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.

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.

BCC Research Market Report on Cognitive Computing. Global Cognitive Computing market size estimates and forecasts for the global market and all major market segments through 2026.

Index: TSE: 1st Section: MA: Real Estate data was reported at 1,520.779 04Jan1968=100 in Jun 2018. This records a decrease from the previous number of 1,559.857 04Jan1968=100 for May 2018. Index: TSE: 1st Section: MA: Real Estate data is updated monthly, averaging 925.960 04Jan1968=100 from Dec 1987 (Median) to Jun 2018, with 367 observations. The data reached an all-time high of 2,363.700 04Jan1968=100 in Dec 1989 and a record low of 402.363 04Jan1968=100 in Apr 2003. Index: TSE: 1st Section: MA: Real Estate 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.

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.

In 2024, the United States was the biggest mobile gaming market worldwide based on revenue. During the measured period, gaming users in the U.S. generated about **** billion U.S. dollars in gaming app revenues.

Hungary - Debt sec, issued by gen govt, in all markets at all original maturities denominated in all currencies at market value stocks

Replication Data for "Total Factor Productivity and Sustained Integration with World Markets: Evidence from Emerging Markets"

Poland - Debt sec, issued by residents, in all markets at all original maturities denominated in all currencies at market value stocks

Explore the dynamics of LME copper trading, influenced by global demand, geopolitical events, and green technology trends. Discover how real-time copper prices provide insights into economic conditions and impact investment strategies on the influential London Metal Exchange.

This table shows prices on various world markets as published daily in the Financieele Dagblad. Statistics Netherlands calculates average monthly prices, quarterly averages and annual averages on the basis of the daily prices. The table is divided into the following product groups: Agricultural Products, Tropical Products, Precious Metals, Metals and Energy. In addition, the exchange rates of the US dollar, the British pound and the Japanese yen against the Euro and the Euro against the US dollar, the British pound and the Japanese yen are shown according to the Amsterdam stock exchange. The data are available from: 2006 Status of the figures. The figures are immediately final. When will new figures be available? The new figures will be published approximately 14 days after the month under review.

Sweden - Debt sec, issued by central bank, in all markets at org mat > 1y and <= 2y denominated in all currencies at nominal value stocks

Russia Number of Trades: Money Market data was reported at 180.600 Unit th in Nov 2017. This records a decrease from the previous number of 188.800 Unit th for Oct 2017. Russia Number of Trades: Money Market data is updated monthly, averaging 156.600 Unit th from Jul 2012 (Median) to Nov 2017, with 65 observations. The data reached an all-time high of 310.500 Unit th in Dec 2014 and a record low of 97.200 Unit th in Jan 2016. Russia Number of Trades: Money Market data remains active status in CEIC and is reported by Moscow Exchange. The data is categorized under Global Database’s Russian Federation – Table RU.ZA010: Moscow Exchange: All Markets: Number of Trades.

Poland - Debt sec, issued by FC, in all markets at org mat > 10y denominated in all currencies at nominal value stocks

Russia Number of Trades: FX Market: Spot Trades: Others data was reported at 2.000 Unit th in Nov 2017. This stayed constant from the previous number of 2.000 Unit th for Oct 2017. Russia Number of Trades: FX Market: Spot Trades: Others data is updated monthly, averaging 1.400 Unit th from Dec 2014 (Median) to Nov 2017, with 36 observations. The data reached an all-time high of 2.500 Unit th in Sep 2017 and a record low of 0.500 Unit th in Jan 2015. Russia Number of Trades: FX Market: Spot Trades: Others data remains active status in CEIC and is reported by Moscow Exchange. The data is categorized under Global Database’s Russian Federation – Table RU.ZA010: Moscow Exchange: All Markets: Number of Trades.

Japan Index: TSE: 1st Section: Metal Products data was reported at 1,159.060 04Jan1968=100 in Nov 2018. This records an increase from the previous number of 1,146.790 04Jan1968=100 for Oct 2018. Japan Index: TSE: 1st Section: Metal Products data is updated monthly, averaging 980.360 04Jan1968=100 from Jan 1994 (Median) to Nov 2018, with 297 observations. The data reached an all-time high of 1,794.290 04Jan1968=100 in Jan 1994 and a record low of 576.940 04Jan1968=100 in Feb 2009. Japan Index: TSE: 1st Section: Metal Products 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.

Japan Index: TSE: 1st Section: Iron and Steel data was reported at 515.460 04Jan1968=100 in Jun 2018. This records a decrease from the previous number of 536.970 04Jan1968=100 for May 2018. Japan Index: TSE: 1st Section: Iron and Steel data is updated monthly, averaging 565.595 04Jan1968=100 from Jan 1994 (Median) to Jun 2018, with 292 observations. The data reached an all-time high of 1,749.000 04Jan1968=100 in Jul 2007 and a record low of 262.540 04Jan1968=100 in Dec 2002. Japan Index: TSE: 1st Section: Iron and Steel 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.

Russia Turnover: Money Market: Repo on the Exchange Market: Repo in Standard/Classica data was reported at 0.000 RUB bn in Jan 2019. This stayed constant from the previous number of 0.000 RUB bn for Dec 2018. Russia Turnover: Money Market: Repo on the Exchange Market: Repo in Standard/Classica data is updated monthly, averaging 0.000 RUB bn from Apr 2010 (Median) to Jan 2019, with 106 observations. The data reached an all-time high of 51.686 RUB bn in Aug 2012 and a record low of 0.000 RUB bn in Jan 2019. Russia Turnover: Money Market: Repo on the Exchange Market: Repo in Standard/Classica data remains active status in CEIC and is reported by Moscow Exchange. The data is categorized under Russia Premium Database’s Financial Market – Table RU.ZA008: Moscow Exchange: All Markets: Turnover Detailed.

Germany - Debt sec, issued by residents, in all markets at org mat > 10y denominated in all currencies at nominal value stocks

Foreign exchange markets data