The statistic shows the development of the MSCI World USD Index from 1986 to 2024. The 2024 year-end value of the MSCI World USD index amounted to ******** points. MSCI World USD index – additional information The MSCI World Index, developed by Morgan Stanley Capital International (MSCI), is one of the most important stock indices. It includes stocks from developed countries all over the world and is regarded as benchmark of global stock market. According to MSCI, this index covers about ** percent of the free float-adjusted market capitalization in each country. As seen on the statistics above, in 2024, MSCI World USD index reported its highest value since 1986 amounting, a threefold increase from the figure recorded in 2013, when the year-end value of the MSCI World index was equal to ********. Along with the S&P Global Broad Market, the MSCI World is one of the most important global stock market performance indexes. Aside of including markets around the globe, these two indexes are global in a sense that they disregard where the companies are domiciled or traded, whereas other important indexes such as the Dow Jones Industrial Average, the Japanese index Nikkei 225, Wilshire 5000, the NASDAQ 100 index, have different approaches.

The average for 2022 based on 75 countries was 1225.97 billion U.S. dollars. The highest value was in the USA: 40297.98 billion U.S. dollars and the lowest value was in Bermuda: 0.21 billion U.S. dollars. The indicator is available from 1975 to 2024. Below is a chart for all countries where data are available.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around ** percent of their value compared to January *, 2020. However, Asian markets and the NASDAQ Composite Index only shed around ** to ** percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around ** percent higher than in January 2020, while most other markets were only between ** and ** percent higher. Why did the NASDAQ recover the quickest? Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide. Which markets suffered the most? The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

Global Stock Market Financial Dataset (from TradingView)

This collection provides a comprehensive snapshot of over 11,800 publicly traded companies worldwide. It combines multiple financial statements and performance indicators extracted from TradingView to support data analysis, stock screening, and financial modeling.

Files Overview

1.tradingview_all_stocks.csv Contains general stock information and market statistics.

Columns: ticker, name, close, change, change_abs, volume, market_cap_basic, price_earnings_ttm, sector, industry Size: 11,806 rows × 10 columns Description: Lists all active stocks with latest prices, PE ratios, and sector/industry classifications.

2.tradingview_performance.csv Tracks short- and long-term stock performance.

Columns (sample): ticker, name, close, Perf.W, Perf.1M, Perf.3M, Perf.6M, Perf.YTD, Perf.1Y, Perf.5Y, etc. Size: 11,814 rows × 17 columns Description: Shows relative percentage performance across multiple timeframes.

3.balance_sheet.csv Summarizes financial position and liquidity metrics.

Columns: total_assets_fq, cash_n_short_term_invest_fq, total_liabilities_fq, total_debt_fq, net_debt_fq, total_equity_fq, current_ratio_fq Size: 11,821 rows × 12 columns Description: Includes key balance sheet values, enabling leverage and liquidity analysis.

4.cashflow.csv Focuses on company cash generation and sustainability.

Columns: free_cash_flow_ttm Size: 11,821 rows × 4 columns Description: Provides trailing twelve-month free cash flow figures for profitability evaluation.

5.dividends.csv Details dividend-related statistics.

Columns: dividends_yield, dividend_payout_ratio_ttm Size: 11,823 rows × 5 columns Description: Useful for income-focused investors; includes dividend yields and payout ratios.

6.income_statement.csv Presents company earnings metrics.

Columns: total_revenue_ttm, gross_profit_ttm, net_income_ttm, ebitda_ttm Size: 11,821 rows × 7 columns Description: Captures profitability over the last 12 months for revenue and margin analysis.

7.profitability.csv Shows margin-based performance indicators.

Columns: gross_margin_ttm, operating_margin_ttm, net_margin_ttm, ebitda_margin_ttm Size: 11,823 rows × 7 columns Description: Enables efficiency and operational performance comparisons across companies.

