In the first quarter of 2020, global stock indices posted substantial losses that were triggered by the outbreak of COVID-19. The period from March 6 to 18 was particularly dramatic, with several stock indices losing more than ** percent of their value. Worldwide panic hits markets From the United States to the United Kingdom, stock market indices suffered steep falls as the coronavirus pandemic created economic uncertainty. The Nasdaq 100 and S&P 500 are two indices that track company performance in the United States, and both lost value as lockdowns were introduced in the country. European markets also recorded significant slumps, which triggered panic selling among investors. The FTSE 100 – the leading share index of companies in the UK – plunged by as much as ** percent in the opening weeks of March 2020. Is it time to invest in tech stocks? The S&P 500 is regarded as the best representation of the U.S. economy because it includes more companies from the leading industries. However, helped in no small part by its focus on tech companies, the Nasdaq 100 has risen in popularity and seen remarkable growth in recent years. Global demand for digital technologies has increased further due to the coronavirus, with remote working and online shopping becoming part of the new normal. As a result, more investors are likely to switch to the tech stocks listed on the Nasdaq 100.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-12-01 about VIX, volatility, stock market, and USA.

Over ********** of respondents to a 2021 survey purchased fixed income ETFs - or exchange traded funds - during periods of heightened market volatility, such as during the economic collapse caused by the global coronavirus (COVID-19) pandemic in March 2020. The least common response was to reduce ETF positions, which was chosen by ** percent of respondents.

Google 2020-2025 Stock Price

Alphabet Inc. is an American multinational technology conglomerate holding company headquartered in Mountain View, California. Alphabet is the world's second-largest technology company by revenue, after Apple, and one of the world's most valuable companies. It was created through a restructuring of Google on October 2, 2015, and became the parent holding company of Google and several former Google subsidiaries. It is considered one of the Big Five American information technology companies, alongside Amazon, Apple, Meta, and Microsoft.

The establishment of Alphabet Inc. was prompted by a desire to make the core Google business "cleaner and more accountable" while allowing greater autonomy to group companies that operate in businesses other than Internet services. Founders Larry Page and Sergey Brin announced their resignation from their executive posts in December 2019, with the CEO role to be filled by Sundar Pichai, who is also the CEO of Google. Page and Brin remain employees, board members, and controlling shareholders of Alphabet Inc.

Source: https://en.wikipedia.org/wiki/Alphabet_Inc.

Information about this dataset

This dataset provides historical data of GOOG. stock (Google). The data is available at a daily level. Currency is USD.

These terms are key indicators in stock market trading and analysis, providing information about a stock's price movements and trading activity over a specific period (e.g., a day, week, or month):

Close Price:

The final price at which a stock trades during a specific trading session (e.g., at the end of the day). This price is often used as a reference point for comparing daily price movements.

Open Price:

The first price at which a stock trades when the market opens for the day. It can be influenced by after-hours trading, news, or economic events.

High Price:

The highest price at which a stock trades during a specific trading session. It shows the maximum value reached by the stock in that period.

Low Price:

The lowest price at which a stock trades during a specific trading session. It represents the minimum value reached by the stock in that period.

Volume:

The total number of shares traded during a specific period. It indicates the level of interest or activity in a stock, with higher volumes often reflecting greater market interest or volatility.

Global Market Volatility Market is segmented by Application (Financial Markets_Hedge Funds_Trading Firms_Investment Banks_Asset Managers), Type (Volatility Indices_Risk Management Platforms_Trading Platforms_Forecasting Tools_Data Analytics), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

United States - Equity Market Volatility Tracker: Healthcare Matters was 2.87035 Index in October of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Healthcare Matters reached a record high of 10.15130 in March of 2020 and a record low of 0.00000 in August of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Healthcare Matters - last updated from the United States Federal Reserve on December of 2025.

Graph and download economic data for CBOE S&P 500 3-Month Volatility Index (VXVCLS) from 2007-12-04 to 2025-12-01 about VIX, volatility, stock market, 3-month, and USA.

United States - Equity Market Volatility Tracker: Entitlement And Welfare Programs was 4.83390 Index in September of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Entitlement And Welfare Programs reached a record high of 10.96995 in March of 2020 and a record low of 0.13203 in May of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Entitlement And Welfare Programs - last updated from the United States Federal Reserve on November of 2025.

The market volatility caused by the coronavirus pandemic resulted in low trading activity among investors in the United States in 2020. According to a survey from May to June 2020, only ** percent of retirement-only investors stated to have bought, sold, or both bought and sold investments. The activity was higher among taxable account investors, where ** percent stated to have both bought and sold investments, another ** percent bought, and another ** percent had sold.

Our measure of disease-related uncertainty is the infectious disease equity market volatility developed by Baker et al. (2020) [9]. Using sophisticated textual analysis, Baker et al. (2020) [9] search for news articles related to infectious diseases and equity market volatility. A higher fraction of these news articles to all articles in each time period signifies a higher level of market uncertainty that can be ascribed to infectious diseases. For ease of interpretation, we have the infectious disease equity market volatility index divided by 100.

