📝 Description

This dataset contains over 400,000 macroeconomic events collected from global sources across more than 90 countries and regions, covering years 2020–2025. It mirrors professional economic calendars used by traders, economists, and analysts to track key economic indicators that move financial markets.

Each event includes its scheduled release time, geographical zone, currency, importance level, and actual, forecast, and previous values when available.

You can use this dataset for:

• 📈 Economic forecasting and sentiment analysis
• 💹 Financial market event-driven modeling
• 🧠 Machine learning applications on macroeconomic signals
• 🌍 Comparative studies of country-level indicators

📊 Dataset Structure

🌐 Coverage

• Zones: 90+ economies — including United States, Euro Zone, China, Japan, United Kingdom, India, Brazil, Australia, Türkiye, South Africa, and more.
• Currencies: USD, EUR, JPY, GBP, CNY, INR, and 50+ others.
• Time Period: ~2000 to 2025 (depending on data availability).
• Events Count: 409,234 records.

⚙️ Data Quality Notes

• Missing values in currency, importance, or actual columns occur mainly for minor or regional events.
• Times are reported as given in the source; some may represent local times.
• The dataset is cleaned and standardized for country and currency fields but can be further enriched with ISO country codes or UTC timestamps.

🧠 Example Use Cases

1. Market Volatility Forecasting: Use event importance and actual-vs-forecast deviations to predict short-term asset volatility.
2. Macroeconomic Trends: Track inflation or employment releases over time by country or region.
3. Event Sentiment Modeling: NLP on the event column for topic clustering (e.g., inflation vs. housing).
4. Calendar Effect Studies: Combine with asset price data (SPX, EURUSD, etc.) to measure event-driven reactions.

📦 File Info

• economic_calendar.csv

  • 409,234 rows
  • 10 columns
  • Size: ~31 MB

🏁 Tags

economics, macroeconomics, finance, forex, stock-market, forecasting, time-series, machine-learning, econometrics

🔗 Suggested License

If it’s scraped or aggregated from public calendars (like Investing.com), use: CC BY-NC-SA 4.0 — Attribution-NonCommercial-ShareAlike.

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

content

This dataset records world economic events on a calendar basis. Date, time, country/zone, currency, event name, importance level, and actual/predicted/previous economic values (if available) are among the details that are included in each row, which represents a single event. Columns such as id, date, time, zone, currency, importance, event, actual, forecast, and prior are included in the dataset. These areas aid in monitoring market-moving announcements, national public events and holidays, and economic indicators.

context

Financial analysis, forecasting, and comprehending the impact of world events on markets and currencies may all be done with this dataset. These economic calendars are used by traders, economists, and data analysts to examine how significant announcements (such as interest rates, inflation figures, and holidays) affect market activity. Time-series forecasting models, market reaction studies, and EDA initiatives that investigate the connections between financial patterns and economic events can all benefit from its support.

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.

Africa Economic Calendar Dataset

  Dataset Description

This dataset contains 111,450 macroeconomic events from 18 African countries, spanning from 2020 to 2025. The dataset mirrors professional economic calendars used by traders, economists, and analysts to track key economic indicators that move financial markets. Each event includes its scheduled release time, geographical zone, currency, importance level, and actual, forecast, and previous values when available.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-economic-calendar-2020-2025.

Total-Cashflows-From-Financing-Activities Time Series for Stifel Financial Corporation. Stifel Financial Corp. operates as the bank holding company for Stifel, Nicolaus & Company, Incorporated that provides retail and institutional wealth management, and investment banking services to individual investors, corporations, municipalities, and institutions in the United States and internationally. It operates in three segments: Global Wealth Management, Institutional Group, and Other. The company provides private client services, including securities transaction and financial planning services; securities brokerage services, such as the sale of equities, mutual funds, fixed income products, and insurance; institutional equity and fixed income sales, trading and research, and municipal finance services; investment banking services, such as mergers and acquisitions, public offerings, and private placements; and retail and commercial banking services comprising personal and commercial lending programs, as well as deposit accounts. It participates in and manages underwritings for corporate and public finance; and offers financial advisory and securities brokerage services. The company was founded in 1890 and is headquartered in Saint Louis, Missouri.

