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.

Inflation: Friend or Foe to the Stock Market?

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

This dataset contains historical stock price data for Tesla, Inc. (TSLA) starting from its IPO date, June 29, 2010, to January 1, 2025. The dataset includes daily records of Tesla's stock performance on the NASDAQ stock exchange. It is ideal for time-series analysis, stock price prediction, and understanding the long-term performance of Tesla in the stock market.

The dataset consists of the following columns:

1. Date: The trading date.
2. Open: Opening stock price on the given date.
3. High: The highest stock price during the trading day.
4. Low: The lowest stock price during the trading day.
5. Close: The closing stock price for the day.
6. Adj Close: Adjusted closing price (corrected for dividends and stock splits).
7. Volume: The number of shares traded during the day.

Use Cases of Tesla Stock Historical Data

1. Time-Series Analysis

   • Analyze trends in Tesla's stock prices over time.
   • Identify seasonality, volatility, and long-term patterns in Tesla’s performance.

2. Stock Price Prediction

   • Develop predictive models to forecast future stock prices using techniques such as ARIMA, LSTMs, or regression.

3. Investment Strategy Evaluation

   • Backtest trading strategies by simulating trades based on historical price movements.
   • Analyze returns of investment strategies such as moving averages, RSI, or Bollinger Bands.

4. Market Sentiment Analysis

   • Correlate Tesla’s stock performance with news sentiment, earnings reports, and market events.

5. Portfolio Diversification

   • Evaluate Tesla’s performance compared to other stocks or indices to assess its role in a diversified portfolio.

6. Risk Management

   • Calculate volatility, beta, and other risk metrics to assess the risk associated with investing in Tesla stock.

7. Economic and Market Studies

   • Study how macroeconomic indicators (like inflation, interest rates) influence Tesla’s stock price.
   • Analyze Tesla’s performance during major economic events such as the COVID-19 pandemic or policy changes.

8. Stock Splits and Adjustments Analysis

   • Examine the impact of Tesla’s stock splits on price and trading volume.

9. Educational Purposes

   • Serve as a dataset for academic projects, coursework, or tutorials on financial data analysis.

10. Correlation with Sector Trends

    • Compare Tesla’s stock performance with other automotive or renewable energy companies.

11. Data Visualization and Dashboarding

    • Create dashboards using tools like Tableau, Power BI, or Python libraries to visualize Tesla’s stock performance metrics.

12. A/B Testing for Financial Applications

    • Use historical stock data for controlled experiments in finance-related applications to improve decision-making tools.

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.

This stock market dataset is designed for financial analysis and predictive modeling. It includes historical stock prices, technical indicators, macroeconomic factors, and sentiment scores to help in developing and testing machine learning models for stock trend prediction.

Dataset Features: Column Description Stock Random stock ticker (AAPL, GOOG, etc.) Date Random business date Open Open price High High price Low Low price Close Close price Volume Trading volume SMA_10 10-day Simple Moving Average RSI Relative Strength Index (10-90 range) MACD MACD indicator (-5 to 5) Bollinger_Upper Upper Bollinger Band Bollinger_Lower Lower Bollinger Band GDP_Growth Random GDP growth rate (2.5% to 3.5%) Inflation_Rate Inflation rate (1.5% to 3.0%) Interest_Rate Interest rate (0.5% to 5.0%) Sentiment_Score Random sentiment score (-1 to 1) Next_Close Next day's closing price (for regression) Target Binary classification (1: Price Increase, 0: Price Decrease)

Key Features: Stock Prices: Open, High, Low, Close, and Volume data. Technical Indicators: Simple Moving Average (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands. Macroeconomic Factors: Simulated GDP growth, inflation rate, and interest rates. Sentiment Scores: Randomized sentiment values between -1 and 1 to simulate market sentiment. Target Variables: Next-day close price (for regression) and price movement direction (for classification).

