Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

Yahoo Finance dataset provides information on top traded companies. It contains financial information on each company including stock ticker and risk scores and general company information such as company location and industry. Each record in the dataset is a unique stock, where multiple stocks can be related to the same company. Yahoo Finance dataset attributes include: company name, company ID, entity type, summary, stock ticker, currency, earnings, exchange, closing price, previous close, open, bid, ask, day range, week range, volume, and much more.

In January 2024, Yahoo! Search had a worldwide market share of 1.34 percent. The search engine is powered by Microsoft's Bing. Neither of these web search providers comes close to the dominance of market leader Google.

Explore the intricacies of wheat as a global commodity on Yahoo Finance, offering live price updates, historical data, and market insights. Discover how geopolitical events, weather conditions, and supply chain logistics influence wheat prices and affect various economic sectors. Stay informed with expert analyses and community discussions, providing comprehensive resources for both novice and seasoned investors in the agricultural markets.

################################################################################################## THE FILES HAVE BEEN CREATED BY SZILÁRD ERHART FOR A RESEARCH: ERHART (2021): ESG RATINGS OF GENERAL # STOCK EXCHANGE INDICES, INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS# USERS OF THE FILES AGREE TO QUOTE THE ABOVE PAPER# THE PYTHON SCRIPT (PYTHONESG_ERHART.TXT) HELPS USERS TO GET TICKERS BY STOCK EXCHANGES AND EXTRACT ESG SCORES FOR THE UNDERLYING STOCKS FROM YAHOO FINANCE.# THE R SCRIPT (ESG_UA.TXT) HELPS TO REPLICATE THE MONTE CARLO EXPERIMENT DETAILED IN THE STUDY.# THE EXPORT_ALL CSV CONTAINS THE DOWNLOADED ESG DATA (SCORES, CONTROVERSIES, ETC) ORGANIZED BY STOCKS AND EXCHANGES.############################################################################################################################################################################################################### DISCLAIMER # The author takes no responsibility for the timeliness, accuracy, completeness or quality of the information provided. # The author is in no event liable for damages of any kind incurred or suffered as a result of the use or non-use of the # information presented or the use of defective or incomplete information. # The contents are subject to confirmation and not binding. # The author expressly reserves the right to alter, amend, whole and in part, # without prior notice or to discontinue publication for a period of time or even completely. ###########################################################################################################################################READ ME############################################################# BEFORE USING THE MONTE CARLO SIMULATIONS SCRIPT: # (1) COPY THE goascores.csv and goalscores_alt.csv FILES ONTO YOUR ON COMPUTER DRIVE. THE TWO FILES ARE IDENTICAL.# (2) SET THE EXACT FILE LOCATION INFORMATION IN THE 'Read in data' SECTION OF THE MONTE CARLO SCRIPT AND FOR THE OUTPUT FILES AT THE END OF THE SCRIPT# (3) LOAD MISC TOOLS AND MATRIXSTATS IN YOUR R APPLICATION# (4) RUN THE CODE.####################################READ ME

This data set includes stock information for the companies Tesla, Porsche, Nio and Ferrari for each day from the date 11/08/2019 to 11/08/2020. Specifically, it shows information about the opening, closing, maximum and minimum price of the session, as well as the volume, the dividends granted to investors and the presence of stock splits generated per day. This dataste has been created with the aim to analyze how the quotes have been evolving during the COVID-19 pandemic in the automotive sector.

The AccionesSectorAutomovil.xlsx dataset contains 4 sheets (TESLA, PAH3.DE, NIO, RACE ) and 9 variables per sheet:

- Fecha: date in dd/MM/yyyy format
- Abrir: value of the share at the market opening expressed in US dollars (USD)
- Max: maximum value of the share throughout the day expressed in USD
- Cierre*: value of the share at the close of the market expressed in USD
- Cierre ajus.*: estimated share value at market close, expressed in USD.
- Volumen: the amount of a specific asset invested in during a day.
- Dividends: money received by shareholders in the form of dividends that day.
- Stock Splits: Whether or not a stock split operation was carried out that day.

For more information about the project visit the link on [Github](https://github.com/paulamlago/Financial-Web-Scrapping)

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.

Explore wheat prices on Yahoo Finance and understand the various factors influencing market trends, from global demand and weather conditions to government policies and financial analysis tools. Discover real-time data and insightful charting on the commodity's performance.

The dataset reports a collection of earnings call transcripts, the related stock prices, and the sector index In terms of volume, there is a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. Furthermore, the data collection was made possible by Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance enabled the search for stock values and Thomson Reuters Eikon provided the earnings call transcripts. Lastly, the dataset can be used as a benchmark for the evaluation of several NLP techniques to understand their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure. Contact at Tilburg University: Francesco Lelli

As of January 2025, online search engine Bing accounted for 12.23 percent of the global desktop search market, while market leader Google had a share of around 78.83 percent. Meanwhile, Yahoo's market share was 3.07 percent. Google in the global 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 2023, with a market capitalization of 1,6 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 2023 with roughly 305.6 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 were nearly 36 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong percentage decrease of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. In the first quarter of 2022 nearly 56 percent of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 27 percent of users in Mexico said they used Yahoo. Another search engine, Bing, operated by Microsoft, was the second most popular search engine in the United Kingdom after Google.

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

The dataset reports a collection of earnings call transcripts, the related stock prices, and the sector index In terms of volume, there is a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. Furthermore, the data collection was made possible by Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance enabled the search for stock values and Thomson Reuters Eikon provided the earnings call transcripts. Lastly, the dataset can be used as a benchmark for the evaluation of several NLP techniques to understand their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure.

Yahoo was the third most popular search engine in India, following Google and Bing. The multinational IT company's share in the South Asian country's desktop search market stood at 1.8 percent as of February 2024. After years of fluctuation, Yahoo's market share steadily increased, comparable with its share in February 2021.

The main stock market index in Nigeria (NSE-All Share) increased 2706 points or 2.63% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Nigeria. Nigeria Stock Market NSE - values, historical data, forecasts and news - updated on March of 2025.

Yahoo's share in the mobile search engine market across India was about 0.03 percent in February 2024. This was a fall in market share compared to its standing of 0.24 percent in September 2018. The immense popularity and database of Google has left little to gain for other search engine operators in India.

In January 2024, Yahoo! held a market share of 1.3 percent in the United Kingdom, being the third most popular search enginein the country.

In January 2025, the online search engine Bing accounted for 4.04 percent of the global search market across all devices, while market leader Google held a search traffic share of around 89.62 percent. Meanwhile, Yandex's market share was 2.62 percent, while Yahoo! represented around 1.34 percent.

In January 2024, Microsoft Sites handled 28.3 percent of all search queries in the United States. During the same period, Verizon Media (formerly known as Yahoo and Oath) had a search market share of little less than 11 percent. Market leader Google generated 60.4 percent of all core search queries in the United States.

This statistic depicts the market share of non-desktop search held by Yahoo in Italy from October 2015 to September 2021. In September 2021, Yahoo's market share of searching on mobile phone, tablet PC and game consoles was 0.34 percent.

The data set is collected for a quantile regression analysis testing the predictive ability of investor sentiment on bitcoin return and volatility. The data is obtained from several online sources including Google trends, Wikipedia, Twitter, News headlines, Bitcointalk.org, and market indexes available at yahoo finance. the dataset includes daily values from mid-2015 to the end of 2020.