Introducing our comprehensive economic calendar, your ultimate resource for tracking major global economic events and their impact on currency and stock market prices. With a vast array of fields including event name, country, previous and current values, and more, our calendar provides you with essential data to make informed financial decisions. Stay ahead of the curve with our real-time updates, ensuring you have access to the latest information every 15 minutes. With this powerful tool at your fingertips, you can confidently navigate the dynamic world of economic events and seize opportunities for success. Don't miss out on this essential resource for staying informed and making calculated moves in the market.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

中文 | 日本語

  Economic Calendar Events Dataset





  Data Source

This dataset is generated by merging multiple economic calendar JSON files. The original data comes from various public economic calendar sources. Each JSON file contains several economic events, covering holidays, macroeconomic indicators, and important announcements from major countries and regions worldwide.

  Data Content

Each row represents a single economic event, with fields including event ID, title… See the full description on the dataset page: https://huggingface.co/datasets/huggingXG/forex_calendar.

NYSE Integrated is a proprietary data feed that disseminates full order book updates from the New York Stock Exchange (XNYS). It delivers every quote and order at each price level, along with any event that updates the order book after an order is placed, such as trade executions, modifications, or cancellations.

NYSE is the leading venue for listing blue-chip companies and large-cap stocks. Powered by NYSE's Pillar platform, its hybrid market model of floor-based auction and electronic trading allows it to capture a significant portion of trading activity during the US equity market open and close. As of January 2025, the NYSE represented approximately 6.31% of the average daily volume (ADV) across all exchange-listed US securities, including those listed on Nasdaq, other NYSE venues, and Cboe exchanges.

NYSE is also the only exchange to offer Designated Market Maker (DMM) privileges, allowing the floor to send D-Quote Orders, short for Discretionary Orders, throughout the day. Most D-Quote Orders execute in the closing auction, where they're known as Closing D Orders and allow traders to access the NYSE closing auction after 3:50 PM. This creates significant price discovery during the NYSE Closing Auction, where interest represented via the floor contributes more than 40% of total volume.

NYSE is also unique for being the only exchange with a Parity/Priority Allocation model for matching. This resembles a mixed FIFO and pro-rata matching algorithm, where the participant who sets the best price is matched first, and then the remaining shares are allocated to other orders entered by floor brokers at that price (parity allocation). Floor brokers may utilize e-Quotes to to receive such parity allocation of incoming executions.

With L3 granularity, NYSE Integrated captures information beyond the L1, top-of-book data available through SIP feeds, enabling accurate modeling of the book imbalances, queue dynamics, and the auction process. This data includes explicit trade aggressor side, odd lots, and imbalances. Auction imbalances offer valuable insights into NYSE’s opening and closing auctions by providing details like imbalance quantity, paired quantity, imbalance reference price, and book clearing price.

Historical data is available for usage-based rates or with any Databento US Equities subscription. Visit our pricing page for more details or to upgrade your plan.

Asset class: Equities

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON (Learn more)

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, BBO-1s, BBO-1m, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Imbalance, Statistics, Status (Learn more)

Resolution: Immediate publication, nanosecond-resolution timestamps

Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis

Experimental studies in the area of Psychology and Behavioral Economics have suggested that people change their search pattern in response to positive and negative events. Using Internet search data provided by Google, we investigated the relationship between stock-specific events and related Google searches. We studied daily data from 13 stocks from the Dow-Jones and NASDAQ100 indices, over a period of 4 trading years. Focusing on periods in which stocks were extensively searched (Intensive Search Periods), we found a correlation between the magnitude of stock returns at the beginning of the period and the volume, peak, and duration of search generated during the period. This relation between magnitudes of stock returns and subsequent searches was considerably magnified in periods following negative stock returns. Yet, we did not find that intensive search periods following losses were associated with more Google searches than periods following gains. Thus, rather than increasing search, losses improved the fit between people’s search behavior and the extent of real-world events triggering the search. The findings demonstrate the robustness of the attentional effect of losses.