Context

The stock market has consistently proven to be a good place to invest in and save for the future. There are a lot of compelling reasons to invest in stocks. It can help in fighting inflation, create wealth, and also provides some tax benefits. Good steady returns on investments over a long period of time can also grow a lot more than seems possible. Also, thanks to the power of compound interest, the earlier one starts investing, the larger the corpus one can have for retirement. Overall, investing in stocks can help meet life's financial aspirations.

It is important to maintain a diversified portfolio when investing in stocks in order to maximise earnings under any market condition. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down. It is often easy to get lost in a sea of financial metrics to analyze while determining the worth of a stock, and doing the same for a multitude of stocks to identify the right picks for an individual can be a tedious task. By doing a cluster analysis, one can identify stocks that exhibit similar characteristics and ones which exhibit minimum correlation. This will help investors better analyze stocks across different market segments and help protect against risks that could make the portfolio vulnerable to losses.

Objective

Trade&Ahead is a financial consultancy firm who provide their customers with personalized investment strategies. They have hired you as a Data Scientist and provided you with data comprising stock price and some financial indicators for a few companies listed under the New York Stock Exchange. They have assigned you the tasks of analyzing the data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group

Data Dictionary

Ticker Symbol: An abbreviation used to uniquely identify publicly traded shares of a particular stock on a particular stock market Company: Name of the company GICS Sector: The specific economic sector assigned to a company by the Global Industry Classification Standard (GICS) that best defines its business operations GICS Sub Industry: The specific sub-industry group assigned to a company by the Global Industry Classification Standard (GICS) that best defines its business operations Current Price: Current stock price in dollars Price Change: Percentage change in the stock price in 13 weeks Volatility: Standard deviation of the stock price over the past 13 weeks ROE: A measure of financial performance calculated by dividing net income by shareholders' equity (shareholders' equity is equal to a company's assets minus its debt) Cash Ratio: The ratio of a company's total reserves of cash and cash equivalents to its total current liabilities Net Cash Flow: The difference between a company's cash inflows and outflows (in dollars) Net Income: Revenues minus expenses, interest, and taxes (in dollars) Earnings Per Share: Company's net profit divided by the number of common shares it has outstanding (in dollars) Estimated Shares Outstanding: Company's stock currently held by all its shareholders P/E Ratio: Ratio of the company's current stock price to the earnings per share P/B Ratio: Ratio of the company's stock price per share by its book value per share (book value of a company is the net difference between that company's total assets and total liabilities)

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.

S&P 500: A Bull or a Bear?

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

Graph and download economic data for Producer Price Index by Industry: Investment Banking and Securities Intermediation: Brokerage Services, Equities and ETFs (PCU523120523120101) from Dec 1999 to Aug 2025 about ETF, brokers, stocks, equity, stock market, securities, services, PPI, industry, inflation, price index, indexes, price, and USA.

Stock prices for various companies are obtained from Google Finance through the utilization of the googlefinance() function, and are stored in an .xlsx file format. The stock data is classified and categorized into individual sheets, which correspond to a specific company. The table contains data for each day from the beginning of data collection up to March 2023, including the opening, high, low, and closing prices for the stock, as well as the volume of trades. The prices are denominated in the local currency of the respective country. Drive Stocks file link: https://docs.google.com/spreadsheets/d/1ElCXYXv-NjAmMKy7fQ1bjI05q1xij5hZ2DCLrJs0A5w/edit?usp=share_link

Alongside the stock data, two other files are used: the Inflation consumer prices (annual %) and the Wholesale price index (2010 = 100).

• The Wholesale price index is a measure of the average price of a basket of goods and services in a given economy, including both agricultural and industrial goods at various stages of production and distribution, and may also include import duties. The Laspeyres formula is typically used to calculate the index.

• The Inflation consumer prices (annual %) file measures the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services. The basket may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is typically used to calculate the index. Both of these files provide valuable context for understanding the performance of the stock market and the broader economic conditions that may be affecting it.

