As of May 23, 2025, Microsoft was the leading tech company by market capitalization globally at 3.38 trillion U.S. dollars. Nvidia ranked second at 3.24 trillion U.S. dollars. Tech company stocks were impacted through 2025 as a result of various global tariff threats by the United States government. Apple among the leaders Since its foundation in a Californian garage in 1976, Apple has expanded massively, becoming one of the most valuable companies in the world. The company started its origins in the PC industry with the Macintosh, but soon entered other segments of the consumer electronics market. Today, the iPhone is the most popular Apple product, although Mac, iPad, wearables, and services also contribute to its high revenues. Aiming at innovation, Apple invests every year in research and development, spanning a wide array of technologies from AI through to extended reality. Nvidia's immense growth With a focus that began with origins in gaming, Nvidia's business strategy has been transformed by demand from data centers that sit at the heart of the AI boom. The company's chips have been favored to support in the training and running of a range of large language models, most notably in the development of OpenAI's ChatGPT.
This statistic presents a ranking of the market capitalization of selected U.S. tech and internet companies in 2006, and from 2014 to 2020. Apple's market cap soared from 461.41 billion U.S. dollars in 2014 to 1.53 trillion dollars in 2020. Apple's market cap pushed the company ahead of last year's leader Microsoft.
Public offerings of tech and internet companies
A public offering is the offering of securities of a company or a similar corporation to the public. Generally, the securities are to be listed on a stock exchange. The initial public offering (IPO) of a company occurs when a company offers its shares for the first time for public ownership and trading.
Hardware companies such as Apple or IBM have been traded publicly for a while but younger, online-based companies such as Google or most notably Facebook and most recently, Snap Inc. have been generating a lot of buzz surrounding their IPOs and subsequent stock prices. Facebook’s initial public offering was intensely hyped over months with projections of a 100 billion US dollar valuation but it dwindled down to a range of 60 to 75 billion US dollars prior to the listing.
Other tech stock performances have been more stable – both online retailer Amazon and search and digital advertising giant Google’s - now Alphabet's - shares have been on a more upwards trend. The most impressive development however came from Apple which totally changed its stock performance after the 2008 introduction of the iPhone. Since then, the company has been catapulted to the top of the smartphone market, multiplying its market capitalization as well as regularly being ranked as one of the most valuable brands worldwide.
Between March 2018 and June 2024, the total market capitalization of technology companies listed on the London Stock Exchange (LSE) fluctuated from a high of almost *** billion British pounds in August 2021 to a low of ****** billion British pounds in December 2023.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Technology One reported AUD11.99B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for Technology One | TNE - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
By [source]
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
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
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
- 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
&g...
Between ********* and ************* (the figures refer to the last trading day of the month), the market capitalization of Italian companies active in the technology sector listed on the Milan Stock Exchange experienced an overall considerable increase. In fact, it started at around *** billion euros as of ********* and closed at around *** billion euros as of *************. Market capitalization of companies in this sector peaked in *************, when it reached over **** billion euros.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Clean Energy Technologies market cap as of June 08, 2025 is $0.02B. Clean Energy Technologies market cap history and chart from 2013 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Tetra Technologies market cap as of June 22, 2025 is $0.33B. Tetra Technologies market cap history and chart from 2010 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
TAT Technologies market cap as of June 18, 2025 is $0.36B. TAT Technologies market cap history and chart from 2010 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Sri Lanka CSE: Market Capitalization: Equities: Information Technology data was reported at 583.931 LKR mn in Oct 2018. This records a decrease from the previous number of 596.145 LKR mn for Sep 2018. Sri Lanka CSE: Market Capitalization: Equities: Information Technology data is updated monthly, averaging 602.251 LKR mn from Sep 2002 (Median) to Oct 2018, with 194 observations. The data reached an all-time high of 6,697.000 LKR mn in Jan 2011 and a record low of 70.000 LKR mn in May 2004. Sri Lanka CSE: Market Capitalization: Equities: Information Technology data remains active status in CEIC and is reported by Colombo Stock Exchange. The data is categorized under Global Database’s Sri Lanka – Table LK.Z005: Colombo Stock Exchange: Market Capitalization.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
China Market Cap: Shanghai SE: Information and Technology data was reported at 3,284,567.000 RMB mn in Mar 2025. This stayed constant from the previous number of 3,284,567.000 RMB mn for Feb 2025. China Market Cap: Shanghai SE: Information and Technology data is updated monthly, averaging 429,800.000 RMB mn from Apr 2001 (Median) to Mar 2025, with 287 observations. The data reached an all-time high of 3,284,567.000 RMB mn in Mar 2025 and a record low of 114,441.291 RMB mn in Sep 2002. China Market Cap: Shanghai SE: Information and Technology data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shanghai Stock Exchange: Market Capitalization.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Next Technology Holding market cap as of June 22, 2025 is $0.12B. Next Technology Holding market cap history and chart from 2020 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
ASE Technology Holding market cap as of June 18, 2025 is $17.84B. ASE Technology Holding market cap history and chart from 2010 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.
https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Ispire Technology market cap as of June 28, 2025 is $0.17B. Ispire Technology market cap history and chart from 2023 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Euro Tech Holdings market cap as of June 30, 2025 is $0.01B. Euro Tech Holdings market cap history and chart from 2010 to 2024. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Horizon Technology Finance market cap as of June 26, 2025 is $0.34B. Horizon Technology Finance market cap history and chart from 2010 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.
https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Microchip Technology reported $40.16B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for Microchip Technology | MCHP - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
China Market Cap: Shenzhen SE: ChiNext: Information and Technology data was reported at 1,704,576.057 RMB mn in Mar 2025. This records a decrease from the previous number of 1,800,864.403 RMB mn for Feb 2025. China Market Cap: Shenzhen SE: ChiNext: Information and Technology data is updated monthly, averaging 993,472.185 RMB mn from Nov 2009 (Median) to Mar 2025, with 185 observations. The data reached an all-time high of 1,800,864.403 RMB mn in Feb 2025 and a record low of 33,979.522 RMB mn in Nov 2009. China Market Cap: Shenzhen SE: ChiNext: Information and Technology data remains active status in CEIC and is reported by Shenzhen Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shenzhen Stock Exchange: ChiNext: Market Capitalization.
As of May 23, 2025, Microsoft was the leading tech company by market capitalization globally at 3.38 trillion U.S. dollars. Nvidia ranked second at 3.24 trillion U.S. dollars. Tech company stocks were impacted through 2025 as a result of various global tariff threats by the United States government. Apple among the leaders Since its foundation in a Californian garage in 1976, Apple has expanded massively, becoming one of the most valuable companies in the world. The company started its origins in the PC industry with the Macintosh, but soon entered other segments of the consumer electronics market. Today, the iPhone is the most popular Apple product, although Mac, iPad, wearables, and services also contribute to its high revenues. Aiming at innovation, Apple invests every year in research and development, spanning a wide array of technologies from AI through to extended reality. Nvidia's immense growth With a focus that began with origins in gaming, Nvidia's business strategy has been transformed by demand from data centers that sit at the heart of the AI boom. The company's chips have been favored to support in the training and running of a range of large language models, most notably in the development of OpenAI's ChatGPT.