The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

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.

The Value Line Investment Survey is one of the oldest, continuously running investment advisory publications. Since 1955, the Survey has been published in multiple formats including print, loose-leaf, microfilm and microfiche. Data from 1997 to present is now available online. The Survey tracks 1700 stocks across 92 industry groups. It provides reported and projected measures of firm performance, proprietary rankings and analysis for each stock on a quarterly basis. This dataset, a subset of the Survey covering the years 1980-1989 has been digitized from the microfiche collection available at the Dewey Library (FICHE HG 4501.V26). It is only available to MIT students and faculty for academic research. Published weekly, each edition of the Survey has the following three parts: Summary & Index: includes an alphabetical listing of all industries with their relative ranking and the page number for detailed industry analysis. It also includes an alphabetical listing of all stocks in the publication with references to their location in Part 3, Ratings & Reports. Selection & Opinion: contains the latest economic and stock market commentary and advice along with one or more pages of research on interesting stocks or industries, and a variety of pertinent economic and stock market statistics. It also includes three model stock portfolios. Ratings & Reports: This is the core of the Value Line Investment Survey. Preceded by an industry report, each one-page stock report within that industry includes Timeliness, Safety and Technical rankings, 3-to 5-year analyst forecasts for stock prices, income and balance sheet items, up to 17 years of historical data, and Value Line analysts’ commentaries. The report also contains stock price charts, quarterly sales, earnings, and dividend information. Publication Schedule: Each edition of the Survey covers around 130 stocks in seven to eight industries on a preset sequential schedule so that all 1700 stocks are analyzed once every 13 weeks or each quarter. All editions are numbered 1-13 within each quarter. For example, in 1980, reports for Chrysler appear in edition 1 of each quarter on the following dates: January 4, 1980 – page 132 April 4, 1980 – page 133 July 4, 1980 – page 133 October 1, 1980 – page 133 Reports for Coca-Cola were published in edition 10 of each quarter on: March 7, 1980 – page 1514 June 6, 1980 – page 1518 Sept. 5, 1980 – page 1517 Dec. 5, 1980 – page 1548 Any significant news affecting a stock between quarters is covered in the supplementary reports that appear at the end of part 3, Ratings & Reports. File format: Digitized files within this dataset are in PDF format and are arranged by publication date within each compressed annual folder. How to Consult the Value Line Investment Survey: To find reports on a particular stock, consult the alphabetical listing of stocks in the Summary & Index part of the relevant weekly edition. Look for the page number just to the left of the company name and then use the table below to identify the edition where that page number appears. All editions within a given quarter are numbered 1-13 and follow equally sized page ranges for stock reports. The table provides page ranges for stock reports within editions 1-13 of 1980 Q1. It can be used to identify edition and page numbers for any quarter within a given year. Ratings & Reports Edition Pub. Date Pages 1 04-Jan-80 100-242 2 11-Jan-80 250-392 3 18-Jan-80 400-542 4 25-Jan-80 550-692 5 01-Feb-80 700-842 6 08-Feb-80 850-992 7 15-Feb-80 1000-1142 8 22-Feb-80 1150-1292 9 29-Feb-80 1300-1442 10 07-Mar-80 1450-1592 11 14-Mar-80 1600-1742 12 21-Mar-80 1750-1908 13 28-Mar-80 2000-2142 Another way to navigate to the Ratings & Reports part of an edition would be to look around page 50 within the PDF document. Note that the page numbers of the PDF will not match those within the publication.

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.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

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 2025, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the financial crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

This dataset contains the weekly closing prices of the NASDAQ Composite Index from 2016 to 2019, sourced using the yfinance library. The data provides a comprehensive view of the NASDAQ market over a four-year period, making it ideal for various financial analyses, including studying stock market trends, volatility, and correlations.

The primary motivation for collecting this dataset is to analyze stock market correlations using mutual information and graph theory. By understanding the relationships between different assets or market sectors, one can gain insights into market dynamics and potential investment strategies. This dataset serves as a valuable resource for financial analysts, data scientists, and researchers interested in exploring market behavior through advanced statistical and machine learning techniques.

France's main stock market index, the FR40, rose to 8121 points on December 2, 2025, gaining 0.29% from the previous session. Over the past month, the index has climbed 0.13% and is up 11.93% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on December of 2025.

