NASDAQ Listed Companies Dataset

Description:

This dataset provides comprehensive information on companies listed on the NASDAQ stock exchange. It includes essential details about each company, making it a valuable resource for financial analysis, stock market research, and investment strategies.

Features:

• symbol: The unique ticker symbol used to identify the company's stock on the NASDAQ exchange.
• name: The full name of the company.
• currency: The currency in which the company's stock is traded.
• exchange: The stock exchange where the company is listed (in this case, NASDAQ).
• mic_code: The Market Identifier Code (MIC) for the NASDAQ exchange.
• country: The country where the company is headquartered.
• type: The type of company, such as common stock or preferred stock.
• Usage: This dataset can be used for various purposes including:

Stock Market Analysis:

Analyze stock symbols, company names, and market data.

Financial Modeling:

Incorporate company details into financial models and investment strategies.

Market Research:

Understand the distribution of companies by country and currency.

Data Visualization:

Create visualizations of the NASDAQ market landscape.

Data Source:

The data is sourced from the Twelve Data API, which provides up-to-date financial and stock market information.

Notes: The dataset includes only NASDAQ-listed companies and does not cover other exchanges. Ensure to comply with any data usage policies or licensing agreements associated with the data source. Feel free to adapt the description based on the specific details and attributes of your dataset.

NASDAQ (National Association of Securities Dealers Automated Quotation) is the world's second largest automated and electronic stock exchange and securities market in the United States, the first being the New York Stock Exchange, with more than 8,000 companies and corporations. It has more trading volume per hour than any other stock exchange in the world. More than 7,000 small and mid-cap stocks are traded on the NASDAQ. It is characterized by comprising high-tech companies in electronics, computers, telecommunications, biotechnology, and many others.

This dataset was created as a result of an automatic extraction of open & public data available in nasdaq.com, using web scraping techniques. The only purpose of creating it was for academic reasons

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.

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.

Context

Predicting the stock market is one of the most commonly performed projects when someone is learning about ML and Data Science. After all, who wouldn't want to delegate the task of picking stocks to a model and reap the rewards for themselves? However, one of the most difficult and tedious steps to predict what stocks to invest in is actually gathering the data to use. There are so many options and it is important to get sufficient information for each. But, what if you can skip this step and just download a dataset that has all that information easily available for you? Look no further as this is the answer to this problem.

Content

This dataset contains information of 4447 stocks traded under Nasdaq across various exchanges. There is a file that contains information for all 4447 stocks but also has several null fields, which is why I labeled it as full_financial_stocks_raw.csv --it has minimal modifications to the values inside the rows. The second file, dividend_stocks_only.csv, is still a raw-ish style dataset but it only contains stocks that pay out dividends to its shareholders. Interestingly, it seems dividend-paying stocks have more information about them, which explains why this file has significantly fewer rows with null values.

Update: In the next 24 hours, I will be uploading an optimized, feature-engineered dataset that has fewer columns overall and fewer rows with null values. This dataset is intended to be a fully cleaned option to directly feed into ML/DL models.

Acknowledgements

I would like to thank the sources where I obtained my data, which are the FTP Nasdaq Trader website and the Yahoo Finance API.

Inspiration

Analyzing the stock market is one of the most intriguing endeavors I could think of as the ways it can be influenced are so broad and distinct from one another. A news article can influence how investors view a particular company, social media can directly fluctuate a company's share price, and there are numerous calculations and formulas that can show what stocks are worth investing in.

This dataset is about stocks. It has 17 rows and is filtered where the exchange is NASDAQ Sector Indices. It features 8 columns including stock name, company, exchange, and exchange symbol.

Comprehensive dataset of 63 companies listed on Nasdaq Baltic, including detailed financial information, market data, and corporate filings. This dataset provides real-time updates on trading metrics, company profiles, financial statements, regulatory filings, and market performance indicators. Updated every 30 minutes, it covers key data points such as market capitalization, trading volume, stock prices, company fundamentals, and regulatory compliance information for all listed securities on Nasdaq Baltic.

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.

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.

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

Experience a decade of NASDAQ market dynamics with this comprehensive historical price dataset from 2014 to 2024.

The NASDAQ Composite is a benchmark index representing the performance of more than 2,500 stocks listed on the NASDAQ stock exchange, encompassing various sectors including technology, healthcare, and finance. This dataset, sourced meticulously from Yahoo Finance, offers daily insights into the index's opening, highest, lowest, and closing prices, along with adjusted close prices and daily volume.

Comprehensive dataset of 14 companies listed on Nasdaq, including detailed financial information, market data, and corporate filings. This dataset provides real-time updates on trading metrics, company profiles, financial statements, regulatory filings, and market performance indicators. Updated every 30 minutes, it covers key data points such as market capitalization, trading volume, stock prices, company fundamentals, and regulatory compliance information for all listed securities on Nasdaq.

Comprehensive dataset of 28 companies listed on Nasdaq Iceland, including detailed financial information, market data, and corporate filings. This dataset provides real-time updates on trading metrics, company profiles, financial statements, regulatory filings, and market performance indicators. Updated every 30 minutes, it covers key data points such as market capitalization, trading volume, stock prices, company fundamentals, and regulatory compliance information for all listed securities on Nasdaq Iceland.

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:

...

Comprehensive dataset of 132 companies listed on Nasdaq Copenhagen, including detailed financial information, market data, and corporate filings. This dataset provides real-time updates on trading metrics, company profiles, financial statements, regulatory filings, and market performance indicators. Updated every 30 minutes, it covers key data points such as market capitalization, trading volume, stock prices, company fundamentals, and regulatory compliance information for all listed securities on Nasdaq Copenhagen.

Comprehensive dataset of 140 companies listed on Nasdaq Helsinki, including detailed financial information, market data, and corporate filings. This dataset provides real-time updates on trading metrics, company profiles, financial statements, regulatory filings, and market performance indicators. Updated every 30 minutes, it covers key data points such as market capitalization, trading volume, stock prices, company fundamentals, and regulatory compliance information for all listed securities on Nasdaq Helsinki.

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.

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

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The 100 traded companies included in the NASDAQ-100 index with their relative ticker.

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.