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 16 of 2025.

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 15 of 2025.

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 ****** billion U.S. dollars in 2014 to **** 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 ** to ** 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.

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

The main stock market index of United States, the US500, rose to 6695 points on October 16, 2025, gaining 0.37% from the previous session. Over the past month, the index has climbed 1.44% and is up 14.62% 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 year 2025 has seen significant stock market volatility, with many of the world's largest companies experiencing substantial year-to-date losses. Tesla, Inc. has been hit particularly hard, with a **** percent decline as of April 10, 2025. Even tech giants like Apple and Microsoft have not been immune, seeing losses of ***** percent and **** percent respectively. Tech giants maintain market dominance despite losses Despite the recent stock price declines, technology companies continue to lead in market capitalization. Microsoft, Apple, NVIDIA, Amazon, and Alphabet (Google) remain among the few companies with market caps exceeding ************ U.S. dollars. This dominance reflects their long-term growth and influence in the global economy, even as they face short-term challenges in the stock market. Market volatility reflects broader economic concerns The current stock market losses are reminiscent of past periods of economic uncertainty. In 2020, the COVID-19 pandemic caused severe market turbulence, with the Dow Jones Industrial Average dropping around ***** points in just four weeks. While the market has since recovered and reached new highs, the current downturn suggests ongoing economic concerns. Investors are likely reacting to various factors, including inflation, geopolitical tensions, and potential shifts in consumer behavior.

Graph and download economic data for NASDAQ 100 Index (NASDAQ100) from 1986-01-02 to 2025-10-14 about NASDAQ, stock market, indexes, and USA.

Dataset extracted from the post NASDAQ 100 | 14th Oct 2025 | FAANG – TECH Stocks | Technicals, Price Targets and Geopolitical Effects on Smart Investello.

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.

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

New York Stock Exchange: Index: Dow Jones US Large Cap Technology Index data was reported at 6,045.360 NA in Apr 2025. This records an increase from the previous number of 6,005.530 NA for Mar 2025. New York Stock Exchange: Index: Dow Jones US Large Cap Technology Index data is updated monthly, averaging 6,432.785 NA from Mar 2024 (Median) to Apr 2025, with 14 observations. The data reached an all-time high of 6,918.630 NA in Dec 2024 and a record low of 5,424.010 NA in Apr 2024. New York Stock Exchange: Index: Dow Jones US Large Cap 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.

In this paper, we study the impact of momentum, volume and investor sentiment on U.S. tech sector stock returns using Principal Component Analysis-Hidden Markov Model (PCA-HMM) methodology. Price and volume are two well-known aspects in general equilibrium model. Momentum effect arises from the determination of prices in the market equilibrium. By studying momentum, volume and investor sentiment, we intuitively connect theoretical finance model with modern behavior finance topic. Instead of predicting future stock returns using machine learning models and doing comparisons, we apply the PCA-HMM method to reveal the hidden force in the financial and macroeconomic time series to calibrate different regimes. Combining the traditional financial study methods with modern machine learning techniques, we show investor sentiment effect show the primary effect on tech sector stock return which outweighs volume effect and momentum effect. The volume effect also has ineligible impact on stock return. The investor sentiment effect and volume effect show most impact on tech stocks with large or medium market shares. In contrast, momentum effect has very trivial correlation with tech sector stock return, from both stock level and individual state level. We also discuss the underlying mechanisms behind above findings according to tech sectors’ unique characteristics, as well as raise risk management concerns. Using such PCA-HMM method, we reveal the unique patterns in tech sector stock returns. The PCA-HMM method can especially help us to identify those edge cases under which market behaves irregularly.

US stock market update: Tech-led sell-off continues, Nasdaq drops 0.7% as AI stocks like Nvidia decline. Investors digest Target earnings and Fed minutes ahead of Powell's key Jackson Hole speech.

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 62 trillion U.S. dollars. Both markets were almost evenly sized at this point in time - at approximately 32 and 30 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 23 and 11 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.

Summary statistics for large tech stocks close price.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-10-14 about composite, NASDAQ, stock market, indexes, and USA.

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.

Context

Alphabet Inc. is a listed US holding company of the former Google LLC, which continues to exist as a subsidiary. The headquarters is Mountain View in Silicon Valley. The company is led by Sundar Pichai as CEO.

With sales of $137 billion, a profit of $30.7 billion and a market value of $ 863.2 billion, Alphabet Inc. ranks 17th among the world's largest companies according to Forbes Global 2000 (as of 4th November 2019). The company had a market cap of $ 766.4 billion in early 2018. In 2019, Alphabet had annual sales of $161.9 billion and an annual profit of $34.3 billion.

Market capitalization of Alphabet (Google) (GOOG)

Market cap: $2.442 Trillion USD

As of August 2025 Alphabet (Google) has a market cap of $2.442 Trillion USD. This makes Alphabet (Google) the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Content

Geography: USA

Time period: August 2004- August 2025

Unit of analysis: Google Stock Data 2025

Variables

Acknowledgements

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

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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.

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