The size of the Middle East And Africa ETF Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 9.00">> 9.00% during the forecast period. The ETF (Exchange-Traded Fund) market refers to the financial industry focused on creating, managing, and trading ETFs, which are investment funds that track the performance of a specific index, sector, commodity, or asset class. ETFs combine the diversification of mutual funds with the liquidity and convenience of stocks, allowing investors to buy or sell shares throughout the trading day at market prices. This industry is a key segment of the broader financial markets and has grown rapidly due to its accessibility, cost efficiency, and flexibility for both retail and institutional investors. ETFs are often classified based on the assets they track, such as equities, bonds, commodities, or currencies. The ETF market offers a wide variety of products, including index-based ETFs, which mirror well-known indices like the S&P 500, sector-specific ETFs that focus on industries like technology or healthcare, and thematic ETFs, which center around global trends like clean energy or artificial intelligence. These products are usually managed by large financial institutions like BlackRock, Vanguard, and State Street Global Advisors. Recent developments include: In March 2024, Abu Dhabi Securities Exchange and HSBC Bank have entered into a partnership to expand the availability of digital fixed-income securities in the capital markets of the region. In collaboration with HSBC, ADX will investigate a framework that would allow digital assets, such digital bonds, to be listed on ADX and accessible via HSBC Orion, the bank's digital assets platform., In September 2023, the Ministry of Investment signed agreements with Al-Rajhi Bank, Alinma Bank, and Banque Saudi Fransi to strengthen the position of the digital banking industry and aid these institutions provide investors with better service.. Key drivers for this market are: Decline in Cost of Service Providers, Availiblity of New distribution platform in the region. Potential restraints include: Market Saturation (lack of Availiblity of new asset class), Extreme market events increasing risk associate with ETF, dampening their demand.. Notable trends are: Equity ETFs a Gateway to Diversified Exposure in the Region's Stock Markets.

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

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

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg"> #
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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

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

Dataset

This dataset was created by cTatu

Contents

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.

📅 Forex Factory Economic Calendar Dataset (2007-01-01 to 2025-04-07)

This dataset contains a comprehensive archive of macroeconomic calendar events sourced from Forex Factory, spanning from January 1, 2007 to April 7, 2025.Each row captures a specific event with detailed metadata including currency, event type, market impact level, reported values, and descriptive context.

  📦 Dataset Summary

Total timespan: 2007-01-01 → 2025-04-07
Format: CSV (UTF-8)
Timezone:… See the full description on the dataset page: https://huggingface.co/datasets/Ehsanrs2/Forex_Factory_Calendar.

Techsalerator's News Events Data for Guinea-Bissau: A Comprehensive Overview

Techsalerator's News Events Data for Guinea-Bissau offers a crucial resource for businesses, researchers, and media organizations. This dataset compiles information on significant news events across Guinea-Bissau, drawn from a wide range of media sources, including news outlets, online publications, and social platforms. It provides valuable insights for those looking to track trends, analyze public sentiment, or monitor industry-specific developments.

Key Data Fields - Event Date: Captures the exact date of the news event. This is important for analysts who need to monitor trends over time or for businesses responding to market shifts.

• Event Title: A brief headline describing the event. This allows users to quickly categorize and assess news content based on relevance to their interests.

• Source: Identifies the news outlet or platform where the event was reported. This helps users track credible sources and assess the reach and influence of the event.

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

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

Top 5 News Categories in Guinea-Bissau - Politics: Major news coverage on government decisions, political movements, elections, and policy changes that affect the national landscape.

• Economy: Focuses on Guinea-Bissau’s economic indicators, international trade, and corporate activities influencing business and finance sectors.

• Social Issues: News events covering public health, education, protests, and other societal concerns driving public discourse.

• Culture and Heritage: Highlights significant events related to Guinea-Bissau’s rich cultural heritage, including music, arts, and traditional festivals.

• Sports: Features key updates on popular sports such as football, often attracting widespread attention and engagement throughout the country.

Top 5 News Sources in Guinea-Bissau - Lusa News Agency: A Portuguese-language news service that provides comprehensive coverage of political, economic, and social events in Guinea-Bissau.

• Rádio Difusão Nacional: The national broadcaster offering updates on current affairs, including politics, culture, and international relations.

• O Democrata: A local publication focusing on political developments, social issues, and economic activities within the country.

