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.

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.

France's main stock market index, the FR40, rose to 7743 points on August 8, 2025, gaining 0.44% from the previous session. Over the past month, the index has declined 1.72%, though it remains 6.51% higher than a year ago, 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 August of 2025.

United States US: Stocks Traded: Total Value data was reported at 39,785.881 USD bn in 2017. This records a decrease from the previous number of 42,071.330 USD bn for 2016. United States US: Stocks Traded: Total Value data is updated yearly, averaging 17,934.293 USD bn from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 47,245.496 USD bn in 2008 and a record low of 1,108.421 USD bn in 1984. United States US: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

As of Janaury 2025, the New York Stock Exchange (NYSE) and the Nasdaq - the two largest stock exchange operators in the United States - held a combined market capitalization for domestic listed companies of over ** trillion U.S. dollars. Both markets were almost evenly sized at this point in time - at approximately ** and ** trillion U.S. dollars, respectively. However, the Nasdaq has grown much quicker than the NYSE since January 2018, when their respective domestic market caps were ** and ** trillion U.S. dollars. Much of this can be attributed to the success of information technology stocks during the global coronavirus (COVID-19) pandemic, as the Nasdaq is the traditional venue for companies operating in the tech sector.

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.

In 2025, stock markets in the United States accounted for roughly ** percent of world stocks. The next largest country by stock market share was China, followed by the European Union as a whole. The New York Stock Exchange (NYSE) and the NASDAQ are the largest stock exchange operators worldwide. What is a stock exchange? The first modern publicly traded company was the Dutch East Industry Company, which sold shares to the general public to fund expeditions to Asia. Since then, groups of companies have formed exchanges in which brokers and dealers can come together and make transactions in one space. Stock market indices group companies trading on a given exchange, giving an idea of how they evolve in real time. Appeal of stock ownership Over half of adults in the United States are investing money in the stock market. Stocks are an attractive investment because the possible return is higher than offered by other financial instruments.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109AUSM293NNBR) from Jan 1897 to Sep 1916 about stock market, industry, price index, indexes, price, and USA.

Chart Industries stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

As of March 2025, the SSE Composite Index had closed at 3,335.75 points. The index reflects the performance of all stocks traded on the Shanghai Stock Exchange, including both boards, the main board, and the Star market. SSE still number one In the greater Chinese region, the stock exchange in Shanghai was the largest, beating the bourses in Shenzhen, Hong Kong, and Taiwan. In 2023, the Shanghai Stock Exchange recorded a market capitalization of over 6.5 trillion. Not only market capitalization was a unique attribute, but the Shanghai Stock Exchange was also home to the most valuable stock in mainland China, which was the baijiu producer Moutai Kweichow. Limited access Despite its size, the exchange in Shanghai only grants limited access to overseas investors. The bourse listed A-shares and B-shares. While A-shares are denominated in yuan and almost exclusively available for domestic traders, the prices of B-shares are in U.S. dollars and available for overseas investors as well. In addition, the bourse offers access to foreign investors through a trading accreditation which is supervised by the Chinese authorities. However, these tight controls are the reason why Hong Kong, despite its lower relative market capitalization, remains an important gateway to capital for mainland Chinese companies.

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.

(GTLS) Chart Industries: Poised for Growth in a Cooling World

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 USA: Value added by industry as percent of GDP: The latest value from 2021 is 17.61 percent, an increase from 17.27 percent in 2020. In comparison, the world average is 26.13 percent, based on data from 185 countries. Historically, the average for the USA from 1997 to 2021 is 20.06 percent. The minimum value, 17.27 percent, was reached in 2020 while the maximum of 23.13 percent was recorded in 1997.

Turkey: Value added by industry as percent of GDP: The latest value from 2024 is 25.94 percent, a decline from 28.38 percent in 2023. In comparison, the world average is 26.35 percent, based on data from 151 countries. Historically, the average for Turkey from 1960 to 2024 is 26.01 percent. The minimum value, 16.95 percent, was reached in 1963 while the maximum of 32.97 percent was recorded in 1989.

The Financial Times Stock Exchange 100 index (FTSE 100) is a share index of the 100 companies listed on the London Stock Exchange with the highest market capitalization. The index, which began in January 1984 with the base level of 1,000, reached ******** at the end of 2024. LSE Overview Established in 1571, the London Stock Exchange (LSE) has grown to become the ninth-largest globally. Companies listed on the LSE had a companies primarily hail from the energy and pharmaceutical sectors, with Shell and AstraZeneca leading the pack. In the realm of

Germany's main stock market index, the DE40, fell to 24013 points on August 12, 2025, losing 0.28% from the previous session. Over the past month, the index has declined 0.61%, though it remains 34.81% 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 August of 2025.

Stock Price Time Series for Volati AB. Volati AB (publ) is a private equity firm specializing in growth capital, buyouts, add on acquisitions in mature and middle market companies. The firm invests primarily in trading, consumer, and industry sectors. Within trading it invests in building materials sector, consumables, and material for construction (like concrete, cut stones & stone products), paper and forest products, industrials, home and garden, and agriculture and forestry farmers. Within consumer, it invests in database marketing, digitization, and e-commerce. Within industry it invests in international expansion, lean manufacturing, and HR, consumer discretionary, consumer staples, utilities. It prefers to invest in small to medium sized Nordic, European, companies mainly in Sweden. The firm invests up to SEK100 million ($99.92 million) in companies with enterprise values up to SEK1000 million ($999.20 million); revenues between SEK50 million ($49.96 million) to SEK1000 million ($999.20 million) and EBITDA up to SEK100 million ($99.92 million). It seeks majority/ownership control and prefers to invest in companies with strong cash flows and take a board seat in its portfolio companies. The firm invests out of balance sheet. Volati AB (publ) was founded in 2003 and is based in Stockholm, Sweden.

Brazil: Value added by industry as percent of GDP: The latest value from 2024 is 21.33 percent, a decline from 22.14 percent in 2023. In comparison, the world average is 26.35 percent, based on data from 151 countries. Historically, the average for Brazil from 1960 to 2024 is 20.16 percent. The minimum value, 0 percent, was reached in 1960 while the maximum of 46.64 percent was recorded in 1984.

Costa Rica: Value added by industry as percent of GDP: The latest value from 2024 is 19.71 percent, a decline from 20.46 percent in 2023. In comparison, the world average is 26.35 percent, based on data from 151 countries. Historically, the average for Costa Rica from 1960 to 2024 is 24.27 percent. The minimum value, 19.23 percent, was reached in 2019 while the maximum of 29.45 percent was recorded in 1984.

The global graph database market size reached USD 2.0 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 8.6 Billion by 2033, exhibiting a growth rate (CAGR) of 17.57% during 2025-2033. The increasing adoption of graph databases in cybersecurity for threat detection and network analysis, growing demand for real-time analytics and AI-driven insights, and expanding application in industries, such as healthcare and finance, for data integration and personalized services, are some of the key factors catalyzing the market growth.

IMARC Group provides an analysis of the key trends in each segment of the global graph database market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on component, type of database, analysis type, deployment model, application, and industry vertical.