Wholesale price index and Inflation consumer prices are uncleared on propose. The cleaned version of the financial data is also included.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

The year 2025 has seen significant stock market volatility, with many of the world's largest companies experiencing substantial year-to-date losses. Tesla, Inc. has been hit particularly hard, with a **** percent decline as of April 10, 2025. Even tech giants like Apple and Microsoft have not been immune, seeing losses of ***** percent and **** percent respectively. Tech giants maintain market dominance despite losses Despite the recent stock price declines, technology companies continue to lead in market capitalization. Microsoft, Apple, NVIDIA, Amazon, and Alphabet (Google) remain among the few companies with market caps exceeding ************ U.S. dollars. This dominance reflects their long-term growth and influence in the global economy, even as they face short-term challenges in the stock market. Market volatility reflects broader economic concerns The current stock market losses are reminiscent of past periods of economic uncertainty. In 2020, the COVID-19 pandemic caused severe market turbulence, with the Dow Jones Industrial Average dropping around ***** points in just four weeks. While the market has since recovered and reached new highs, the current downturn suggests ongoing economic concerns. Investors are likely reacting to various factors, including inflation, geopolitical tensions, and potential shifts in consumer behavior.

This dataset is designed to support research and model development in financial market forecasting. It consists of daily stock market data for multiple companies, enriched with macroeconomic indicators and simulated market stress events to reflect real-world volatility.

Key features include:

Stock price details (Open, High, Low, Close) and Trading Volume

Macroeconomic indicators such as GDP growth rate, inflation rate, interest rate, and unemployment rate

A Market Stress Level (normalized between 0 and 1) indicating systemic volatility

A binary Event Flag to simulate major financial shocks or critical economic events

Data spans across multiple tickers (e.g., AAPL, GOOGL, TSLA) for 500+ trading days

The aim of this investigation is, to describe the development of the German Stock Market during the inter-war period. Causes for the so called change of the stock exchange functions are analysed. The author wants to make a contribution on special aspects of the economic history of the Weimar Republic and the following NS-regime. In his investigation the researcher analyses the activities of the involved players in a historical-institutional framework. The Study’s subjectIn the year 1890 the constitution of security exchange markets and stock markets has been the object of political debate and there has been discussed similar questions according to this topic in public and in policy as today. A current question is about the possibilities to boost the functionality of the security exchange and stock markets, not least in the face of Germany’s position in the global economy. In 1896 as a result of massive political conflicts a stock exchange act has arisen that disappointed the representatives of liberal trading interests because of the restriction of the stock market system’s autonomy and the prohibition of certain forms of trade. In 1908 an amendment to the stock exchange act has been adopted by the parliament. The stock market act in this new form has had validity until today. After the years of the hyperinflation deep changes of the stock market processes has been taken place. This changes can be described as a change of function. The economic-historical study at hand deals with the description of the development of the German security exchange markets during the interwar period. Reasons of the functional changes, which means mainly the decrease in importance, are analysed. In this context the primary investigator’s analysis contributes also to specific aspects of the economic history of the Weimar Republic and the Nazi empire. Due to a lack of date the needed statistical information concerning the period of interest is not available and therefore a statistical analysis cannot meet cliometric requirements. Therefore, the study’s concept is primary a desciptive one. On the basis of the quantitative information an identification of the functional change and the definition of stages of this process is made. The researcher tries to carve out the factors which have led to the functional change particularly during the period between 1924 and 1939. In this context the annual reports of banks, reports of the Chamber of Commerce and Industry, contributions of professional journals, and documents of authorities charged with the stock exchange market, are the empirical basis for the investigation. The researcher analyzed the effects of the banking sector’s concentration-process on the stock exchange market and assessed quantitatively the functional change. On the basis of the collected time series for the period of the late 19th century until 1939 the investigator analyzed the activities at the stock markets. First, the focus on interest is on the development of investments and securities issues. Then information on the securities turnover of German capital market before 1940 are given on the basis of an estimation procedure, developed by the researcher. The sepcial conditions during the inflation between 1914 and 1923 are discussed separately and the long term effects of this hyper-inflation on the stock exchange are identified. The effects of the taxation of stock exchange market visits and the high transaction costs are discussed, too. Used sources for the investigation have been:Archives of German Public Authorities:- finance ministry of the German Reich,- imperial chancellery- Reich´s ministry of economics- reference files of the German Reichsbank- Imperial commissioner of the stock market in Berlin Official Statistics, statistics of trade associations, chambers of commerce, enterprises, the press, and scientific publications. Finally, the author made estimates and calculations. The Study’s data:Data tables are accessible via the search- and download-system HISTAT unter the Topic ‘State: Finances and Taxes’ (= Staat: Finanzen und Steuern). The Study’s data are diveded into the following parts: A. Quantitative Indicators on the Change of Functions (Quantitative Indikatoren des Funktionswandels) A.1 Structure of floatation (Struktur der Wertpapieremission ausgewählter Zeitspannen (1901-1939).)A.2 Tax revenues of exchange turnover (Börsenumsatzsteueraufkommen (1885-1939).)A.3 Vergleich des unkorrigierten mit einem fiktiv möglichen Börsenumsatzsteueraufkommen (1906-1913).A.4 Estimation of everage tax rates (Geschätzte Durchschnittssteuersätze (1884-1913).)A.5 Amount of stock companies of the German Empire (Zahl der Aktiengesellschaften im Deutschen Reich zu bestimmten Jahren (1886-1939).)A.6 Shares listed on the Berlin stock exchange at the end of the year (Die zum Jahresende an der Berliner Börse notierten Aktien (1926-1939).)A.7 Reports und Lombards der Berliner Großbanken in ...