• ANG (Agência de Notícias da Guiné): A news agency providing real-time coverage of significant national events and developments in Guinea-Bissau.

• No Pintcha: A widely-read newspaper that covers sports, culture, and general news, with an emphasis on local events and happenings.

Accessing Techsalerator’s News Events Data for Guinea-Bissau To access Techsalerator’s News Events Data for Guinea-Bissau, 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 an invaluable tool for tracking significant events in Guinea-Bissau. It aids in making informed decisions, whether for business strategy, market analysis, or academic research, providing a clear picture of the country’s news landscape.

• This is the Historical Stock Market Data of five major Big Tech companies: NVIDIA (NVDA), Apple (AAPL), Microsoft (MSFT), Google (GOOGL), and Amazon (AMZN) over a 15 years from January 1, 2010 to January 1, 2025.
• It includes daily stock data with opening and closing prices, highs, lows and trading volume.
• This dataset serves as a valuable resource for analyzing long term growth trends, volatility and market behavior of leading tech giants.
• By analyzing this dataset, we can gain a deeper understanding of NVDA, AAPL, MSFT, GOOGL, and AMZN's historical stock behavior over 15 years and make predictions about their future performance.

Columns Description:

1. Date: The trading date of the stock data entry.
2. Close_AAPL: Apple’s stock price at market close at the end of the trading days.
3. Close_AMZN: Amazon’s stock price at market close at the end of the trading days.
4. Close_GOOGL: Google’s stock price at market close at the end of the trading days.
5. Close_MSFT: Microsoft’s stock price at the end of the trading days.
6. Close_NVDA: NVIDIA’s stock price at the end of the trading days.
7. High_AAPL: The highest price of Apple’s stock reached during the trading days.
8. High_AMZN: The highest price of Amazon’s stock reached during the trading days.
9. High_GOOGL: The highest price of Google’s stock reached during the trading days.
10. High_MSFT: The highest price of Microsoft’s stock reached during the trading days.
11. High_NVDA: The highest price of NVIDIA’s stock reached during the trading days.
12. Low_AAPL: The lowest price of Apple’s stock reached during the trading days.
13. Low_AMZN: The lowest price of Amazon’s stock reached during the trading days.
14. Low_GOOGL: The lowest price of Google’s stock reached during the trading days.
15. Low_MSFT: The lowest price of Microsoft’s stock reached during the trading days.
16. Low_NVDA: The lowest price NVIDIA’s stock reached during the trading days.
17. Open_AAPL: Apple’s opening stock price at the beginning of the trading days.
18. Open_AMZN: Amazon’s opening stock price at the beginning of the trading days.
19. Open_GOOGL: Google’s opening stock price at the beginning of the trading days.
20. Open_MSFT: Microsoft’s opening stock price at the beginning of the trading days.
21. Open_NVDA: NVIDIA’s opening stock price at the beginning of the trading days.
22. Volume_AAPL: The number of shares traded of Apple’s stock during the trading days.
23. Volume_AMZN: The number of shares traded of Amazon’s stock during the trading days.
24. Volume_GOOGL: The number of shares traded of Google’s stock during the trading days.
25. Volume_MSFT: The number of shares traded of Microsoft’s stock during the trading days.
26. Volume_NVDA: The number of shares traded of NVIDIA’s stock during the trading days.

Usefulness of Data:

1. Trend Analysis: This dataset can be used for the analysis of long term stock price trends for major 5 tech companies. By analyzing this dataset and taking deep insights about the data and stock patterns over 15 years, investors can identify potential opportunities.
2. Volatility and Risk Assessment: The data helps to assess the volatility of 5 big tech companies' stocks by comparing highs and lows and provides the management strategies to the investors.
3. Predictive Modeling: With stock prices, this dataset can be used for developing predictive models such as forecasting future stock prices using techniques such as ARIMA, SARIMAX, or Deep Learning Models.
4. Comparative Analysis: By analyzing this Dataset, researchers and analysts can compare the performance of NVIDIA, Apple, Microsoft, Google, and Amazon over 15 years, which helps to identify trends in the stock market and relative growth between these companies.
5. Market Behavior Understanding: By analyzing how each stock reacts to major market events (e.g., earnings reports & macroeconomic changes, etc.), we can understand the companies' growth & patterns.