CoFiF is the first corpus comprising company reports in the French language. It contains over 188 million tokens in 2655 reports, covering four types of documents: Reference documents (documents de référence) published annually, usually in the months following the end of the calendar year, and contain information regarding the financial situation and perspectives of a company; Annual report (résultats annuels) which summarises a company’s business and activities throughout the previous year. Semestrial (résultats semestriels): similar to annual reports in content but published every 6 months; Trimestrial reports (résultats trimestriels): similar to annual reports but published every 3 months; These documents are collected from the 60 largest French companies listed in France’s main stock indices CAC40 and CAC Next 20. The corpus spans over 20 years, ranging from 1995 to 2018.

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

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

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.

Total-Cashflows-From-Investing-Activities Time Series for Flagstar Financial, Inc.. Flagstar Bank, National Association operates as the bank holding company for Flagstar Bank, N.A. that provides banking products and services in the United States. The company's deposit products include interest-bearing checking and money market, savings, and non-interest-bearing accounts, as well as certificates of deposit. Its loan products comprise multi-family loans; commercial real estate loans; acquisition, development, and construction loans; commercial and industrial loans; one-to-four family loans; specialty finance loans and leases; warehouse loans; and other loans, such as home equity lines of credit, as well as other consumer loans, including overdraft loans. The company offers non-deposit investment and insurance products; and online and mobile banking services. It primarily serves individuals, small and mid-size businesses, and professional associations. The company was formerly known as Flagstar Financial, Inc. Flagstar Bank, National Association was founded in 1859 and is headquartered in Hicksville, New York.

The CSV files contain S&P historical data and are meant to be used in the Hull Tactical - Market Prediction competition.

With this dataset, you can - align the competition's date_id with real dates and analyze the seasonality of the data (weekly, yearly) - analyze the influence of holidays - analyze how the forward returns of the competition have been preprocessed - analyze the correlation with external events - and so on...

There are two files: - sp-historical.csv: S&P index. The 8990 rows of this CSV file correspond to the 8990 rows of the training dataset of the Hull Tactical - Market Prediction competition. - spy-historical.csv: SPY ETF. The forward returns in this file match train.csv better, but the file starts only in February of 1993 so that its rows correspond to the last 8210 rows of the training dataset.

The forward returns in spy-historical.csv were calculated as spy['forward_returns'] = (spy['Close'].shift(-1) + spy['Dividend'].shift(-1)) / spy['Close'] - 1

Total-Cash-From-Operating-Activities Time Series for Dgb Financial. iM Financial Group Co., Ltd., through its subsidiaries, engages in the provision of various banking products and services in South Korea. The company offers deposit products; loans; foreign exchange; financial products, including fund and bancassurance; asset management; and internet banking services. It also provides investment trading and brokerage, discretionary investment, investment advisory, and trust services; sells insurance products, including coverage, endowment, pension, and variable; and insurance contract loans, etc. In addition, the company is involved in the collective investment, investment discretionary, and investment advisory businesses; issuing and managing prepaid electronic payment services and payment gateway; and facility and automobile rental, instalment and automobile finance, corporate and personal loans, and technology business finance services. Further, it engages in the development, operation, maintenance, and integration of information systems; sale, rental, and maintenance of information equipment and software; IT consulting; IT related education; e-commerce; and internet related businesses. Additionally, the company provides claim collection, credit investigation, lease inspection, and civil affairs agency services; venture investment and support; and offers fintech, digital asset management, software development, and education solutions, as well as global merger and acquisition, and investment services; and foreign exchange, electronic finance, and other ancillary/related businesses. The company was formerly known as DGB Financial Group Co., Ltd. and changed its name to iM Financial Group Co., Ltd. in March 2025. iM Financial Group Co., Ltd. was founded in 1967 and is based in Daegu, South Korea.

Each year, County departments and agencies report performance data on core activities for public viewing on the County’s website. This dataset contains these reports for all past years starting in 2018. recordKey: A unique identifier consisting of, respectively, a code for the department and the numbers of the goal, objective, and measureGoal: Encompasses one or more objectivesObjective: A subdivision of a goal, encompasses one or more measuresTimeframe: Either Calendar Year or Fiscal Year. For example, the 2023 fiscal year began on July 1, 2022, and ended on June 30, 2023.Measure: The specific result being measuredMeasure Type: Resource (Input); Workload, Demand, Production (Output); Efficiency; Quality; or Impact (Outcome)Units: Number; Percentage; Average; or DollarsYear (for example ‘2018): The amount reported by the department for the listed measure in this fiscal or calendar year

Explore the intricate dance between gold prices and key economic events across major global players – Canada, Japan, USA, Russia, European Union, and China. This comprehensive dataset spans from January 2019 to December 2023, offering a nuanced analysis of how economic news from these influential regions impacts the ever-volatile gold market. Delve into the ebb and flow of financial landscapes, uncovering trends, correlations, and invaluable insights for strategic decision-making in the dynamic world of investments.