Use Cases 1. Stock market and financial analysis 2. Portfolio optimization and factor modeling 3. Machine learning for price prediction 4. Company benchmarking and screening 5. Academic or educational use in finance courses

Data Source & Notes 1. All data was aggregated from TradingView using public financial data endpoints. 2. Missing values may occur for companies that do not report certain metrics. 3. All monetary figures are based on the latest available TTM (Trailing Twelve Months) or FQ (Fiscal Quarter) data at the time of extraction.

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

The value of global domestic equity market increased from ***** trillion U.S. dollars in 2013 to ****** trillion U.S. dollars in 2024. The United States was by far the leading country with the largest share of total world stocks as of 2024. Global market capitalization in different regions The market capitalization of domestic companies listed varied across different regions of the world. As of Decmber 2024, the Americas region had the largest domestic equity market, totaling ** trillion U.S. dollars. This region is home to the NYSE and Nasdaq, which are the two largest stock exchange operators in the world. The market capitalization of these two exchanges alone exceeded ** billion U.S. dollars as of January 2025, larger than the total market capitalization in the Asia-Pacific, and in the EMEA regions in the same period. Largest Stock Exchanges in Latin America As of December 2024, the B3 (Brasil Bolsa Balcao) was the biggest stock exchange in Latin America in terms of market capitalization and the second-largest in terms of number of listed companies. Following the B3 were the Mexican Stock Exchange and the Santiago Stock Exchange in Chile. The most valuable company in Latin America is listed on the Mexican Stock Exchange: Fomento Económico Mexicano, a multinational beverage and retail company headquartered in Monterrey, had a market cap of *** billion U.S. dollars as of March 2025.

United States New York Stock Exchange: Index: US 100 Index data was reported at 18,140.503 NA in Nov 2025. This records an increase from the previous number of 17,877.968 NA for Oct 2025. United States New York Stock Exchange: Index: US 100 Index data is updated monthly, averaging 9,534.600 NA from Jan 2012 (Median) to Nov 2025, with 167 observations. The data reached an all-time high of 18,140.503 NA in Nov 2025 and a record low of 5,695.000 NA in May 2012. United States New York Stock Exchange: Index: US 100 Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Monthly.

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

data regarding stock exchange rates, data regarding CoVID-19, and government actions regarding 15 countries 1-6/2020

Japan Index: TOPIX 100 data was reported at 1,054.690 01Apr1998=1000 in Oct 2018. This records a decrease from the previous number of 1,154.390 01Apr1998=1000 for Sep 2018. Japan Index: TOPIX 100 data is updated monthly, averaging 861.340 01Apr1998=1000 from Feb 1998 (Median) to Oct 2018, with 249 observations. The data reached an all-time high of 1,551.340 01Apr1998=1000 in Dec 1999 and a record low of 478.150 01Apr1998=1000 in May 2012. Japan Index: TOPIX 100 data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

Graph and download economic data for Stock Market Capitalization to GDP for United States (DDDM01USA156NWDB) from 1975 to 2020 about market cap, stock market, capital, GDP, and USA.

Israel's main stock market index, the TA-125, rose to 3538 points on December 2, 2025, gaining 1.75% from the previous session. Over the past month, the index has climbed 4.40% and is up 50.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on December of 2025.

New York Stock Exchange: Index: S&P 500 Industrials Sector data was reported at 1,298.520 NA in Nov 2025. This records a decrease from the previous number of 1,311.710 NA for Oct 2025. New York Stock Exchange: Index: S&P 500 Industrials Sector data is updated monthly, averaging 654.890 NA from Aug 2013 (Median) to Nov 2025, with 148 observations. The data reached an all-time high of 1,311.710 NA in Oct 2025 and a record low of 379.900 NA in Aug 2013. New York Stock Exchange: Index: S&P 500 Industrials Sector data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

As the coronavirus spreads around the world, the impact on the Polish stock exchange is increasing. As of 4 March, the WIG20 index was at the level of 1,860.95 points. Since then, the index has been systematically decreasing, and it reached the level of 1,305.73 points on 12 March. The reason for the falls on the stock exchange is a coronavirus (COVID-19). Fear of the epidemic has been visible in the markets for three weeks. As of 27 March, WIG20 has lost over 31 percent since the beginning of the year. Most probably, the first quarter of 2020 will be the worst in the history of the index. Even worse than the end of the memorable 2008, when the financial crisis broke out. On June 29, WIG20 index reached the closing value of 1,769.47, which is a decrease of 17.70 percent compared to the beginning of 2020.