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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.

US Stock Market Data (21st November 2023 – 2nd February 2024)

Overview

This dataset provides detailed historical data on the US stock market, covering the period from 21st November 2023 to 2nd February 2024. It includes daily performance metrics for major stocks and indices, enabling investors, analysts, and researchers to study short-term market trends, fluctuations, and patterns.

Dataset Contents

The dataset contains the following key attributes for each trading day:

Date: The trading date.

Ticker: Stock ticker symbol (e.g., AAPL for Apple, MSFT for Microsoft).

Open Price: The price at which the stock opened for trading.

Close Price: The price at which the stock closed for trading . High Price: The highest price reached during the trading session.

Low Price: The lowest price reached during the trading session.

Adjusted Close Price: The closing price adjusted for splits and dividend payouts.

Trading Volume: The total number of shares traded on that day.

Highlights

Time Period: Covers daily data for over two months of trading activity.

Market Scope: Includes data from a diverse set of stocks, industries, and sectors, reflecting the broader US market trends.

Indices and Major Stocks: Tracks key indices (e.g., S&P 500, NASDAQ) and major stocks across various sectors .

Potential Applications

Analyzing short-term market performance trends. Developing trading strategies or backtesting investment models. Exploring the impact of macroeconomic events on stock performance. Studying sector-wise performance in the US stock market.

Data Source

The data has been sourced from publicly available market records, ensuring reliability and accuracy. Each data point represents an official trading record from the respective exchange.

Usage Notes

The dataset is intended for educational, analytical, and research purposes only. Users should be mindful of potential market anomalies or external factors influencing data during this time frame.

Acknowledgments

Special thanks to the organizations and platforms that make financial market data accessible for analysis and research.

India's main stock market index, the SENSEX, rose to 82571 points on July 15, 2025, gaining 0.39% from the previous session. Over the past month, the index has climbed 0.95% and is up 2.30% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Snapshot img

Corporate Event Market Size 2025-2029

The corporate event market size is forecast to increase by USD 221.7 billion at a CAGR of 10.8% between 2024 and 2029.

The market is experiencing significant growth due to several key factors. One of the primary drivers is the increasing corporate budgets allocated towards organizing events. Another trend shaping the market is the adoption of artificial intelligence (AI) and machine learning technology for event management, offering enhanced efficiency and personalized experiences. Geopolitical and economic uncertainty also present opportunities for the market, as companies turn to events as a means of building relationships and addressing business challenges. These trends, coupled with the ongoing digital transformation, are expected to shape the future of the market.

What will be the Size of the Corporate Event Market during the Forecast Period?

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The market encompasses a diverse range of activities designed to foster knowledge sharing, team-building, and organizational success. These events include workshops, project-based gatherings, product launches, and conferences, among others. They serve various business objectives, such as networking, brand awareness, and strategic planning. Company culture is strengthened through appreciation dinners, seminars, and leadership engagement programs. Trade shows, exhibitions, and business seminars provide opportunities for business expansion and innovation, while entrepreneurship events ignite new ideas and opportunities. Incentive group activities and training programs cater to professional development and employee motivation. Silent conferences and lunch clubbing encourage introspection and networking in unique settings.
The strong economic climate has led to an increase in corporate event demand, with companies investing in both traditional and online events. Branded multi-use apps, projection mapping, and foreign direct investment are transforming the event landscape, offering new opportunities for corporate organizations. Overall, the market is a vibrant and evolving sector, driven by the ever-changing needs of businesses and the continuous pursuit of organizational growth.

How is this Corporate Event Industry segmented and which is the largest segment?

The corporate event industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Conferences
  Trade shows
  Incentive programs
  Company meetings
  Others


Platform

  Physical events
  Virtual events
  Hybrid events


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    Japan
    Singapore


  Middle East and Africa



  South America

By Type Insights

The conferences segment is estimated to witness significant growth during the forecast period.