Historical Gold Price Dataset:

• Day: The day of the week when the data was recorded.
• Date: The specific date corresponding to the recorded gold price.
• Hour: The time of day when the gold price was recorded.
• Country: The country associated with the economic event or news affecting gold prices.
• Event: The economic event or news that potentially influenced gold prices.
• Actual: The actual reported value or figure related to the economic event.
• Previous: The previously reported value or figure for the same economic event.
• Consensus: The consensus forecast or expected value for the economic event.
• Forecast: The forecasted value or figure for the economic event.

** Economic Calendar Dataset**:

• Day: The day of the week when the economic event is scheduled.
• Date: The specific date when the economic event is expected to occur.
• Hour: The time of day when the economic event is scheduled.
• Country: The country associated with the economic event.
• Event: The specific economic event or news scheduled to take place.
• Actual The actual reported value or figure related to the economic event.
• Previous: The previously reported value or figure for the same economic event.
• Consensus: The consensus forecast or expected value for the economic event.
• Forecast: The forecasted value or figure for the economic event.

Total-Cash-From-Operating-Activities Time Series for First Financial Corporation Indiana. First Financial Corporation, through its subsidiaries, provides various financial products and services in west-central Indiana, east-central Illinois, western Kentucky, central and eastern Tennessee, and northern Georgia. It offers non-interest-bearing demand, interest-bearing demand, savings, time, and other time deposits. The company also provides commercial loans primarily to expand a business or finance asset purchases; residential real estate and residential real estate construction loans; and home equity loans and lines, secured loans, and cash/CD secured and unsecured loans. In addition, it offers lease financing, trust account, depositor, investment, and insurance services. The company was founded in 1834 and is headquartered in Terre Haute, Indiana.

📦 Saudi Arabia Events & Crowding Impact Dataset

Unlock insights into crowding, sales trends, and delivery optimization using public events, weather, and paydays.

📝 Dataset Overview

This dataset captures public events, holidays, weather conditions, and financial factors that influence crowding, consumer behavior, and online deliveries across Saudi Arabia.

Key Highlights:
✅ Covers multiple Saudi cities with rich event data.
✅ Includes weather conditions affecting business & logistics.
✅ Tracks paydays & school schedules for demand forecasting.
✅ Ideal for crowding prediction, sales analysis, and delivery optimization.

📊 Data Description

Each row represents a daily snapshot of city conditions with the following variables:

📆 Date & Calendar Information

• DateG – Gregorian date (YYYY-MM-DD).
• DateH – Hijri date.
• Day – Day of the week (Sunday, Monday, etc.).

🎉 Public Holidays & Events

• Holiday Name – Name of the holiday (if applicable).
• Type of Public Holiday – National, Religious, or School-related holidays.
• Event – Major events (e.g., festivals, matches, etc.).
• Match – Includes Premier League & KSA League games.

🌦 Weather Conditions

• Cloudy, Fog, Rain, Widespread Dust, Blowing Dust, etc.
• Useful for studying weather impact on mobility & sales.

🏙 Crowding & City Impact

• City – Name of the city.
• Effect on City – Expected impact (e.g., increased traffic, reduced mobility).

💰 Economic & Financial Impact

• Pay Day – Indicates whether it was a salary payout day.
• days till next payday – How many days until the next salary payout.
• days after payday – How many days after the last payday.

🎓 Education & School Impact

• days after school – Number of days since school ended.
• days before school – Number of days until school resumes.

🚀 Potential Use Cases

This dataset can be leveraged for:

📌 Crowding Prediction – Identify peak congestion periods based on holidays, weather, and events.
📌 Sales & Demand Forecasting – Analyze payday effects on consumer spending & delivery volumes.
📌 Delivery Optimization – Find the best times for online deliveries to avoid congestion.
📌 Weather Impact Analysis – Study how dust storms & rain affect mobility & e-commerce.
📌 Event-driven Business Planning – Plan logistics around national events & sports matches.