The net value of retail sales of money market funds in the United Kingdom (UK) fluctuated considerably between January 2020 and January 2025. The net value of retail sales of money market funds was negative in January 2025 and amounted to **** million British pounds. What are money market funds? Money market funds are a category of mutual funds that invest in liquid and short-term assets. The composition of money market assets is designed to provide investors with a predictable and relatively secure return on their investment while simultaneously preserving liquidity. In addition, the increasing inflow of money market funds signifies heightened investor demand for safety and liquidity, often triggered by rising risk aversion in the face of economic uncertainty or market volatility. In March 2020, the fund flow of money market funds in the United States jumped by over ** percent, surging from ***** to ***** billion U.S. dollars. This significant rise in money market fund flows could be attributed to the elevated economic uncertainty and market turmoil resulting from the COVID-19 pandemic. What do money market fund values indicate? The trajectory of the value of money market funds in a country reveals the sentiments of investors, the economic performance, and the evolution of the market. The ascending trend in these funds often indicates a flight to safety by those looking for security and liquidity, especially during times of increased market volatility or economic uncertainty. The value of money market funds in the United Kingdom remained quite stable, with a few exceptions. This indicates a general sense of security, low volatility, and a cautious approach to investing in the marketplace.

The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.

Graph and download economic data for CBOE 10-Year Treasury Note Volatility Futures (DISCONTINUED) (VXTYN) from 2003-01-02 to 2020-05-15 about notes, volatility, stock market, 10-year, Treasury, and USA.

United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Labor Markets was 8.51688 Index in September of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Labor Markets reached a record high of 24.55960 in March of 2020 and a record low of 1.81916 in March of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Labor Markets - last updated from the United States Federal Reserve on November of 2025.

United States - Equity Market Volatility Tracker: Intellectual Property Matters was 0.61508 Index in October of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Intellectual Property Matters reached a record high of 3.42230 in April of 2025 and a record low of 0.00000 in December of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Intellectual Property Matters - last updated from the United States Federal Reserve on November of 2025.

This dataset provides synthetic daily stock market data for 50 representative U.S.-listed companies across multiple sectors, covering the period 2020–2024.

It includes traditional OHLC (open, high, low, close) data, adjusted close prices, trading volume, and synthetic fundamental indicators such as market capitalization, P/E ratio, and dividend yield. Additionally, daily returns and 30-day rolling volatility are provided to support time-series and risk modeling.

The dataset is fully synthetic, generated via statistical simulations to mimic realistic U.S. stock market behavior across sectors such as Technology, Financials, Healthcare, Energy, Consumer Discretionary, and Industrials. It does not represent real financial data, making it safe for learning, research, and experimentation.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

📝 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.

NEPSE Daily Index Data (2020-2025)

Subtitle: Daily NEPSE Index Values, Changes, and Percentages from 2020 to March 2025

This dataset provides a comprehensive collection of daily historical data for the Nepal Stock Exchange (NEPSE) Index, covering the period from January 1, 2020, to March 4, 2025 (with data available up to March 4, 2025, at the time of creation). The NEPSE Index is the primary benchmark for Nepal's stock market, reflecting the performance of listed companies. This dataset was scraped from publicly available financial data sources and is intended for financial analysis, time series forecasting, and economic research related to Nepal's capital markets.

Dataset Details

Columns:

• Date: The trading day in YYYY-MM-DD format (e.g., 2025-03-04).
• Index Value: The closing value of the NEPSE Index for the day, representing the market’s end-of-day performance.
• Absolute Change: The daily change in the index value (positive or negative), calculated as the difference from the previous trading day’s close.
• Percentage Change: The daily percentage change in the index value, reflecting the relative movement (e.g., -1.05 for a -1.05% drop).

Data Range:

• Start Date: January 1, 2020
• End Date: March 4, 2025 (latest available as of dataset creation)
• Frequency: Daily (trading days only; excludes weekends and public holidays as per NEPSE’s schedule)

Size:

• Approximately 1,200–1,500 rows (depending on trading days), covering over 5 years of data.

Source

The data was sourced from ShareSansar, a leading financial portal in Nepal providing NEPSE index history. It was scraped using Python with Selenium and processed into a clean CSV format.

Methodology

• Scraping: Automated web scraping extracted daily records from ShareSansar’s Index History Data page.
• Processing: Data was cleaned to ensure consistent date formatting and numeric values (removing commas and percentage signs).
• Tools: Python libraries including Selenium, BeautifulSoup, and pandas were used to compile this dataset.

Usage

This dataset is ideal for: - Time Series Analysis: Forecasting NEPSE trends using models like ARIMA, LSTM, or Prophet. - Financial Research: Studying Nepal’s stock market volatility, growth patterns, or economic correlations. - Data Visualization: Plotting index trends, daily changes, or comparative analyses with other markets. - Educational Purposes: Learning data analysis or financial modeling with real-world data.

The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.