Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this July 21 of 2025.

Investors believed the stock price of two large U.S. tech companies in particular would grow by between 2020 and 2025. According to a survey conducted in ************, Tesla especially was believed to witness a stock growth. Nearly half of all respondents selected Tesla, close to double the number of respondents who selected the next-most popular option, Amazon. The source used a large definition of "tech", as the survey included companies that are active in different categories.

Prices for US Tech Composite Index including live quotes, historical charts and news. US Tech Composite Index was last updated by Trading Economics this July 22 of 2025.

Graph and download economic data for NASDAQ 100 Index (NASDAQ100) from 1986-01-02 to 2025-07-18 about NASDAQ, stock market, indexes, and USA.

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 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.

Tech Sector Outlook: Bullish Trend Expected for Dow Jones U.S. Technology Index.

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 main stock market index of United States, the US500, rose to 6313 points on July 21, 2025, gaining 0.25% from the previous session. Over the past month, the index has climbed 4.77% and is up 13.45% compared to the same time last year, 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 July of 2025.

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.

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.

Tech Titans' Next Frontier?

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

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.

New York Stock Exchange: Index: Dow Jones US Technology Index data was reported at 6,168.380 NA in Apr 2025. This records an increase from the previous number of 6,085.580 NA for Mar 2025. New York Stock Exchange: Index: Dow Jones US Technology Index data is updated monthly, averaging 2,065.140 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 6,979.640 NA in Dec 2024 and a record low of 788.170 NA in Aug 2013. New York Stock Exchange: Index: Dow Jones US Technology Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Dow Jones: Monthly.

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 annual returns of the Nasdaq 100 Index from 1986 to 2024. fluctuated significantly throughout the period considered. The Nasdaq 100 index saw its lowest performance in 2008, with a return rate of ****** percent, while the largest returns were registered in 1999, at ****** percent. As of June 11, 2024, the rate of return of Nasdaq 100 Index stood at ** percent. The Nasdaq 100 is a stock market index comprised of the 100 largest and most actively traded non-financial companies listed on the Nasdaq stock exchange. How has the Nasdaq 100 evolved over years? The Nasdaq 100, which was previously heavily influenced by tech companies during the dot-com boom, has undergone significant diversification. Today, it represents a broader range of high-growth, non-financial companies across sectors like consumer services and healthcare, reflecting the evolving landscape of the global economy. The annual development of the Nasdaq 100 recently has generally been positive, except for 2022, when the NASDAQ experienced a decline due to worries about escalating inflation, interest rates, and regulatory challenges. What are the leading companies on Nasdaq 100? In August 2023, ***** was the largest company on the Nasdaq 100, with a market capitalization of **** trillion euros. Also, ****************************************** were among the five leading companies included in the index. Market capitalization is one of the most common ways of measuring how big a company is in the financial markets. It is calculated by multiplying the total number of outstanding shares by the current market price.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-07-18 about NASDAQ, composite, stock market, indexes, and USA.

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.

Will Tech Remain the King? The Dow Jones U.S. Technology Index

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

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this July 21 of 2025.

Prices for United States Stock Market Index (US500) including live quotes, historical charts and news. United States Stock Market Index (US500) was last updated by Trading Economics this July 19 of 2025.

The Nasdaq 100 Index tracks the largest domestic and international non-financial companies listed on the Nasdaq Stock Exchange. This statistic shows the leading 20 companies on the Nasdaq 100 Index by market capitalization. As of January 2025, the American technology company ***** ranked first, with a market capitalization of over ***** trillion euros. This was followed by ******, with a market capitalization amounting to *** trillion euros.

Context

Stock market data -- and particularly intraday price data -- can be very expensive to buy. To help more people gain access to it, here I provide daily as well as intraday price and volume data for all U.S.-based stocks and ETFs trading on the NYSE, NASDAQ, and NYSE MKT.

Content

The dataset (last updated 12/06/2017) is presented in CSV format as follows:

• Intraday data: Date,Time,Open,High,Low,Close,Volume,OpenInt

• Daily data: Date,Open,High,Low,Close,Volume,OpenInt

Acknowledgements

The dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

Inspiration

Many have tried, but most have failed, to predict the stock market's ups and downs. Can you do any better?

China's main stock market index, the SHANGHAI, rose to 3534 points on July 18, 2025, gaining 0.50% from the previous session. Over the past month, the index has climbed 5.13% and is up 18.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.