📈 Exploratory Data Analysis (EDA)

🔍 Ideas for Data Exploration

• Visualize order volume trends across paydays, school terms, & holidays.
• Analyze correlations between weather conditions & delivery delays.
• Find seasonal trends in crowding & online shopping behavior.

🔥 Example Analysis in Python

import pandas as pd
import matplotlib.pyplot as plt

# Load the dataset
df = pd.read_csv("saudi_events.csv")

# Convert date column to datetime format
df['DateG'] = pd.to_datetime(df['DateG'])

# Plot orders over time
plt.figure(figsize=(10,5))
df.groupby('DateG')['days after payday'].mean().plot()
plt.title("Effect of Payday on Consumer Activity")
plt.xlabel("Date")
plt.ylabel("Days After Payday")
plt.show()

📌 Getting Started

How to Use the Dataset:

1️⃣ Download the dataset and load it into Python or R.
2️⃣ Perform EDA to uncover insights into crowding & spending patterns.
3️⃣ Use classification models to predict crowding based on weather, holidays & city impact.
4️⃣ Apply time-series forecasting for sales & delivery demand projections.

🏆 Why This Dataset is Valuable

📊 Multidimensional Insights – Combines weather, paydays, and events for a complete picture of crowding & sales trends.
📌 Business & Logistics Applications – Helps companies plan deliveries, optimize marketing, and predict demand.
⚡ Unique & Rich Data – A rare dataset covering Saudi Arabia's socio-economic events & crowd impact.

📜 License & Acknowledgments

• 📖 License: CC BY 4.0 – Free to use with attribution.

Conclusion

This dataset is a powerful tool for online delivery companies, businesses, and city planners looking to optimize operations. By analyzing external factors like holidays, paydays, weather, and events, we can predict crowding, improve delivery timing, and forecast sales trends.

🚀 We welcome feedback and contributions! If you find this dataset useful, please ⭐ it on Kaggle and share your insights!

Total-Cashflows-From-Financing-Activities Time Series for Ally Financial Inc. Ally Financial Inc., a digital financial-services company, provides various digital financial products and services in the United States, Canada, and Bermuda. The company operates through Automotive Finance Operations, Insurance Operations, Corporate Finance Operations, and Corporate and Other segments. It offers automotive financing services, including providing retail installment sales contracts, loans and operating leases, term loans to dealers, financing dealer floorplans and other lines of credit to dealers, warehouse lines to automotive retailers, and fleet financing to consumers, automotive dealers and retailers, companies, and municipalities; and financing services to companies and municipalities for the purchase or lease of vehicles, and vehicle-remarketing services. The company also provides consumer finance protection and insurance products through the automotive dealer channel, and commercial insurance products directly to dealers; VSCs, VMCs, and GAP products; and underwrite select commercial insurance coverages, which primarily insure dealers' vehicle inventory. In addition, it provides senior secured asset-based and leveraged cash flow loans to middle-market companies; leveraged loans; commercial real estate product to serve companies in the nursing facilities, senior housing, and medical office buildings; and treasury activities, such as management of the cash and corporate investment securities and loan portfolios, short- and long-term debt, retail and brokered deposit liabilities, derivative instruments, original issue discount, and equity investments. Further, the company offers commercial banking products and services; and securities brokerage and investment advisory services. The company was formerly known as GMAC Inc. and changed its name to Ally Financial Inc. in May 2010. Ally Financial Inc. was founded in 1919 and is based in Detroit, Michigan.