These reports collect information on market activity from primary dealers in U.S. government securities. This family of reports consists of the Weekly Report of Dealer Positions (FR 2004A), the Weekly Report of Cumulative Dealer Transactions (FR 2004B), the Weekly Report of Dealer Financing and Fails (FR 2004C), the Weekly Report of Specific Issues (FR 2004SI), the Daily Report of Specific Issues (FR 2004SD), and the Daily Report of Dealer Activity in Treasury Financing (FR 2004WI). The FR 2004A collects weekly data on dealers' outright positions in Treasury and other marketable debt securities. The FR 2004B collects cumulative weekly data on the volume of transactions made by dealers in the same instruments for which positions are reported on the FR 2004A. The FR 2004C collects weekly data on the amounts of dealer financing and fails. The FR 2004SI collects weekly data on outright, financing, and fails positions in current or on-the-run issues. Under certain circumstances this information is also collected on a daily basis on the FR 2004SD for on-the-run and off-the-run securities. The FR 2004WI collects daily data on positions in to-be-issued Treasury coupon securities, mainly the trading on a when-issued delivery basis.

Key information about India Sensitive 30 (Sensex)

• India Sensitive 30 (Sensex) closed at 85,706.7 points in Nov 2025, compared with 83,938.7 points at the previous month end
• India Equity Market Index: Month End: BSE: Sensitive 30 (Sensex) data is updated monthly, available from Apr 1979 to Nov 2025, with an average number of 2,987.7 points
• The data reached an all-time high of 85,706.7 points in Nov 2025 and a record low of 115.6 points in Nov 1979

• In the latest reports, SENSEX recorded a daily P/E ratio of 23.2 in Dec 2025

Germany's main stock market index, the DE40, rose to 23722 points on December 2, 2025, gaining 0.56% from the previous session. Over the past month, the index has declined 1.70%, though it remains 18.51% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on December of 2025.

The weekly value of the DAX index amounted to 23,081.03 as of March 5, 2025. This is well above the value of 13,681.19 points recorded in the middle of February 2020, prior to the global coronavirus (COVID-19) pandemic. All global stock markets were hit by the growing panic about the coronavirus pandemic, which accounts for this drop. The DAX (Deutscher Aktienindex) is the most important German stock index, showing the value trends of the 30 largest and most liquid companies 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.

🚗📈 BYDDF Stock Data (2002–2025) – BYD Company Limited

🔗 Ticker Symbol: BYDDF (BYD Company Limited)
📆 Date Range: July 31, 2002 – March 16, 2025
📍 Industry: Electric Vehicles (EV), Batteries, Renewable Energy

📌 Dataset Overview

This dataset contains the historical stock price data for BYD Company Limited (BYDDF), one of the leading electric vehicle (EV) and battery manufacturers in the world. The data spans over 20+ years, covering the company's evolution from a battery producer to a global EV powerhouse.

📂 Features in This Dataset

• Date – Trading date
• Open Price – Opening price of the stock
• High Price – Highest price reached during the trading day
• Low Price – Lowest price during the trading day
• Close Price – Closing price at the end of the trading session
• Adjusted Close – Adjusted closing price considering dividends/splits
• Volume – Total number of shares traded on that day

🔥 Why Use This Dataset?

✅ Stock Market Analysis – Study BYDDF's historical price trends
✅ Time Series Forecasting – Build ML models to predict future stock prices
✅ EV Industry Insights – Analyze how market events impact BYD's stock
✅ Comparative Analysis – Compare with Tesla (TSLA), NIO, and other EV stocks

📈 Potential Use Cases

🔹 Stock Price Visualization – Plot BYDDF stock trends over the years
🔹 Moving Average Analysis – Identify support & resistance levels
🔹 Market Sentiment Study – Analyze how news/events impact stock price

💬 Let’s Discuss!

• How has BYDDF's stock performed compared to other EV makers?
• What market trends have influenced its stock movements?
• Can we predict future price movements using machine learning models?

Contect info:

You can contect me for more data sets

-E_mail

-Linkdin

-Kaggle

-X

-Github

🚀 Feel free to share your insights, notebooks, and visualizations!

🏷️ Tags

#BYDDF #StockMarket #EVStocks #Finance #StockPrediction #DataAnalysis #TimeSeries #MachineLearning

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

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United States Unemployment Rate Nowcast: sa: Contribution: Securities Yield: Treasury Bills Yield: Secondary Market: 4 Weeks data was reported at 0.000 % in 12 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. United States Unemployment Rate Nowcast: sa: Contribution: Securities Yield: Treasury Bills Yield: Secondary Market: 4 Weeks data is updated weekly, averaging 2.372 % from Jan 2020 (Median) to 12 May 2025, with 279 observations. The data reached an all-time high of 21.743 % in 31 May 2021 and a record low of 0.000 % in 12 May 2025. United States Unemployment Rate Nowcast: sa: Contribution: Securities Yield: Treasury Bills Yield: Secondary Market: 4 Weeks data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Unemployment Rate.