At the end of February 2025, the DAX index reached ********* points, marking its highest level since January 2015. Moreover, this also reflected a strong recovery from the global coronavirus (COVID-19) pandemic, having risen from ******** points at the end of March 2020 and surpassing its pre-pandemic level of approximately ********* points at the end of December 2019. Origin and composition of the DAX Index The DAX (Deutscher Aktienindex) is the most important German stock index, showing the value trends of the 40 largest companies by market capitalization listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The count among their number some of the most recognizable companies in the world, such as carmakers Volkswagen and Daimler, sportswear brand adidas, and industrial giants Siemens and BASF. After the DAX, the 50 next-largest German companies are included in the midcap MDAX index, while the 70 next-largest small and medium-sized German companies (ranked from 91 to 160) are included in the SDAX index. The Frankfurt Stock Exchange All the companies included in the DAX family of indices are traded on the Frankfurt Stock Exchange. Dating back to 1585, the Frankfurt Stock Exchange is considered to be the oldest exchange in the world. It is the twelfth largest stock exchange in the world in terms of market capitalization, and accounts for around ** percent of all equity trading in Germany. Two main trading venues comprise the Frankfurt Stock Exchange: the Börse Frankfurt is a traditional trading floor; while the Xetra is an electronic trading system which accounts for the vast majority of trading volume on Frankfurt Stock Exchange. As of December 2023, the total market capitalization of all companies listed on the Frankfurt Stock Exchange was around *** trillion euros.

Graph and download economic data for Volatility of Stock Price Index for Portugal (DDSM01PTA066NWDB) from 1988 to 2021 about Portugal, volatility, stocks, price index, indexes, and price.

Description:

This dataset provides key economic indicators from various countries between 2010 and 2023. The dataset includes monthly data on inflation rates, GDP growth rates, unemployment rates, interest rates, and stock market index values. The data has been sourced from reputable global financial institutions and is suitable for economic analysis, machine learning models, and forecasting economic trends.

Data Sources:

The data has been generated to simulate real-world economic conditions, mimicking information from trusted sources like: - World Bank for GDP growth and inflation data - International Monetary Fund (IMF) for macroeconomic data - OECD for labor market statistics - National Stock Exchanges for stock market index values

Columns:

1. Date: The specific date (in Year/Month/Day format) representing when the data was collected.
2. Country: The country the data pertains to (e.g., USA, Germany, Japan).
3. Inflation Rate (%): The rate of inflation for that country, showing how fast prices for goods and services are increasing.
4. GDP Growth Rate (%): The percentage growth of the country’s Gross Domestic Product (GDP), indicating economic expansion or contraction.
5. Unemployment Rate (%): The percentage of the working-age population that is unemployed.
6. Interest Rate (%): The central bank's interest rate, used to control inflation and influence the economy.
7. Stock Index Value: The value of the country’s main stock market index, reflecting the performance of the stock market.

Potential Uses: - Economic Analysis: Researchers and analysts can use this dataset to study trends in inflation, GDP growth, unemployment, and other economic factors. - Machine Learning: This dataset can be used to train models for predicting economic trends or market performance. Financial Forecasting: Investors and economists can leverage this data for forecasting market movements based on economic conditions. - Comparative Studies: The dataset allows comparisons across countries and regions, offering insights into global economic performance.

Graph and download economic data for Dorsey Wright Country and Stock Momentum Index (NASDAQDWACSM) from 2017-12-05 to 2025-10-09 about stocks, NASDAQ, World, and indexes.

The average for 2022 based on 5 countries was 254.46 billion U.S. dollars. The highest value was in Brazil: 794.42 billion U.S. dollars and the lowest value was in Argentina: 52.95 billion U.S. dollars. The indicator is available from 1975 to 2024. Below is a chart for all countries where data are available.