Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this September 23 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 September 22 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

In the first quarter of 2020, global stock indices posted substantial losses that were triggered by the outbreak of COVID-19. The period from March 6 to 18 was particularly dramatic, with several stock indices losing more than ** percent of their value. Worldwide panic hits markets From the United States to the United Kingdom, stock market indices suffered steep falls as the coronavirus pandemic created economic uncertainty. The Nasdaq 100 and S&P 500 are two indices that track company performance in the United States, and both lost value as lockdowns were introduced in the country. European markets also recorded significant slumps, which triggered panic selling among investors. The FTSE 100 – the leading share index of companies in the UK – plunged by as much as ** percent in the opening weeks of March 2020. Is it time to invest in tech stocks? The S&P 500 is regarded as the best representation of the U.S. economy because it includes more companies from the leading industries. However, helped in no small part by its focus on tech companies, the Nasdaq 100 has risen in popularity and seen remarkable growth in recent years. Global demand for digital technologies has increased further due to the coronavirus, with remote working and online shopping becoming part of the new normal. As a result, more investors are likely to switch to the tech stocks listed on the Nasdaq 100.

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

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

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.

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

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.

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.

Hong Kong's main stock market index, the HK50, fell to 26398 points on September 22, 2025, losing 0.55% from the previous session. Over the past month, the index has climbed 2.20% and is up 44.67% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on September of 2025.

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

The dataset consists of 5,001,460 daily Tweets crawled from Twitter's streaming API between April 1, 2011 and May 31, 2011, focusing on sentiment features related to four tech companies to predict their stock price movements.

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

This dataset is about stocks. It has 3 rows and is filtered where the company is GLG Life Tech. It features 8 columns including stock name, company, exchange, and exchange symbol.

Japan's main stock market index, the JP225, rose to 45481 points on September 22, 2025, gaining 0.97% from the previous session. Over the past month, the index has climbed 6.24% and is up 19.87% 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 September of 2025.

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.

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

This dataset is about stocks. It has 2 rows and is filtered where the company is Chanjet Information Technology. It features 8 columns including stock name, company, exchange, and exchange symbol.

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