset addresses the challenges of document-level event extraction, where events and their arguments may span multiple sentences or exhibit cross-sentence dependencies. On average, documents contain ​1.86 events with a broad event range (960.06 characters) and a median document length of 2,277.25 characters, reflecting real-world complexity. Key features include fine-grained annotations for diverse financial scenarios, such as tracking shareholder reductions (Reduction Start Date, Shareholder, Reduction Amount) or quantifying losses in Major Asset Loss events (Loss Amount, Other Losses).DocFEE supports research in financial NLP, regulatory compliance, and risk analysis by providing robust, structured data for modeling cross-sentence event relations and argument extraction in long-form texts. Its comprehensive annotations and domain specificity make it a valuable resource for advancing document-level understanding in Chinese financial contexts.Please refer to README.pdf for detailed description.

Event Studies are overwhelmingly widespread in financial research, providing tools for shedding light on market efficiency, as well as measuring the impact of various occurrences on public firms’ security prices. Mastering the Event Study approach is essential for researchers and practitioners alike.

Event Studies for Financial Research aims to help readers obtain valuable hands-on experience with Event Study tools and gain technical skills for conducting their own studies. Kliger and Gurevich provide a detailed application of their approach, which consists of a description of the method, references, guided applications, and elaborated framework for implementing the applications.

The data provided here as part of the DEBS 2022 Grand Challenge is based on real tick data captured by Infront Financial Technology GmbH for the complete week of November 8th to 14th, 2021 (i.e., five trading days Monday to Friday + Saturday and Sunday). The data set contains 289 million tick data events covering 5504 equities and indices that are traded on three European exchanges: Paris (FR), Amsterdam (NL), and Frankfurt (ETR).

Some event notifications appear to come with no payload. This is due to the fact that the 2022 GC requires only a small subset of attributes to be evaluated; other attributes have been eliminated from the data set to minimize its overall size while keeping the amount of events to process unchanged.

Further details on the data set, its syntax and its semantics can be found in the official DEBS 2022 Grand Challenge paper as part of the DEBS 2022 conference proceedings (please use this for citation):

Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In The 16th ACM International Conference on Distributed and Event-based Systems (DEBS ’22), June 27-June 30, 2022, Copenhagen. ACM, New York, NY, USA.

All files of the DEBS 2022 Grand Challenge Data Set “Trading Data” are provided as-is. By downloading and using this data you agree to the terms and conditions of the licensing agreement (CC by-nc-sa).

Climate change poses a major threat to long-term growth and prosperity and has a direct impact on the economic well-being of all countries. Extreme events cost US$143 billion per year due to climate change. The majority (63%) of this number is due to the loss of human life. Losses resulting from no action on climate change to the world economy could reach US$178 trillion in 2070. Benefits from accelerating the transition to net zero are US$43 trillion in the next 50 years, so climate-related financial risk management must be carried out as optimally as possible in industrial groups in the financial sector and non-financial groups. The World Economic Forum reports that climate action failure will dominate the next decade. To achieve financial stability, a strategy is needed through four main aspects: governance, strategy, risk management, and metrics and targets. The scenario that must be targeted is an orderly scenario to achieve global climate mitigation and adaptation targets. The transition to the new climate economy must be carried out by measuring predetermined indicators, as is done by the IMF. Mitigation indicators include environmental taxes, environmental protection spending, renewable energy, low-carbon technology trade, and forests and carbon. Adaptation indicators include carbon taxes, climate finance, the primary energy mix, fossil fuel prices, and the final energy mix.

Graph and download economic data for Government current expenditures: Economic affairs: Space (G160241A027NBEA) from 1959 to 2023 about outer space, economic affairs, expenditures, government, GDP, and USA.

The MAS 12-Week Bill Auction is a regular event where the Monetary Authority of Singapore (MAS) issues short-term government securities to manage liquidity in the financial system.

Climate Risk Dataplace for Financial Services Market Outlook

According to our latest research, the global Climate Risk Dataplace for Financial Services market size reached USD 2.4 billion in 2024, driven by the increasing integration of climate risk analytics into financial decision-making. The market is witnessing a robust growth trajectory, with a recorded CAGR of 19.7% from 2025 to 2033. By 2033, the market is forecasted to achieve a value of USD 11.4 billion, reflecting the growing emphasis on sustainable finance and mandatory climate risk disclosures across key financial jurisdictions. The primary growth factor propelling this market is the convergence of regulatory mandates and investor demand for climate-resilient portfolios, compelling financial institutions to adopt advanced climate risk data platforms and analytical tools.