Brazil's main stock market index, the IBOVESPA, rose to 159976 points on December 2, 2025, gaining 0.86% from the previous session. Over the past month, the index has climbed 6.33% and is up 26.83% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Brazil. Brazil Stock Market (BOVESPA) - values, historical data, forecasts and news - updated on December of 2025.

Argentina's main stock market index, the Merval, rose to 3060289 points on December 1, 2025, gaining 1.12% from the previous session. Over the past month, the index has declined 1.42%, though it remains 33.32% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Argentina. Argentina Stock Market (MERVAL) - values, historical data, forecasts and news - updated on December of 2025.

The global stock market demonstrates a robust growth trajectory, poised for significant expansion in the coming decade. Projections indicate the market will surge from approximately $9.55 trillion in 2021 to over $23.85 trillion by 2033, expanding at a compound annual growth rate (CAGR) of 7.926%. This growth is underpinned by strong corporate earnings, technological advancements in trading, and increasing participation from retail investors. While North America currently dominates in terms of market size, the Asia-Pacific region is emerging as the fastest-growing hub, driven by the burgeoning economies of India and China. Factors such as monetary policies, geopolitical stability, and regulatory environments will continue to be pivotal in shaping regional market dynamics and overall global performance.

Key strategic insights from our comprehensive analysis reveal:

The Asia-Pacific region is the primary growth engine for the global stock market, exhibiting the highest CAGR of 9.112%, with nations like India and China leading this rapid expansion.
North America, particularly the United States, will maintain its position as the largest market by value, commanding a significant share of the global total, despite a slightly more moderate growth rate compared to APAC.
There is a consistent and broad-based growth trend across all major global regions, indicating widespread investor confidence and economic recovery, though the pace of expansion varies, highlighting diverse investment opportunities and risks.

Global Market Overview & Dynamics of Stock Market Analysis The global stock market is on a path of sustained and significant growth, driven by a confluence of economic, technological, and social factors. The market is forecast to expand from $9.55 trillion in 2021 to nearly $23.86 trillion by 2033. This expansion reflects growing global wealth, increased corporate profitability, and the continuous innovation in financial technologies that makes investing more accessible. However, this growth is not without its challenges, as markets must navigate through geopolitical tensions, inflationary pressures, and evolving regulatory landscapes that can introduce volatility and uncertainty.

