A complete list of live websites using the Yahoo! Web Analytics technology, compiled through global website indexing conducted by WebTechSurvey.

Traffic analytics, rankings, and competitive metrics for yahoo.com as of August 2025

yahoo.com is ranked #6 in US with 3.94B Traffic. Categories: Finance, Online Services. Learn more about website traffic, market share, and more!

Only askers who provided complete data are represented here. Data are stratified by inquired weight. (N = 3,926).aAges 20 and up; adult BMI classifications: [44].bAges 13–19; teen BMI classifications: [34], [57].

Analysis of ‘Yahoo Finance Apple Inc. (AAPL)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/achintyatripathi/yahoo-finance-apple-inc-aapl on 27 August 2021.

--- Dataset description provided by original source is as follows ---

Context

This is Historical Data which contains data that tells the onening and closing price of the market. The highest and lowest points and also tells about VWAP . It have data of one whole year, which is divided into 3 parts, 1.Daily updates 2. Weekly updates, 3. Monthly Updates.

Inspiration

The idea came from whether we can actually predict what will be the opening or closing price of the market, or what will be the higgest and lowest price of the market.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

--- Original source retains full ownership of the source dataset ---

Context

We have historical stock data for Yahoo Finance. The stock dates back to as old as 1986 and is very recent until July 2020.

Content

There is a raw file containing the entire datasets. The training data has stock information from 2014 till 2019 and the test data contains everything beyond that. (Till July 2020). The raw file can be used to create your own test and training set as per your model requirements.

Source

Yahoo's Public API

Inspiration

This can be a very a great way to get into some time series analysis. I've used the datasets in one of my own kernel. Please have a look here: https://www.kaggle.com/jvdahemad/yahoo-stock-prediction-using-lstm and leave you feedback. Thanks

Background

Social media opinion has become a medium to quickly access large, valuable, and rich details of information on any subject matter within a short period. Twitter being a social microblog site, generate over 330 million tweets monthly across different countries. Analysing trending topics on Twitter presents opportunities to extract meaningful insight into different opinions on various issues.

Aim

This study aims to gain insights into the trending yahoo-yahoo topic on Twitter using content analysis of selected historical tweets.

Methodology

The widgets and workflow engine in the Orange Data mining toolbox were employed for all the text mining tasks. 5500 tweets were collected from Twitter using the “yahoo yahoo” hashtag. The corpus was pre-processed using a pre-trained tweet tokenizer, Valence Aware Dictionary for Sentiment Reasoning (VADER) was used for the sentiment and opinion mining, Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) was used for topic modelling. In contrast, Multidimensional scaling (MDS) was used to visualize the modelled topics.

Results

Results showed that "yahoo" appeared in the corpus 9555 times, 175 unique tweets were returned after duplicate removal. Contrary to expectation, Spain had the highest number of participants tweeting on the 'yahoo yahoo' topic within the period. The result of Vader sentiment analysis returned 35.85%, 24.53%, 15.09%, and 24.53%, negative, neutral, no-zone, and positive sentiment tweets, respectively. The word yahoo was highly representative of the LDA topics 1, 3, 4, 6, and LSI topic 1.

Conclusion

It can be concluded that emojis are even more representative of the sentiments in tweets faster than the textual contents. Also, despite popular belief, a significant number of youths regard cybercrime as a detriment to society.

Explore how Yahoo Finance serves as a key resource for tracking soybeans, offering real-time analytics, historical insights, and expert commentary on the global soybean market's trends, supply chain dynamics, and economic impact.

https://www.logodesignlove.com/images/monograms/tesla-logo-01.jpg" alt="Tesla">

Context

Tesla, Inc. designs, develops, manufactures, leases, and sells electric vehicles, and energy generation and storage systems in the United States, China, and internationally. The company operates in two segments, Automotive, and Energy Generation and Storage. The Automotive segment offers electric vehicles, as well as sells automotive regulatory credits. It provides sedans and sport utility vehicles through direct and used vehicle sales, a network of Tesla Superchargers, and in-app upgrades; and purchase financing and leasing services. This segment is also involved in the provision of non-warranty after-sales vehicle services, sale of used vehicles, retail merchandise, and vehicle insurance, as well as the sale of products through its subsidiaries to third party customers; services for electric vehicles through its company-owned service locations, and Tesla mobile service technicians; and vehicle limited warranties and extended service plans. The Energy Generation and Storage segment engages in the design, manufacture, installation, sale, and leasing of solar energy generation and energy storage products, and related services to residential, commercial, and industrial customers and utilities through its website, stores, and galleries, as well as through a network of channel partners. This segment also offers service and repairs to its energy product customers, including under warranty; and various financing options to its solar customers. The company was formerly known as Tesla Motors, Inc. and changed its name to Tesla, Inc. in February 2017. Tesla, Inc. was founded in 2003 and is headquartered in Palo Alto, California.

Content

The dataset contains data from 29 Jun'10 to 14 Oct'21

Acknowledgements

Yahoo! Finance API..

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Ablation experiment results on Yahoo finance dataset.

RISA-Japan focus on Yahoo！Japan, one of the most active stock forums in Japan. Since 2012, RISA-Japan has meticulously collected, cleaned, and analyzed over 94 million posts related to more than 4,900 securities discussed within the stock forum.

By analyzing the discussions on the stock forum, RISA-Japan provides valuable information about the sentiments, opinions, and trends expressed by retail investors regarding various securities. This dataset provides information on the sentiment and attention (hotness) of retail investors on each stock on the Yahoo! Japan Forum.

