metadata

license: mit
task_categories:
  - text-classification
language:
  - aa
tags:
  - finance
pretty_name: pettahai
size_categories:
  - 1K<n<10K

Global Top Index: Exploring Trends in Stock Markets

About the Dataset

The Global Top Index dataset offers a detailed view of daily trading activities from several of the world's leading stock market indices. This dataset is ideal for conducting comprehensive analyses to uncover insights and predictive trends in the international stock markets.

Dataset Contents

The dataset encompasses the following key data points for each trading session across multiple dates, allowing for temporal trend analysis and pattern recognition:

Date: The date of the trading session.
Name: Name of the stock market index.
Symbol: Symbol or abbreviation representing the index.
Open: Opening price of the index for the trading session.
High: Highest price reached by the index during the trading session.
Low: Lowest price reached by the index during the trading session.
Close: Closing price of the index for the trading session.
Adj Close: Adjusted closing price, accounting for corporate actions such as dividends or stock splits.
Volume: Total volume of shares traded for the index during the trading session.

Source

This data is sourced from Yahoo Finance, which provides comprehensive financial information and data analysis tools. Yahoo Finance aggregates data from various stock exchanges around the world, giving users access to real-time and historical market data, news, analysis, and portfolio management resources.

Collection Methodology

Data for this dataset was gathered using Python libraries Selenium and BeautifulSoup4 (bs4), ensuring accurate and up-to-date information from the web. The scraping scripts are designed to run quarterly, capturing the latest market trends and updates.

Expected Update Frequency

The dataset is updated quarterly. The last update was made 19 days ago.

Usage

This dataset is valuable for researchers, financial analysts, and investors looking to study market trends, conduct performance analysis, and make informed investment decisions. The data can be utilized for various applications such as predictive modeling, comparative analysis, and temporal trend analysis in the context of global financial markets.

License

MIT License