Global Stock Market Drivers

Favorable Economic Conditions: Broad-based global GDP growth, coupled with supportive monetary policies from central banks in major economies, stimulates corporate investment and boosts earnings, attracting investors to equity markets.
Technological Innovation and Accessibility: The proliferation of online trading platforms, robo-advisors, and mobile investing apps has democratized access to stock markets, leading to a surge in retail investor participation.
Corporate Profitability and IPO Activity: Strong and resilient corporate earnings growth, along with a healthy pipeline of Initial Public Offerings (IPOs) from innovative companies, continually injects fresh capital and opportunities into the market.

Global Stock Market Trends

Rise of ESG Investing: There is a rapidly growing trend of investors integrating Environmental, Social, and Governance (ESG) criteria into their investment decisions, pushing companies to adopt more sustainable practices.
Increased Focus on Emerging Markets: Investors are increasingly allocating capital to emerging markets, particularly in the Asia-Pacific and South American regions, in pursuit of higher growth potential compared to more mature markets.
Growth of Passive Investing: The shift towards passive investment strategies, such as index funds and Exchange-Traded Funds (ETFs), continues to gain momentum due to their lower costs and broad market exposure.

Global Stock Market Restraints

Geopolitical Instability and Trade Disputes: International conflicts, trade wars, and political uncertainty can disrupt global supply chains, dampen investor sentiment, and lead to significant market volatility.
Inflation and Interest Rate Hikes: Persistent inflationary pressures force central banks to raise interest rates, which increases borrowing costs for companies and can make less risky assets like bonds more attractive relative to stocks.
Regulatory Scrutiny and Complexity: Stricter regulations on financial markets, data privacy, and corporate governance can increase compliance costs and limit certain market activities, potentially hindering growth.

Strategic Recommendations for Manufacturers

Prioritize market entry and expansion s...

Turkey's main stock market index, the BIST 100, rose to 11132 points on December 2, 2025, gaining 0.14% from the previous session. Over the past month, the index has climbed 0.64% and is up 13.27% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Turkey. Turkey Stock Market - values, historical data, forecasts and news - updated on December of 2025.

The DAX is a stock market index composed of the ** major German blue chip companies trading on the Frankfurt Stock Exchange. At the close of 2024, the DAX (Deutscher Aktienindex) closed at ********* points. This was the highest closing value of the observed period.What is the DAX index? The DAX is the most important stock index in Germany. It was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The DAX index is comprised of ** largest and most liquid German companies such as Deutsche Bank, Allianz or Bayer. These companies are traded on the Frankfurt Stock Exchange, which is the oldest exchange worldwide. The index can be viewed as a snapshot of the investment climate in Germany. What is not included in the DAX? Most notably, the DAX, like most indices, is not adjusted for inflation. While inflation has been relatively low in recent years, it might be useful to adjust the historic figures on the index when comparing historic data to current levels. This is particularly important for years when the index appears to increase by a few percentage points, because inflation may have increased at a more rapid rate than the stock prices.

Extensive and dependable pricing information spanning the entire range of financial markets. Encompassing worldwide coverage from stock exchanges, trading platforms, indicative contributed prices, assessed valuations, expert third-party sources, and our enhanced data offerings. User-friendly request-response, bulk access, and tailored desktop interfaces to meet nearly any organizational or application data need. Worldwide, real-time, delayed streaming, intraday updates, and meticulously curated end-of-day pricing information.

Index Time Series for iShares III Public Limited Company - iShares Global Inflation Linked Government Bond UCITS ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund is an exchange traded fund (ETF) that aims to track the performance of the Barclays World Government Inflation-Linked Bond Index as closely as possible. The ETF invests in physical index securities. The Barclays World Government Inflation-Linked Bond Index offers exposure to developed world government inflation-linked bonds issued in the domestic currency of each included country. Only capital-indexed bonds, linked to an eligible inflation index, with a minimum remaining time to maturity of one year are included in the index. iShares ETFs are funds managed by BlackRock. They are transparent, cost-efficient, liquid vehicles that trade on stock exchanges like normal securities. iShares ETFs offer flexible and easy access to a wide range of markets and asset classes.

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.