Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this December 2 of 2025.

Context

This dataset consists of the daily stock prices and volume of 14 different tech companies, including Apple (AAPL), Amazon (AMZN), Alphabet (GOOGL), and Meta Platforms (META), Adobe (ADBE), Cisco Systems (CSCO), IBM, Intel Corporation (INTC), Netflix (NFLX), Tesla (TSLA), NVIDIA (NVDA), and more!

Note: All stock_symbols have 3271 prices, except META (2688) and TSLA (3148) because they were not publicly traded for part of the period examined.

Content

Geography: Worldwide

Time period: Jan 2010- Jan 2023

Unit of analysis: Big Tech Giants Stock Price Data

Variables

Acknowledgements

Datasource: Yahoo Finance Credit: Evan Gower

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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.

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

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Title: Stock Prices of 500 Biggest Companies by Market Cap (Last 5 Years)

Description: This dataset comprises historical stock market data extracted from Yahoo Finance, spanning a period of five years. It includes daily records of stock performance metrics for the top 500 companies based on market capitalization.

Attributes: 1. Date: The date corresponding to the recorded stock market data. 2. Open: The opening price of the stock on a given date. 3. High: The highest price of the stock reached during the trading day. 4. Low: The lowest price of the stock observed during the trading day. 5. Close: The closing price of the stock on a specific date. 6. Volume: The volume of shares traded on the given date. 7. Dividends: Any dividend payments made by the company on that date (if applicable). 8. Stock Splits: Information regarding any stock splits occurring on that date. 9. Company: Ticker symbol or identifier representing the respective company.

Usefulness: - Investors and analysts can leverage this dataset to conduct various analyses such as trend analysis, volatility assessment, and predictive modeling. - Researchers can explore correlations between stock prices of different companies, sector-wise performance, and market trends over the specified duration. - Machine learning enthusiasts can employ this dataset for developing predictive models for stock price forecasting or anomaly detection.

Note: Prior to using this dataset, it's recommended to perform data cleaning, handling missing values, and verifying the consistency of data across companies and time periods.

License: The dataset is sourced from Yahoo Finance and is provided for analytical purposes. Refer to Yahoo Finance's terms of use for further details on data usage and licensing.

This dataset consists of the daily stock prices and volume of 14 different tech companies, including Apple (AAPL), Amazon (AMZN), Alphabet (GOOGL), and Meta Platforms (META) and more!

There are 14 CSV files in the data/ folder named with the stock symbol for each of the 14 companies.

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

Stock Price Time Series for Beijing Zhidemai Technology. Beijing Zhidemai Technology Co., Ltd. engages in the Internet marketing and data service-related businesses in China and internationally. It also involved in Internet information promotion activities. In addition, The company provides consumer content, and content creation services. Beijing Zhidemai Technology Co., Ltd. was founded in 2011 and is headquartered in Beijing, China.

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

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

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

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Stock Price Time Series for CETC Digital Technology Co Ltd. CETC Digital Technology Co.,Ltd. provides software and information technology services in China. It operates through the Digital Products, Industry Digitalization, and Digital New Infrastructure segments. The company provides software and hardware products, including embedded computers, signal processing, high-speed network switching, data recording storage, and information processing products, which are used in radar communications, manufacturing, industrial control, rail transportation, civil aviation, financial technology, and other industries, as well as for general units and enterprises in aviation, aerospace, shipbuilding, electronics, electricity, and transportation industries. It offers digital intelligence application software; digital infrastructure solutions and products for banks, securities, insurance, internet finance, and regulators; infrastructure and ICT solutions for domestic telecom operators and foreign operators; and network and data center infrastructure for internet companies. In addition, the company offers digital solutions, such as smart production lines, wireless warehousing, industrial control, and smart retail for commercial and manufacturing enterprises; and urban governance, digital water conservancy, digital transportation, smart medical care, and digital energy for party, government, and public services, as well as consulting and design, general contracting management, engineering construction, operation and maintenance support, industry testing, and other services in the fields of data centers and building intelligence. The company was formerly known as Shanghai East-China Computer Co.,Ltd. The company was founded in 1993 and is based in Shanghai, China.

Stock Price Time Series for Genbyte Technology Inc. Genbyte Technology Inc. manufactures and sells controllers for use in household appliances, industrial inverters, and power supply and automotive products in China. It offers intelligent controllers, variable frequency drives, digital power supplies, intelligent IoT modules, energy storage systems, and inverters. The company was founded in 1999 and is based in Shenzhen, China.

Stock Price Time Series for GSI Technology Inc. GSI Technology, Inc. designs, develops, and markets semiconductor memory solutions for networking, industrial, test equipment, medical, aerospace, and military customers in the United States, China, Singapore, Germany, the Netherlands, and internationally. It offers associative processing unit products, which focuses on applications using similarity search in visual search queries for e-commerce, computer vision, drug discovery, cyber security, and service markets. The company also provides static random-access memory (SRAM) products, including SyncBurst, NBT, SigmaQuad, and SigmaDDR. In addition, it offers radiation-hardened and radiation-tolerant SRAMs for military/defense and aerospace applications, such as networking satellites and missiles. The company's products are used as components in the original equipment manufacturer customers' products, including routers, switches, and other networking and telecommunications products; military and aerospace applications, including radar and guidance systems and satellites; test and measurement applications; automotive applications comprising smart cruise control; medical applications, such as ultrasound and CAT scan equipment; and audio/video processing. The company markets its products through a network of independent sales representatives and distributors. GSI Technology, Inc. was incorporated in 1995 and is headquartered in Sunnyvale, California.

Stock Price Time Series for ZJBC Information Technology Co Ltd. ZJBC Information Technology Co., Ltd, through its subsidiaries, primarily provides information intelligent transmission services in China. The company also offers communication network maintenance services, such as operation management, fault repair, and routine maintenance for network resources owned by communication operators and tower companies. In addition, it provides business process outsourcing services for financial institutions, including banks. The company was formerly known as Maoye Communication and Network Co., Ltd. ZJBC Information Technology Co., Ltd was founded in 1997 and is based in Qinhuangdao, China.

Stock Price Time Series for Uber Technologies Inc. Uber Technologies, Inc. develops and operates proprietary technology applications in the United States, Canada, Latin America, Europe, the Middle East, Africa, and the Asia Pacific. It operates through three segments: Mobility, Delivery, and Freight. The Mobility segment connects consumers with a range of transportation modalities, such as ridesharing, carsharing, micromobility, rentals, public transit, taxis, and other modalities; and offers riders in a variety of vehicle types, as well as financial partnerships products and advertising services. The Delivery segment allows consumers to search for and discover restaurants to grocery, alcohol, convenience, and other retails, as well as order a meal or other items, and either pick-up at the restaurant or have it delivered; and provides Uber direct, a white-label delivery-as-a-service for retailers and restaurants, as well as advertising services. The Freight segment manages transportation and logistics network, which connects shippers and carriers in digital marketplace, including carriers upfronts, pricing, and shipment booking; and offers on-demand platform to automate logistics end-to-end transactions for small-and medium-sized business to global enterprises. The company was formerly known as Ubercab, Inc. and changed its name to Uber Technologies, Inc. in February 2011. Uber Technologies, Inc. was founded in 2009 and is headquartered in San Francisco, California.

Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis