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.

List of companies in the NASDAQ exchanges.

Data and documentation are available on NASDAQ's official webpage. Data is updated regularly on the FTP site.

The file used in this repository:

Notes:

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Content

Top Companies of NASDAQ 100 in 2022

Here are the top companies on the NASDAQ 100 index in 2022. NASDAQ 100 is one of the most prominent large-cap growth indices in the world.

Many companies listed in the NASDAQ 100 operate in the tech sector. That is why many investors who are focused investing in tech stocks also invest in NASDAQ index to grow their funds

What is NASDAQ 100?

NASDAQ 100 is a stock market index composed of the 100 largest and most actively traded companies in the United States of America in the non- financial sector and are segmented under technology, retail, industrial, biotechnology, health care, telecom, transportation, media and services sectors.

Acknowledgement

Data collected from Yahoo Finance.

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.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

1) Data Introduction • The Stock Market Dataset contains metadata on stocks and ETFs listed on NASDAQ, including attributes such as ticker symbol, company name, market classification, ETF status, start date, and last trading date.

2) Data Utilization (1) Characteristics of the Stock Market Dataset: • Since the dataset includes only static metadata without price data, it is well-suited for preprocessing and classification tasks such as stock filtering, sector labeling, and distinguishing between ETFs and regular stocks.

(2) Applications of the Stock Market Dataset: • Automated sector classification of stocks: This dataset can be used to automatically tag or analyze stocks by sector using text-based industry keywords.

This dataset contains a detailed information on companies listed in the NASDAQ exchanges. The dataset also includes the market category as well as the financial status of the listed companies.

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

Stock Market Analysis of Nasdaq 100 Stock from it's Founding / Listing Years which is 2001 to 2022

Data Dictionary

About Nasdaq 100 Stock

The Nasdaq-100 is a stock market index made up of 102 equity securities issued by 101 of the largest non-financial companies listed on the Nasdaq stock exchange. It is a modified capitalization-weighted index. The stocks' weights in the index are based on their market capitalizations, with certain rules capping the influence of the largest components. It is limited to companies from a single exchange, and it does not have any financial companies. The financial companies are in a separate index, the NASDAQ Financial-100.

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About this Dataset

The dataset consists of companies listed in the S&P500, stock market index that measures the stock performance of 500 large companies listed on stock exchanges in the United State. The S&P500 or SPX is the most commonly followed equity index.

Category

Markets

Keywords

s&p500,nyse,nasdaq,stock market,equities

Row Count

505

Price

$20.00

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.

United States US: Number of Listed Domestic Companies: Total data was reported at 4,336.000 Unit in 2017. This records an increase from the previous number of 4,331.000 Unit for 2016. United States US: Number of Listed Domestic Companies: Total data is updated yearly, averaging 5,930.000 Unit from Dec 1980 (Median) to 2017, with 38 observations. The data reached an all-time high of 8,090.000 Unit in 1996 and a record low of 4,102.000 Unit in 2012. United States US: Number of Listed Domestic Companies: Total 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. Listed domestic companies, including foreign companies which are exclusively listed, are those which have shares listed on an exchange at the end of the year. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status, are excluded. A company with several classes of shares is counted once. Only companies admitted to listing on the exchange are included.; ; 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.

Sweden's main stock market index, the Stockholm 30, fell to 2782 points on December 2, 2025, losing 0.11% from the previous session. Over the past month, the index has climbed 0.95% and is up 8.08% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

this group contains a list of listed companies in Amman stock exchange and their sector , .symbol, code , market and number of shares .

Content

This dataset provides the latest Nasdaq stock ticker information for all companies listed on Nasdaq in 2025. It includes key financial and industry-specific attributes, making it a valuable resource for investors, analysts, and researchers.

The dataset contains 6,975 unique stock tickers, representing companies across various sectors, market capitalizations, and industries. The data is available in multiple formats to suit different needs, whether for stock screening, financial modeling, or market research.

Columns:

Name – Full name of the company Symbol – Stock ticker symbol Sector – General sector classification Industry – Specific industry category Market Cap – Market capitalization (in USD) Country – Country where the company is headquartered Date – The date the data was last updated

About this Dataset

Daily stock market price information for all U.S. publicly traded firms.

Category

Markets

Keywords

stocks,prices,nyse,nasdaq,S&P500

Row Count

65501

Price

$1499.99

This dataset contains firm-level, daily observations of capital flow activity for approximately 2,000 publicly traded companies listed on the NASDAQ, Russell 3000, and S&P 500 indices between 2015 and 2025. Each record corresponds to one trading day per firm and reports the following variables: date – trading day in YYYY-MM-DD format buy_flow_usd – total value of buy-side transactions (USD) sell_flow_usd – total value of sell-side transactions (USD) net_flow_usd – difference between buy and sell flows (positive = inflow; negative = outflow) trades – number of executed trades during the trading day The data were constructed from aggregated trade-level information using intraday transaction records obtained from official exchange data feeds. All flows are expressed in nominal USD. This dataset can be used to study market liquidity, institutional trading behavior, investor sentiment, and capital movement patterns across major U.S. indices. It supports replication of research on daily capital flows, market microstructure, and cross-sectional determinants of liquidity.

List of companies in the NYSE, and other exchanges.

Data and documentation are available on NASDAQ's official webpage. Data is updated regularly on the FTP site.

The file used in this repository: ...

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.