Total-Other-Finance-Cost Time Series for Ringcentral Inc. RingCentral, Inc., together with its subsidiaries, provides cloud business communications, contact center, video, and hybrid event solutions in North America and internationally. The company's products include RingEX, a unified communications as a service platform for collaboration across voice, messaging, and video; RingCentral Contact Center, a contact center solution that delivers omni-channel and workforce engagement solutions; and RingCX, a contact center as a service solution for customer engagement with CRM integrations. It also offers artificial intelligence (AI) solutions, such as AI Receptionist, an AI phone agent; AI Assistant, which automates conversation recaps, captures notes, and summarizes actions; RingSense for transforming conversations into actionable conversational intelligence, sentiment and trend analysis, and sales intelligence and analyzing customer interactions; AI-based Quality Management for coaching and operational insights; AI Agent Assist that provides real-time suggestions and contextual responses; AI Supervisor Assist for real-time monitoring, coaching, and sentiment analysis; and RingCentral for Microsoft Teams. In additions, the company provides RingCentral Events, which enables businesses to host virtual, hybrid, and in-person events with AI-powered engagement tools; and sells pre-configured phones and professional services. It serves a range of industries, including financial services, education, healthcare, legal services, real estate, retail, technology, insurance, construction, hospitality, and state and local government, and others. The company sells its products to enterprise customers, and small and medium-sized businesses through resellers and distributors, partners, and global service providers. RingCentral, Inc. was incorporated in 1999 and is headquartered in Belmont, California.

Return-On-Assets Time Series for EVN Finance JSC. EVN Finance Joint Stock Company provides financial services in Vietnam. The company offers capital mobilization, including receiving deposits; issuing certificates of deposits, bills, and bonds; borrowing from domestic and foreign financial organizations and credit institutions under prevailing regulations; and receiving refinancing loans from the SBV. The company is also involved in credit activities, such as loans and consumer credit; and discounting and re-discounting commercial papers and other valuable papers. In addition, it engages in other finance and banking activities, comprising opening and managing deposit and credit accounts; contributing capital and purchasing shares; providing insurance agency, consultancy, and asset management services; and trading in treasury bonds and foreign currencies. EVN Finance Joint Stock Company was incorporated in 2008 and is headquartered in Ha Noi, Vietnam.

Total-Cash-From-Operating-Activities Time Series for Ameriprise Financial Inc. Ameriprise Financial, Inc., together with its subsidiaries, operates as a diversified financial services company in the United States and internationally. The company offers financial planning and advice services to individual and institutional clients. It operates through Advice & Wealth Management, Asset Management, and Retirement & Protection Solutions segments. The Advice & Wealth Management segment provides financial planning and advice; brokerage products and services for retail and institutional clients; discretionary and non-discretionary investment advisory accounts; mutual funds; insurance and annuities products; cash management and banking products; and face-amount certificates. The Asset Management segment offers investment management, advice, and products to retail, high net worth, and institutional clients through third-party financial institutions, advisor networks, direct retail, and its institutional sales force under the Columbia Threadneedle Investments brand name. Its products include U.S. mutual funds and their non-U.S. equivalents, exchange-traded funds, variable product funds underlying insurance, and annuity separate accounts; and institutional asset management products, such as traditional asset classes, separately managed accounts, individually managed accounts, collateralized loan obligations, hedge funds, collective funds, and property and infrastructure funds. The Retirement & Protection Solutions segment provides variable annuity products, as well as life and disability income insurance products to retail clients. The company was formerly known as American Express Financial Corporation and changed its name to Ameriprise Financial, Inc. in September 2005. Ameriprise Financial, Inc. was founded in 1894 and is based in Minneapolis, Minnesota.

Total-Cash-From-Operating-Activities Time Series for KB Financial Group. KB Financial Group Inc. provides various banking and related financial services to consumers and corporations in South Korea, the United States, New Zealand, China, Cambodia, the United Kingdom, Indonesia, and internationally. It operates through Retail Banking, Corporate Banking, Other Banking Services, Credit Card, Securities, Life Insurance, and Non-Life Insurance segments. The company offers loans, deposit products, and other related financial products and services to large, small, medium-sized enterprises, and small office/home office, as well as individuals and households; investment banking, and brokerage and supporting services; life insurance products; non-life insurance products, including fire, maritime, injury, technology, liability, package, title, guarantee, other special type insurances, automobile, long-term nonlife, property damage, injury, driver, savings, illness, nursing, pension, and others; and credit sale, cash service, card loan, and other supporting services. It also involved in securities and derivatives trading, funding, and other supporting activities. In addition, the company offers foreign exchange transaction; financial investment; credit card and installment financing; financial leasing; auto Installment finance; real estate trust management; capital and collective investment; collection of receivables or credit investigation; software advisory, development, and supply; microfinance; investment advisory; claim; management; savings banking; information and communication; e-commerce; and general advisory services. KB Financial Group Inc. was founded in 1963 and is headquartered in Seoul, South Korea.