The market encompasses conferences and seminars that serve as crucial platforms for knowledge sharing, networking, and industry discourse. These events cater to diverse audiences, including corporate leaders, employees, and industry experts. Industry conferences, such as the Consumer Electronics Show (CES) and Web Summit, showcase industry trends, technological advancements, and networking opportunities. Business executives, innovators, and thought leaders attend these events to exchange insights, explore innovations, and discuss emerging trends. CES is an annual conference held at the Las Vegas Convention Center in Winchester, Nevada, US, while Web Summit takes place every November at the MEO Arena and Lisbon Exhibition and Congress Centre in Lisbon, Portugal.

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The conferences segment was valued at USD 91.00 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 34% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market is characterized by substantial corporate spending, advanced event infrastructure, and a significant presence of multinational companies. The US dominates the region, hosting a majority of events due to its strong business ecosystem, world-class venues, and high demand for in-person networking opportunities. Canada also plays a crucial role, with major cities like Toronto, Vancouver, and Montreal serving as key event destinations. Notable events such as the Consumer Electronics Show (CES) in Las Vegas, Dreamforce in San Francisco, and Collision i

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

The India B2B events market is experiencing robust growth, projected to reach $534.70 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 11.72% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing adoption of digital technologies is transforming the events landscape, with virtual and hybrid events gaining traction, complementing traditional physical events. Secondly, a burgeoning number of businesses across diverse sectors—including Food and Beverage, Public Sector Units (PSUs), Luxury, Banking, Financial Services and Insurance (BFSI), Fast-Moving Consumer Goods (FMCG), Retail, Healthcare, and Automotive—are recognizing the value of B2B events for networking, lead generation, and brand building. The rising disposable incomes and economic growth in India further fuel this market expansion. Furthermore, strategic partnerships and collaborations between event organizers and technology providers are enhancing event experiences, creating more engaging and efficient platforms for attendees. However, the market also faces certain challenges. Competition amongst numerous event management companies necessitates continuous innovation and differentiation. Economic downturns or unforeseen events (like pandemics) can significantly impact event participation and spending. Therefore, successful players must adapt swiftly to changing market conditions, embrace technological advancements, and offer highly targeted and valuable experiences to maintain market share. The market segmentation across platforms (physical and virtual) and end-user verticals allows for focused strategies, maximizing returns in specific niches and minimizing susceptibility to wider market fluctuations. Major players like Sapphire Connect, Mantra, Seventy EMG, and others are actively shaping the market through innovative offerings and strategic acquisitions. This report provides an in-depth analysis of the burgeoning India B2B events market, offering invaluable insights for businesses looking to capitalize on its immense growth potential. With a study period spanning 2019-2033, a base year of 2025, and a forecast period from 2025-2033, this report utilizes data from the historical period (2019-2024) to project future trends and market size in the millions. The report segments the market by platform (physical and virtual events), end-user verticals (Food and Beverage, PSU, Luxury, BFSI, FMCG, Retail, Healthcare, Automotive, and Others), and key players, providing a granular understanding of this dynamic sector. Recent developments include: In March 2024, by bringing together 3,500 exhibitors from across the entire value chain under one roof for the first time, the theme of Bharat Tex 2024 emphasized India’s capability to provide end-to-end textile solutions. Spread across nearly two million square feet and attracting 100,000 visitors, this huge event, staged in New Delhi, was organized by a consortium of 11 textile export promotion councils and sponsored by the country’s Ministry of Textile., In November 2023, a mega B2B food event was organized in Delhi. The mega food festival generated significant interest from foreign and Indian stakeholders, organized in collaboration with ten ministries of government, six commodities commissions, and 25 states. A total of 1208 exhibitors, 14 country pavilions, and significant participation by 715 foreign buyers, 218 domestic buyers, and 97 corporate executives were present at this event. The event brought together a broad range of platforms for highlighting the most recent developments in the food processing industry, covering an area of over 50,000 m2 across seven spaces. The event was attended by 14 delegations from the member states, seven of which were ministers. The distinguished participation of the Netherlands as a partner country and Japan as the focal country further enhanced the global appeal of this event.. Key drivers for this market are: Mobile e-commerce to be the fastest-growing retailing channel due to proliferation of mobile apps and convenience, Retailers develop mobile-friendly strategies to attract young and tech-savvy consumers. Potential restraints include: , Lack of Awareness Among Government Organizations About New Technologies. Notable trends are: Retail Sector to be the Largest End User.