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.

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.

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

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.

Prices for United States Stock Market Index (US5000) including live quotes, historical charts and news. United States Stock Market Index (US5000) was last updated by Trading Economics this December 2 of 2025.

This dataset contains several daily features of NASDAQ Composite, Dow Jones Industrial Average, and NYSE Composite from 2010 to 2024. It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of major companies in the U.S. market, and treasury bill rates. Sources and thorough description of features have been mentioned in the paper of "CNNpred: CNN-based stock market prediction using a diverse set of variables" published at Expert Systems with Applications. This dataset has been used in "SAMBA: A Graph-Mamba Approach for Stock Price Prediction" published at ICASSP 2025. Link to Code: https://github.com/Ali-Meh619/SAMBA

Graph and download economic data for Index of Stock Prices (General) for Germany (M1123BDEM334NNBR) from Jan 1924 to Dec 1935 about stock market, Germany, and indexes.

Netflix, Inc. is an American subscription streaming service and production company founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California. Initially, Netflix started as a DVD rental service, pioneering the model of online rentals with no late fees. In 2007, the company transitioned into streaming media, revolutionizing the entertainment industry by offering a vast library of movies and TV shows accessible on-demand. Netflix further expanded its influence by producing original content, beginning with the series "**House of Cards**" in 2013. Today, Netflix is a global powerhouse in entertainment, with over 200 million subscribers worldwide and a diverse portfolio of acclaimed original series, films, and documentaries.

This dataset provides a comprehensive record of Netflix's stock price changes over time. It includes essential columns such as the date, opening price, highest price of the day, lowest price of the day, closing price, adjusted closing price, and trading volume.

This data is invaluable for conducting historical analyses, forecasting future stock performance, and understanding market trends related to Netflix's stock.

📁 Dataset 3: Microsoft Corp. (MSFT)

💻 Microsoft Corp. (MSFT) Stock Price Data – Last 5 Years

Description:
This dataset contains daily historical stock price data for Microsoft Corporation (Ticker: MSFT) over the past 5 years. It is sourced from reliable financial market data providers and is well-suited for:

• 📈 Time series forecasting and trend analysis
• 📊 Financial dashboards and visualizations
• 🧠 Machine learning applications in finance
• 💼 Investment research and strategy backtesting

Each entry corresponds to a single trading day and includes various price indicators and trading volume.

Columns Included:

• Date: The calendar date of the trading session (YYYY-MM-DD).
• Open: The price of the stock at the beginning of the trading day.
• High: The highest price reached during the trading day.
• Low: The lowest price recorded during the trading day.
• Close: The price of the stock at the end of the trading session.
• Adj Close: The closing price adjusted for splits and dividend distributions.
• Volume: The total number of shares traded during that day.

🔍 Beginner-Friendly Analysis Techniques:

If you're new to data analysis or finance, here are some simple but powerful techniques you can apply:

• Line Plots: Use line charts to visualize how the stock price has changed over time.
• Moving Averages: Calculate simple or exponential moving averages to identify trends and smooth out short-term fluctuations.
• Daily Returns: Compute percentage change between closing prices to analyze daily gains or losses.
• Volatility Analysis: Use rolling standard deviation to understand how volatile the stock has been.
• Volume Trends: Track trading volume to identify periods of high market activity.
• Candlestick Charts (intermediate): Create candlestick charts to represent daily open-high-low-close patterns visually.

Use Cases:
This dataset can be used to evaluate stock performance trends, calculate technical indicators, simulate investment strategies, or train predictive models on financial data.

U.S. - Railroad Stock Prices - Historical chart and current data through 1937.

London Stock Exchange stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Results of ANOVA analysis of the difference in accuracy between stock price predictions using image characteristics.

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

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109BUSM293NNBR) from Dec 1914 to Dec 1968 about stock market, industry, price index, indexes, price, and USA.

Macy's stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Experimental parameter settings.

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

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

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

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