View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

India's main stock market index, the SENSEX, fell to 85138 points on December 2, 2025, losing 0.59% from the previous session. Over the past month, the index has climbed 1.38% and is up 5.31% 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 December of 2025.

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.

Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

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.

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.

Stock market index in Mexico, September, 2025 The most recent value is 139.61 points as of September 2025, an increase compared to the previous value of 132.58 points. Historically, the average for Mexico from January 1970 to September 2025 is 36.41 points. The minimum of 0 points was recorded in January 1970, while the maximum of 139.61 points was reached in September 2025. | TheGlobalEconomy.com

United States FCI-G Index: 1-Yr Lookback: Stock Market data was reported at -0.084 Index in Mar 2025. This records an increase from the previous number of -0.306 Index for Feb 2025. United States FCI-G Index: 1-Yr Lookback: Stock Market data is updated monthly, averaging -0.212 Index from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 1.267 Index in Feb 2009 and a record low of -0.864 Index in Mar 2021. United States FCI-G Index: 1-Yr Lookback: Stock Market data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S021: Financial Conditions Impulse on Growth.

Comprehensive 27+ years of daily stock market data for Indian indices (SENSEX & NIFTY 50) and all their constituent companies. This dataset includes OHLCV data along with pre-calculated technical indicators, making it perfect for time series analysis, algorithmic trading strategies, and machine learning applications.

Total Records: 400,000+
Companies: 80 stocks (30 SENSEX + 50 NIFTY 50)
Features: 21 columns per record

Use Cases:

Machine Learning & Prediction:

• Stock price forecasting using LSTM, GRU, or Transformers
• Next-day close price prediction
• Multi-stock portfolio prediction
• Market regime detection (bull/bear markets)

Technical Analysis:

• Backtest trading strategies (RSI, MACD, Moving Average crossovers)
• Identify support/resistance levels
• Bollinger Band squeeze patterns
• Golden Cross / Death Cross detection

Statistical Analysis:

-Correlation analysis between stocks - Volatility clustering analysis - Market crash impact studies (2008 financial crisis, 2020 COVID) - Sectoral performance comparison

Portfolio Optimization:

• Modern Portfolio Theory implementation
• Risk-return optimization
• Diversification analysis
• Sharpe ratio calculations

Education:

• Financial markets course projects
• Time series analysis tutorials
• Data science portfolio projects
• Algorithmic trading education

Company List:

SENSEX 30 Companies:

Adani Enterprises, Asian Paints, Axis Bank, Bajaj Finance, Bajaj Finserv, Bharti Airtel, HDFC Bank, HCL Technologies, Hindustan Unilever, ICICI Bank, IndusInd Bank, Infosys, ITC, Kotak Mahindra Bank, Larsen & Toubro, Mahindra & Mahindra, Maruti Suzuki, Nestle India, NTPC, ONGC, Power Grid Corporation, Reliance Industries, State Bank of India, Sun Pharmaceutical, Tata Consultancy Services, Tata Motors, Tata Steel, Tech Mahindra, Titan Company, UltraTech Cement, Wipro

NIFTY 50 Companies:

All SENSEX 30 companies plus: Adani Ports, Apollo Hospitals, Bajaj Auto, Bharat Petroleum, Britannia Industries, Cipla, Coal India, Divi's Laboratories, Dr. Reddy's Laboratories, Eicher Motors, Grasim Industries, Hero MotoCorp, Hindalco Industries, Hindustan Zinc, JSW Steel, LTIMindtree, Shriram Finance, Tata Consumer Products, Trent

Ticker Conventions: - .BO suffix = Bombay Stock Exchange (BSE) - .NS suffix = National Stock Exchange (NSE)

Citation Policy:

If you use this dataset in your research, please cite:

Indian Stock Market Historical Data - SENSEX & NIFTY 50 (1997-2024)
Kaggle Dataset, November 2024
URL: https://www.kaggle.com/datasets/rockyt07/stock-market-sensex-nifty-all-time-dataset

