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.

This statistic shows the stock price development of selected companies in the technology industry from January 6, 2020 to February 3, 2025. During this period, stock prices of most of the tech companies have increased. Out of all companies shown here, stock values of **** saw the most substantial increase between January and October 2020. In February 3, 2025, ***** stock prices increased more than others with over an increase of *** index points.

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

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.

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

The NYSE U.S. Market Technology Sector Index tracks the performance of the U.S. domiciled equity components listed on the U.S. stock exchanges that offer goods and services in the technology sector. The statistics shows the monthly development of the NYSE U.S. Market Technology Sector Index from December 2015 to June 2023. During that time, the index increased steadily until December 2021. Since then, the NYSE U.S. Market Technology Index fluctuated and stood at ******** as of June 2023.

China Index: Shanghai Stock Exchange: Information Technology data was reported at 3,796.940 31Dec2003=1000 in 14 May 2025. This records an increase from the previous number of 3,769.150 31Dec2003=1000 for 13 May 2025. China Index: Shanghai Stock Exchange: Information Technology data is updated daily, averaging 3,291.878 31Dec2003=1000 from Jan 2005 (Median) to 14 May 2025, with 4943 observations. The data reached an all-time high of 8,470.330 31Dec2003=1000 in 03 Jun 2015 and a record low of 1,360.048 31Dec2003=1000 in 03 Dec 2012. China Index: Shanghai Stock Exchange: Information Technology data remains active status in CEIC and is reported by China Securities Index Co., Ltd.. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: China Securities Index : Daily.

View quarterly updates and historical trends for S&P 500 Information Technology Earnings Per Share. from United States. Source: Standard and Poor's. Track…

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.

This statistic presents the returns of the S&P 500 Information Technology Index in the United States from 2007 to 2023. The IT sector had its worst year in 2008, where it lost **** percent of its value. After three years of value gain, it lost **** percent of its value in 2022. On the contrary, 2023 witnessed the second-highest value gain during this period, reaching **** percent.

Graph and download economic data for Nasdaq US Small Cap Technology Index (NASDAQNQUSS10) from 2011-05-23 to 2025-09-23 about small cap, market cap, NASDAQ, indexes, and USA.

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.

The Dow Jones U.S. Technology Capped index is predicted to continue its upward trend, driven by continued growth in the technology sector. However, there are downside risks to consider, including rising interest rates, a global economic slowdown, and increased regulatory scrutiny.

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 Poised for Moderate Growth, Analysts Predict 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

Graph and download economic data for NASDAQ-100 Technology Sector Market-Cap Weighted Notional Net Total Return Index (NASDAQNDXTMCNR) from 2022-03-21 to 2025-09-22 about return, NASDAQ, sector, Net, 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 the Tech Index Reach New Heights?

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 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.

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.

Description

Tweets were collectect between April 9 and July 16, 2020 using not only the SPX500 tag but also the top 25 companies in the index and "#stocks". 1300 tweets were manually classified and reviewed. The proposed specialised dictionary is also present in the data of this contribution. All the source code used to download tweets, check the top words and evaluate the sentiment are present.

Content

- ID -> Contains id used for the tweet. - Date and time -> Date and time when the tweet was tweeted. - Tweet -> Tweet/text written by the user. - Sentiment -> Wheter the tweet was postive or negative.

Inspiration

Currently exploring nlp and learning more about it, found this very easy to use dataset for the topic and now sharing with other fellow kaggelers.

Notebook and a task is missing for the dataset hence it shows as 9.4 usability.

Acknowledgements

Cite - Bruno Taborda, Ana de Almeida, José Carlos Dias, Fernando Batista, Ricardo Ribeiro, April 15, 2021, "Stock Market Tweets Data", IEEE Dataport, doi: https://dx.doi.org/10.21227/g8vy-5w61.

I do not own this dataset, thanks to the authors for creating this dataset. Please cite if using the dataset.

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 the Tech Index Continue its Ascent?

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