China Number of Investor: Integrated Account: Trade in Secondary Market in this Week: B Share data was reported at 31.900 Unit th in 03 Feb 2017. This records a decrease from the previous number of 42.100 Unit th for 20 Jan 2017. China Number of Investor: Integrated Account: Trade in Secondary Market in this Week: B Share data is updated daily, averaging 88.000 Unit th from May 2015 (Median) to 03 Feb 2017, with 89 observations. The data reached an all-time high of 351.700 Unit th in 17 Jul 2015 and a record low of 26.000 Unit th in 08 Jun 2016. China Number of Investor: Integrated Account: Trade in Secondary Market in this Week: B Share data remains active status in CEIC and is reported by China Securities Depository and Clearing Corporation Limited. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: China Securities Depository and Clearing: No of Investor Account: Weekly.

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Predictive AI In Stock Market Size 2025-2029

The predictive AI in stock market size is valued to increase by USD 1.63 billion, at a CAGR of 21.8% from 2024 to 2029. Increasing availability and integration of alternative data will drive the predictive AI in stock market.

Market Insights

North America dominated the market and accounted for a 33% growth during the 2025-2029.
By Component - Solution segment was valued at USD 329.80 billion in 2023
By Application - Algorithmic trading segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 445.64 million 
Market Future Opportunities 2024: USD 1632.20 million
CAGR from 2024 to 2029 : 21.8%

Market Summary

Predictive AI in the stock market refers to the application of artificial intelligence (AI) algorithms and techniques to analyze historical market data and make predictions about future trends. This technology has gained significant attention in recent years due to the increasing availability and integration of alternative data sources and the advancement of generative AI and large language models for qualitative alpha generation. One real-world business scenario where predictive AI is making a significant impact is in supply chain optimization. For instance, a manufacturing company can use predictive AI to forecast demand for its products based on historical sales data, economic indicators, and other external factors.
By accurately predicting demand, the company can optimize its inventory levels, reduce carrying costs, and improve operational efficiency. However, the adoption of predictive AI in the stock market also presents several challenges. Data quality and overfitting are major concerns, as historical data may not accurately reflect future market conditions. Market reflexivity, or the phenomenon where market participants' actions influence market trends, can also make it challenging to make accurate predictions. Despite these challenges, the potential benefits of predictive AI in the stock market are significant, including improved risk management, increased operational efficiency, and enhanced investment strategies.

What will be the size of the Predictive AI In Stock Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

Predictive AI in the stock market is an evolving technology that leverages advanced algorithms and real-time analytics to identify trends and patterns, enabling data-driven decision-making for businesses. One significant trend in this domain is the integration of demand sensing technology, which improves accuracy by reducing false positive and false negative rates. For instance, model performance can be enhanced through algorithm performance improvements, feature engineering techniques, and model retraining frequencies. In the realm of supply chain optimization, predictive AI-powered forecasting plays a pivotal role in inventory control strategies. By monitoring data in real-time, businesses can implement automated ordering systems, ensuring stockout prevention and minimizing excess inventory.
This approach not only improves precision and recall but also enables better risk mitigation planning and compliance with data privacy regulations. Scalability testing and data quality management are essential aspects of deploying predictive AI models in the stock market. Hyperparameter tuning and error rate reduction are critical for maintaining model performance, while system monitoring tools facilitate predictive maintenance and performance benchmarks. By adhering to data governance policies, businesses can ensure the reliability and accuracy of their predictive AI models, ultimately leading to improved business intelligence and strategic decision-making.

Unpacking the Predictive AI In Stock Market Landscape

The market management employs advanced clustering techniques and predictive modeling to minimize lead time variability and enhance production planning. By integrating real-time data processing and scalable infrastructure, businesses can achieve significant improvements in inventory optimization and order fulfillment prediction. For instance, predictive models trained on model training datasets have demonstrated a 20% increase in demand prediction accuracy compared to traditional methods. Data security protocols are essential to safeguard sensitive stock market data. Predictive AI systems employ machine learning models, deep learning algorithms, and neural network architecture for model evaluation and classification. These advanced techniques enable real-time anomaly detection and statistical process control, ensuring risk assessment metrics align with business objectives. Cloud-based infrastructure and process automation tools facilitate seamless data integration pipelines, allowing for efficient supply chain analytics and stock level monitoring. P

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.

An In-depth Analysis of the S&P 500 Index: Performance, Composition, and Implications

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