The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

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.

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.

1) name - The full name of the company or stock listed in the dataset.Example: NVIDIA Corporation. dtype -- object

2) symbol - The stock ticker symbol, which is a unique identifier for the company in the stock exchange. Example: NVDA (NVIDIA). dtype -- object

3) price - The current trading price of the stock in USD.Example: 131.29. dtype -- float64

4) change - The net change in the stock price during the last trading session, expressed in USD. Positive values indicate an increase, while negative values indicate a decrease in price. Example: -1.54. dtype -- flaot64

5) volume - The total number of shares traded for the stock during the trading session.Represented in millions (e.g., 197.102M = 197,102,000 shares). Example: 197.102M. dtype -- object

6) market_cap - The market capitalization of the company, calculated as the total number of outstanding shares multiplied by the stock's price.Represented in trillions (T), billions (B), or other notations.Example: 3.202T. dtype -- object

7) pe_ratio - The Price-to-Earnings ratio, a financial metric to evaluate a company's profitability relative to its stock price.A value of -- indicates that the P/E ratio is unavailable, often because the company is not profitable.Example: 44.66. dtype -- float

Dataset

This dataset was created by Maimona Musad

Contents

Monthly Trending Stocks Dataset

A ranked dataset of the most trending stocks on Yahoo Finance from July 2024 to October 2025, based on weighted scoring of their monthly trending appearances in Yahoo Finance.

  📊 Dataset Overview

Total Entries: 7,993 ranked stocks Time Period: July 2024 - October 2025 (16 months) Source: Wayback Machine snapshots of Yahoo Finance Trending Stocks Data Granularity: Monthly rankings Data Order: Sorted by month (descending: Oct 2025 → July… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/trending-stocks-yahoo-finance.

The dataset comprises financial market data aggregated from two primary sources:

The data files contain seven low-dimensional financial research data (in .txt format) and two high-dimensional daily stock prices data (in .csv format). The low-dimensional data sets are provided by Lorenzo Garlappi on his website, while the high-dimensional data sets are downloaded from Yahoo!Finance by the contributor's own effort. The description of the low-dimensional data sets can be found in DeMiguel et al. (2009, RFS). The two high-dimensional data sets contain daily adjusted close prices (from Jan 1, 2013 to Dec 31, 2014) of the stocks, which are in the index components list (as of Jan 7, 2015) of S&P 500 and Russell 2000 indices, respectively.

Financial data from S&P500 historical members by Yahoo Finance. 2021 to 2024 data with the final indicators: ticker,year,date,Sector,Industry,Return,ROE,NetMargin,GrossMargin,DebtToEquity,CurrentRatio,ROA,PE,FCF_Yield,MarketCap,DividendPayout,ClosePrice,PricePrevYear Github to documentation: https://github.com/jbussi/trabalho_ICD

A survey conducted between 2022 and 2024 among consumers in the United States found that most of Yahoo! users visit the platform every day. In 2024, over 20 percent of respondents reported accessing Yahoo! services such as Yahoo Mail and Yahoo Finance daily. This represents a marginal increase compared to the usage recorded in the previous years. While approximately 40 percent of respondents reporting to have never used Yahoo! websites, daily and weekly usage remained more common than monthly access.

Source

• https://www.marketwatch.com
  • Download Data - xxxxx.csv
  • for daily data
• https://finance.yahoo.com/
  • xxxxx-series.csv
  • combined_data.csv
  • for daily data
• http://www.histdata.com/
  • DAT-MT-xxxxx.csv
  • for minutely data
• https://www.myfxbook.com/
  • xxxxx-historical-data.csv
  • for minutely data

Date Open High Low Close

Yahoo.com was the most-visited finance-related website worldwide in 2024, with an average of ************ visits. Paypal.com was ranked second with ************* monthly visits, while tradingview.com was ranked third, with ************* average accesses.

From the full period (Jan 1 – May 30, 2025), we extracted data corresponding to April 1, 2025 through May 31, 2025 and created this dataset.

  Data Curation





  Stock Data

Tickers: AAPL, TSLA, AMZN, MSFT, NVDA, GOOGL, META, INTC, SHOP, SPYG(10 stocks in total) Period: 2025‑01‑01 to 2025‑05‑30
Source: Historical daily OHLCV (open, high, low, close, volume) via a financial data API (e.g., Yahoo Finance).
Frequency: Daily (market close).

  Twitter Data

Accounts… See the full description on the dataset page: https://huggingface.co/datasets/Knovaai/tweetstock.

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.

Comparison of simulation result with S&P500 from Yahoo! Finance [32].

Dataset

This dataset was created by Vatsal Lakhmani

Released under MIT

Contents

View market daily updates and historical trends for CBOE Equity Put/Call Ratio. from United States. Source: Chicago Board Options Exchange. Track economic…

This is the name of the 38 global main stock indexes in the world. We collected from Yahoo! Finance. For the convenience of expression and computation later, we numbered it. For each item, the front is its serial number, followed by the corresponding stock index.

View monthly updates and historical trends for US M2 Money Supply YoY. from United States. Source: Federal Reserve. Track economic data with YCharts analy…

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.