Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this October 4 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 October 4 of 2025.

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

As of July 16, 2025, Nvidia was the leading tech company by market capitalization globally at 4.16 trillion U.S. dollars. Nvidia became the first company to ever achieve the four trillion milestone, hitting this figure for the first time in July 2025. Microsoft ranked second at 3.76 trillion U.S. dollars. 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. Apple is also 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.

The main stock market index of United States, the US500, rose to 6718 points on October 3, 2025, gaining 0.04% from the previous session. Over the past month, the index has climbed 3.31% and is up 16.80% 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 October of 2025.

Graph and download economic data for NASDAQ 100 Index (NASDAQ100) from 1986-01-02 to 2025-10-03 about NASDAQ, 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

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 Tide Rising: Can Technology Stocks Ride the Wave?

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

A dataset of mentions, growth rate, and total volume of the keyphrase 'Tech Stocks' over time.

As of February 2025, ************************************************ was the leading technology company listed on the London Stock Exchange (LSE), in terms of market capitalization. The corporation was valued at around ** billion British pounds. It was followed in the ranking by ********** and *****************, which reached market values of ** and ***** billion British pounds, respectively.

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

The number of domestic companies listed on the Nasdaq and on the New York Stock Exchange (NYSE) has seen some fluctuations since 2018. As of the end of 2023, the NYSE had a total of ***** listed domestic and international companies, while the figure for the Nasdaq was much higher, standing at *****. Despite this, the NYSE has a higher market capitalization than the Nasdaq. What are the top listed companies? The NYSE has been a home for stable and long-lasting firms, also known as “blue-chip” companies. For example, Berkshire Hathaway, established in 1839, is the largest company traded on the NYSE. On the other hand, the Nasdaq has been known for listing major tech companies. For instance, Apple is the largest company listed on the Nasdaq. As of 2024, both companies were among the biggest companies in the world in terms of market capitalization. Which stock exchange has the most companies worldwide? Although the NYSE and the Nasdaq are the world’s largest two stock market operators by market capitalization, the Japan Exchange Group (JPX) is the biggest stock exchange in the world based on the number of companies. The JPX was created in 2013 through the merger of the Tokyo Stock Exchange and the Osaka Securities Exchange and is also one of the largest stock exchanges in the world based on total market capitalization.

Stock Price Time Series for Daou Tech. Daou Technology Inc., together with its subsidiaries, provides IT and finance services. The company offers marketing communication services, such as texting, business text message, mobile coupon, corporate mobile coupon, internet fax, and bulk mail; online commerce solutions, including escrow, 050 virtual number, and integrated management service; biz infra services comprising business platform, cloud, IDC, and domain services; and financial IT professional services. It also engages in the advertising services, real estate development, and building management. The company was founded in 1986 and is headquartered in Seongnam-si, South Korea

Japan's main stock market index, the JP225, rose to 45770 points on October 3, 2025, gaining 1.85% from the previous session. Over the past month, the index has climbed 7.49% and is up 18.46% compared to the same time last year, 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 October of 2025.

This dataset is about stocks. It has 1 row and is filtered where the company is TS TECH. It features 8 columns including stock name, company, exchange, and exchange symbol.

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.

Semtech (SMTC): Is the tech stock's rally sustainable?

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

Stock Price Time Series for Shanghai Dragonnet Tech. Shanghai DragonNet Technology Co.,Ltd. engages in the provision of information technology (IT) services, infrastructure localization, and smart industry application solutions in China and internationally. The company offers IT support and maintenance services, such as system environment check, equipment health check, technical support response, fault resolution, parts replacement, and other support services; IT outsourcing services comprising on-site, and remote and system hosting operation and maintenance services; and IT professional consulting and technical implementation services, including system consulting, design, evaluation, development, tuning, integration, upgrade, and relocation services, as well as equipment configuration, and other implementation services for data center IT infrastructure, industry users, and IT business application systems. It is also involved in the provision of IT software services, such as software and software modules development, as well as installation, debugging, testing, training, software developer outsourcing, etc.; and engages in the agency sale of third-party software, and hardware equipment and spare parts. In addition, the company offers PBData, a database all-in-one machine with cloud platform which helps enterprises in minimizing TCO and simplifying IT operation and maintenance; PriData, a hyper-converged all-in-one machine that provides real private cloud capabilities based on virtualization and IT resource delivery services; and PhegData, a distributed storage platform, for realizing product delivery based on domestically produced server chips and operating systems. Shanghai DragonNet Technology Co.,Ltd. was founded in 2001 and is headquartered in Shanghai, China.

This dataset is about stocks. It has 1 row and is filtered where the company is Holders Technology. It features 8 columns including stock name, company, exchange, and exchange symbol.