The surge in demand for climate risk dataplace solutions within financial services is fundamentally driven by the global shift toward sustainability and responsible investing. As climate change poses escalating risks to asset valuations, creditworthiness, and overall portfolio stability, financial institutions are under mounting pressure to comprehensively assess and manage these risks. The integration of sophisticated climate risk data platforms enables banks, asset managers, and insurers to quantify exposure to physical and transition risks, such as extreme weather events, regulatory changes, and shifting market preferences. This capability is essential not only for risk mitigation but also for capitalizing on emerging opportunities in green finance and sustainable investment products. The positive momentum in this market is further reinforced by the proliferation of climate scenario modeling and stress testing, which are becoming standard practices for forward-looking risk management in the financial sector.

Another key growth driver is the evolving regulatory landscape, particularly in developed economies such as North America and Europe. Regulatory bodies including the European Central Bank (ECB), the Bank of England, and the U.S. Securities and Exchange Commission (SEC) have introduced or proposed stringent climate risk disclosure requirements, compelling financial institutions to enhance their climate risk assessment capabilities. The Task Force on Climate-related Financial Disclosures (TCFD) and the International Sustainability Standards Board (ISSB) frameworks are increasingly being adopted as benchmarks, necessitating the deployment of robust data platforms that can aggregate, analyze, and report climate-related financial information. This regulatory push is catalyzing investments in both proprietary and third-party climate risk data solutions, fostering a dynamic ecosystem of technology providers, data aggregators, and consulting firms that are shaping the future of climate risk management in financial services.

The rapid advancement of data analytics, artificial intelligence, and cloud computing technologies also plays a pivotal role in shaping the climate risk dataplace market. Financial institutions are leveraging these innovations to enhance the granularity, accuracy, and timeliness of climate risk assessments. AI-powered analytics enable the processing of vast and diverse datasets, including satellite imagery, weather forecasts, and socio-economic indicators, to generate actionable insights for portfolio managers and risk officers. Cloud-based deployment models further facilitate scalability, interoperability, and real-time collaboration across geographically dispersed teams and stakeholders. As a result, financial organizations are increasingly able to integrate climate risk considerations into core business processes, such as lending, underwriting, and investment decision-making, thereby driving sustained market growth.

Climate Risk Analytics for Financial Institutions is becoming a cornerstone in the strategic planning and risk management processes of banks and investment firms. As the financial sector grapples with the multifaceted impacts of climate change, the ability to analyze and interpret climate-related data is crucial. These analytics provide insights into potential future scenarios, helping institutions to anticipate and mitigate risks associated with extreme weather events, regulatory changes, and shifts in consumer preferences. By leveraging advanced analytics, financial institutions can

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

Sweden Central Govt Budget: Expenditure: EA: Financial Security Illness and Disability Event data was reported at 101,868.165 SEK mn in 2017. This records a decrease from the previous number of 105,613.633 SEK mn for 2016. Sweden Central Govt Budget: Expenditure: EA: Financial Security Illness and Disability Event data is updated yearly, averaging 101,868.165 SEK mn from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 127,049.423 SEK mn in 2005 and a record low of 31,535.312 SEK mn in 1995. Sweden Central Govt Budget: Expenditure: EA: Financial Security Illness and Disability Event data remains active status in CEIC and is reported by The Swedish National Financial Management Authority. The data is categorized under Global Database’s Sweden – Table SE.F005: Central Government Budget.

Techsalerator’s News Event Data in Latin America offers a detailed and extensive dataset designed to provide businesses, analysts, journalists, and researchers with an in-depth view of significant news events across the Latin American region. This dataset captures and categorizes key events reported from a wide array of news sources, including press releases, industry news sites, blogs, and PR platforms, offering valuable insights into regional developments, economic changes, political shifts, and cultural events.

Key Features of the Dataset: Comprehensive Coverage:

The dataset aggregates news events from numerous sources such as company press releases, industry news outlets, blogs, PR sites, and traditional news media. This broad coverage ensures a wide range of information from multiple reporting channels. Categorization of Events:

News events are categorized into various types including business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly locate and analyze information relevant to their interests or sectors. Real-Time Updates:

The dataset is updated regularly to include the most recent events, ensuring users have access to the latest news and can stay informed about current developments. Geographic Segmentation:

Events are tagged with their respective countries and regions within Latin America. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps in understanding the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and perform comparative analysis over time. This feature supports longitudinal studies and provides insights into how news events evolve. Advanced Search and Filter Options:

Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. Latin American Countries Covered: South America: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama Caribbean: Cuba Dominican Republic Haiti (Note: Primarily French-speaking but included due to geographic and cultural ties) Jamaica Trinidad and Tobago Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across Latin America, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to Latin American news and events. Techsalerator’s News Event Data in Latin America is a crucial resource for accessing and analyzing significant news events across the region. By providing detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.