The global capital exchange ecosystem market, valued at $1.06 trillion in 2025, is projected to experience robust growth, driven by increasing global trade, the rise of fintech innovations, and a growing preference for digital trading platforms. The market's Compound Annual Growth Rate (CAGR) of 5.80% from 2025 to 2033 signifies a consistently expanding market opportunity. Key segments, including the primary and secondary markets, contribute significantly to this growth, with the primary market fueled by Initial Public Offerings (IPOs) and other new listings, while the secondary market thrives on the continuous trading of existing securities. The diverse range of stock and bond types (common, preferred, growth, value, defensive stocks; government, corporate, municipal, mortgage bonds) caters to a broad spectrum of investor profiles and risk appetites. Technological advancements, including high-frequency trading algorithms and improved data analytics, are further enhancing market efficiency and liquidity. However, regulatory hurdles, geopolitical uncertainties, and cybersecurity threats remain as potential restraints on market growth. The strong presence of established exchanges like the New York Stock Exchange (NYSE), NASDAQ, and the London Stock Exchange, alongside emerging players in Asia and other regions, contributes to the market's competitive landscape. Regional growth will likely be influenced by economic development, regulatory frameworks, and investor confidence, with North America and Asia Pacific anticipated to maintain leading positions. The future of the capital exchange ecosystem hinges on adaptation and innovation. The increasing integration of blockchain technology and decentralized finance (DeFi) is expected to reshape trading infrastructure and potentially challenge traditional exchange models. Increased regulatory scrutiny globally will likely necessitate further transparency and improved risk management practices by exchanges. Furthermore, the growing prominence of Environmental, Social, and Governance (ESG) investing will influence investment strategies and, consequently, trading activity across various asset classes. The market's future success will depend on its ability to effectively manage risks, embrace technological innovation, and meet the evolving needs of a diverse and increasingly sophisticated investor base. Continued growth is anticipated, driven by both established and emerging markets. Recent developments include: In December 2023, Defiance ETFs, introduced the Defiance Israel Bond ETF (NYSE Arca: CHAI) to facilitate investors' access to the Israeli bond market. CHAI commenced trading on the New York Stock Exchange. The ETF, CHAI, mirrors the MCM (Migdal Capital Markets) BlueStar Israel Bond Index, enabling investors to tap into both Israel government and corporate bonds. This index specifically monitors the performance of bonds, denominated in USD and shekels, issued by either the Israeli government or Israeli corporations., In January 2024, the National Stock Exchange (NSE) saw a 22% rise in its investor base, increasing from 70 million to 85.4 million during the calendar year 2023. This growth highlights the increasing participation of retail investors in the stock market.. Key drivers for this market are: Automating all processes, Regulatory Landscape. Potential restraints include: Automating all processes, Regulatory Landscape. Notable trends are: Increasing Stock Exchanges Index affecting Capital Market Exchange Ecosystem.

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 statistic shows the annual growth of Philippine Stock Exchange' (PSE) stock market regular accounts in the Philippines from 2014 to 2018. In 2018, there was approximately ***** thousand stock market regular accounts in the Philippine Stock Exchange (PSE).

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Can we predict stock market using machine learning? (WY Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Actual value and historical data chart for Denmark Stock Market Return Percent Year On Year

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

FTSE 100: Where to Next?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Rolling Stock Market size was valued at USD 67.12 billion in 2023 and is projected to reach USD 102.94 billion by 2032, exhibiting a CAGR of 6.3 % during the forecasts period. Recent developments include: In February 2023, Stadler Rail AG partnered with ASPIRE Engineering Research Centre and the Utah State University, for the construction of a passenger train powered by batteries centered on the FLIRT Akku idea. The development, construction, and testing of a FLIRT Akku battery-operated two-car multi-unit are all included in the project's scope. During subsequent test runs, the trio will focus on delivering insights for American passenger transit decarburization using battery-powered trains. , In February 2023, Stadler Rail AG announced the acquisition of BBR Verkehrstechnik GmbH, a railroad company, and its group businesses to increase its internal expertise in the digitalization and signaling technology fields. By joining forces, the companies will be able to offer advanced signaling solutions that will enhance and shape the digitization of the rail industry. , In January 2023, Siemens Mobility partnered with the Indian Railways, wherein it received a purchase order for 1,200 locomotives with 9,000 HP, making it the single largest locomotive order in the history of Siemens Mobility and Siemens India. The trains will be designed, developed, assembled, and put through testing by Siemens Mobility. The contract covers 35 years of full-service maintenance, and the deliveries are scheduled over an 11-year period. The trains will be assembled at the Indian Railways facility in Gujarat, India. , In November 2022, Siemens Mobility announced the construction of a train bogies factory in Aurangabad, India. The new plant can fill a single export order with more than 200 bogies. These rail bogies were produced by Siemens using the SF30 Combino Plus global design idea. The factory has a flexible manufacturing facility to meet domestic and overseas rolling stock demand. It can produce bogies for locomotives, coaches, trams, metros, and various electric vehicles. .