In addition to statistical data, this data provides record-level post analyses and stock ratings by users, that allow clients to group the posts to gain in-deep insight, for example, identifying hot posts or grouping posts by stock ratings.

• Coverage: 4500+ Japanese stocks, 300+ ETFs • History: From 2012-11-20 • Update Frequency: Daily

Historical disruption data for Yahoo Mail

User mood and ratings for Yahoo Mail

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

📈 Adidas Stock Data Analysis

Welcome to the Adidas Stock Data Analysis project! This dataset contains comprehensive historical stock data for Adidas (Ticker: ADDYY) from 2006 to 2024, retrieved from Yahoo Finance. With this data, you can explore trends, perform detailed analysis, and make data-driven insights into the stock performance of one of the world’s leading sportswear brands.

🗂️ Dataset Overview

• Source: Yahoo Finance
• Ticker Symbol: ADDYY (Adidas on the OTC market)
• Date Range: May 31, 2006 – December 31, 2024
• Features Included:
  • 🕒 Date: Trading date
  • 💵 Open: Opening price of the stock
  • 📈 High: Highest price of the day
  • 📉 Low: Lowest price of the day
  • 🔒 Close: Closing price of the stock
  • 📊 Adj Close: Adjusted closing price (dividend and split-adjusted)
  • 📦 Volume: Number of shares traded

🔍 Key Use Cases

1. 📊 Trend Analysis

   • Examine long-term trends in Adidas stock prices.
   • Visualize price movements with moving averages (50-day, 100-day, 200-day).

2. 🛠️ Statistical Insights

   • Perform correlation analysis between features.
   • Evaluate volatility and average returns over time.

3. 📡 Predictive Modeling

   • Use the data for machine learning models like Linear Regression, Random Forest, or LSTM to predict future stock prices.

4. 🧮 Trading Strategies

   • Develop backtesting strategies using historical prices and volume data.

🌟 Visualization Ideas

• 📈 Line charts for price trends over time.
• 📊 Bar charts for trading volume comparison across years.
• 🖼️ Candlestick charts for daily price movement analysis.

🚀 Get Started Now!

Analyze the stock performance of Adidas with this clean and well-structured dataset. Whether you're a data analyst, machine learning enthusiast, or finance professional, this project is perfect for gaining insights and building predictive models.

Feel free to ⭐️ this project if you find it useful and share your findings!

The Natural Language Processing (NLP) for Finance market is experiencing robust growth, driven by the increasing volume of unstructured financial data and the need for faster, more accurate insights. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 20% from 2025 to 2033, reaching approximately $60 billion by 2033. This surge is fueled by several key factors. The adoption of advanced NLP techniques like sentiment analysis, name entity recognition, and relationship extraction allows financial institutions to derive actionable intelligence from news articles, social media posts, financial reports, and customer communications. This enhanced data analysis improves decision-making across various applications, including risk management, fraud detection, regulatory compliance, and customer service. The rise of fintech and the increasing demand for personalized financial services further accelerate market growth. Furthermore, the growing adoption of cloud-based NLP solutions is reducing implementation costs and enabling wider accessibility for smaller financial institutions. Segments like sentiment analysis and KYC/AML compliance are witnessing particularly high growth due to their critical role in mitigating financial risks and enhancing regulatory compliance. However, challenges remain. Data security and privacy concerns, the need for high-quality training data, and the complexity of integrating NLP solutions into existing financial infrastructure represent significant restraints. Despite these challenges, the market's potential is undeniable. The ongoing advancements in AI and machine learning, coupled with the increasing availability of big data, are expected to overcome these hurdles, fueling further market expansion. The competitive landscape includes established players like Bloomberg, Yahoo Finance, Google Finance, and major financial institutions, alongside emerging fintech companies leveraging advanced NLP capabilities. Geographic growth is expected to be widespread, with North America and Europe leading initially, followed by rapid expansion in Asia-Pacific driven by the growth of fintech in regions like China and India.

The global app analytics tool market is estimated to reach XXX million by 2033, registering a CAGR of XX% from 2025 to 2033. Key market drivers include the increasing adoption of mobile and web applications, growing demand for data-driven insights, and the need for improved app performance. The market is segmented by type (mobile analytics, web analytics), application (marketing analytics, user analytics, app performance analytics), and region (North America, Europe, Asia Pacific). Prominent players in the market include Google, Yahoo, Adobe Systems Incorporated, Amazon Web Services, IBM Corporation, Teradata Corporation, Webtrends Corp, SAS Institute, Apptentive, Localytics, Appsee, and CleverTap. The market is highly competitive, with vendors offering a wide range of app analytics tools to cater to the specific needs of businesses. Strategic partnerships, acquisitions, and product innovations are common strategies adopted by vendors to gain market share and sustain their competitive position.

About Data

This data presents Apple stock prices from 1980-2021. Data is kept daily and presented in USD currency. Data has 8 numeric columns and 10273 rows.

Columns; - Date = Date - Open = Price from the first transaction of a trading day - High = Maximum price in given date - Low = Minimum price in given date - Close = Price of last transaction in given date - Volume = Volume in given date - Dividends = Share of Stocks - Stock Splits = Stock Splits

Source

This data was collected by Yahoo Finance's own Python API. I was able to easily get data in this way from the API I ran for testing purposes. If you want to investigate further and use the API, you can access the documentation and codes here.