NEPSE Daily Index Data (2020-2025)

Subtitle: Daily NEPSE Index Values, Changes, and Percentages from 2020 to March 2025

This dataset provides a comprehensive collection of daily historical data for the Nepal Stock Exchange (NEPSE) Index, covering the period from January 1, 2020, to March 4, 2025 (with data available up to March 4, 2025, at the time of creation). The NEPSE Index is the primary benchmark for Nepal's stock market, reflecting the performance of listed companies. This dataset was scraped from publicly available financial data sources and is intended for financial analysis, time series forecasting, and economic research related to Nepal's capital markets.

Dataset Details

Columns:

• Date: The trading day in YYYY-MM-DD format (e.g., 2025-03-04).
• Index Value: The closing value of the NEPSE Index for the day, representing the market’s end-of-day performance.
• Absolute Change: The daily change in the index value (positive or negative), calculated as the difference from the previous trading day’s close.
• Percentage Change: The daily percentage change in the index value, reflecting the relative movement (e.g., -1.05 for a -1.05% drop).

Data Range:

• Start Date: January 1, 2020
• End Date: March 4, 2025 (latest available as of dataset creation)
• Frequency: Daily (trading days only; excludes weekends and public holidays as per NEPSE’s schedule)

Size:

• Approximately 1,200–1,500 rows (depending on trading days), covering over 5 years of data.

Source

The data was sourced from ShareSansar, a leading financial portal in Nepal providing NEPSE index history. It was scraped using Python with Selenium and processed into a clean CSV format.

Methodology

• Scraping: Automated web scraping extracted daily records from ShareSansar’s Index History Data page.
• Processing: Data was cleaned to ensure consistent date formatting and numeric values (removing commas and percentage signs).
• Tools: Python libraries including Selenium, BeautifulSoup, and pandas were used to compile this dataset.

Usage

This dataset is ideal for: - Time Series Analysis: Forecasting NEPSE trends using models like ARIMA, LSTM, or Prophet. - Financial Research: Studying Nepal’s stock market volatility, growth patterns, or economic correlations. - Data Visualization: Plotting index trends, daily changes, or comparative analyses with other markets. - Educational Purposes: Learning data analysis or financial modeling with real-world data.

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