Perforated Core Muffler Tube Stock Market Outlook

According to our latest research, the global Perforated Core Muffler Tube Stock market size reached USD 1.42 billion in 2024, reflecting a dynamic industry driven by innovations in automotive and industrial noise reduction solutions. The market is set to grow at a robust CAGR of 5.6% during the forecast period, with the market size anticipated to reach USD 2.32 billion by 2033. This upward trajectory is fueled by increasing environmental regulations, heightened consumer demand for quieter vehicles, and the continuous expansion of industrial and aerospace sectors globally.

One of the key growth factors propelling the Perforated Core Muffler Tube Stock market is the tightening of global emission and noise control standards, particularly in the automotive sector. Governments worldwide are mandating stricter noise and emission limits, compelling automakers and industrial manufacturers to adopt advanced muffler technologies. The rising adoption of perforated core muffler tubes, known for their superior acoustic performance and durability, is a direct response to these regulatory pressures. Moreover, as electric vehicles (EVs) and hybrid vehicles continue to penetrate the market, there is a growing need for innovative muffler solutions that support quieter operation without compromising performance, thereby further accelerating market demand.

Technological advancements in material science and manufacturing processes are also significantly contributing to the market’s growth. The development of high-strength, corrosion-resistant materials such as stainless steel and advanced aluminum alloys has enhanced the longevity and effectiveness of perforated core muffler tubes. Additionally, the integration of automation and precision engineering in manufacturing has improved production efficiency and product consistency, reducing overall costs and making these solutions more accessible to a broader range of end-users. The trend towards lightweight components, driven by the automotive industry's focus on fuel efficiency and emissions reduction, is further stimulating the adoption of innovative muffler tube stock materials.

Another critical growth driver is the burgeoning demand from emerging economies, particularly in Asia Pacific and Latin America. Rapid industrialization, urbanization, and increasing vehicle ownership rates in these regions are generating significant opportunities for manufacturers and suppliers of perforated core muffler tube stock. The expansion of the aftermarket segment, with consumers seeking to upgrade or replace muffler systems for enhanced performance and compliance, is also contributing to sustained market growth. Furthermore, the aerospace and industrial sectors are increasingly utilizing advanced muffler tube technologies to address stringent noise abatement requirements, thus diversifying the market’s application landscape.

From a regional perspective, Asia Pacific remains the largest and fastest-growing market for Perforated Core Muffler Tube Stock, accounting for over 38% of the global market share in 2024. This dominance is attributed to the region's robust automotive manufacturing base, supportive government policies, and rising investments in infrastructure development. North America and Europe also represent significant markets, driven by mature automotive and industrial sectors, as well as strong regulatory frameworks. Meanwhile, Latin America and the Middle East & Africa are emerging as promising regions, supported by steady economic growth and increasing industrial activity. The interplay of these regional dynamics is expected to shape the competitive landscape and growth trajectory of the market in the coming years.

Material Type Analysis

The Material Type segment is a pivotal determinant in the Perforated Core Muffler Tube Stock market, as the choice of material directly influences product durability, acoustic performance, and cost-effectiveness. &

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.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

NZ Dow Jones Forecast: Gains Projected Amidst Global Uncertainty for Local Market Index

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data