Pakistan's main stock market index, the KSE 100, fell to 167838 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has climbed 3.09% and is up 60.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Pakistan. Pakistan Stock Market (KSE100) - values, historical data, forecasts and news - updated on December of 2025.

This dataset contains historical stock market data for the Top 50 companies listed on the Pakistan Stock Exchange. It includes daily OHLCV (Open, High, Low, Close, and Volume) data making it a valuable resource for financial analysis forecasting and time series modeling.

The dataset is particularly useful for:

. Stock trend analysis

. Volatility and risk-return studies

. Machine learning models for prediction

. Portfolio optimization & financial research

Column Descriptors

symbol : Ticker symbol of the company (e.g., MARI, NESTLE, RMPL).

date : Trading date (format: YYYY-MM-DD).

open : Opening stock price on that day.

high : Highest stock price during the trading session.

low : Lowest stock price during the trading session.

close : Closing stock price on that day.

volume : Number of shares traded.

month : Month extracted from the trading date

year : Year extracted from the trading date.

This dataset provides historical daily trading data for companies listed on the Pakistan Stock Exchange (PSX). It includes records of stock prices, volumes, and percentage changes across all active and delisted companies over the period January 2017 – October 2025.

The dataset can be used for:

Financial market trend analysis

Predictive modeling of stock movements

Time-series forecasting

Quantitative finance and algorithmic trading research

Data visualization and analytics education

Data Source

Data compiled from publicly available PSX company price reports and market summaries.

Coverage

Exchange: Pakistan Stock Exchange (PSX)

Time Range: 2017-01-02 → 2025-10-24

Frequency: Daily (trading days only)

Total Records: 840,330 rows

File Details Column Description DATE Trading date (YYYY-MM-DD) SYMBOL Stock symbol/ticker LDCP Last Day Closing Price (previous close) OPEN Opening price of the day HIGH Highest traded price during the day LOW Lowest traded price during the day CLOSE Closing price of the day CHANGE Absolute price change (CLOSE − LDCP) CHANGE (%) Percentage change relative to previous close VOLUME Number of shares traded

Notes

Missing or zero values may represent suspended or inactive trading for a symbol on a given date.

Data has been cleaned and standardized for consistent formatting.

Date range and total records verified as of October 24, 2025.

PSX Listed Companies Dataset

Overview

This dataset contains detailed information about companies listed on the Pakistan Stock Exchange (PSX). The PSX is the premier stock exchange in Pakistan, where companies from various sectors are publicly listed for trading. The data was scraped from the official PSX website and includes essential information about each listed company, its representative, and contact details. This dataset can be valuable for anyone interested in financial markets, business research, or investment opportunities within Pakistan.

Data Features

The dataset contains the following columns:

• Representative: Name of the company representative.
• Designation: The representative's role or title within the company.
• Company: Name of the listed company.
• Address: Official mailing address of the company.
• Phone: Primary contact number.
• Phone 2: Additional contact number (if available).
• Fax: Fax number (if available).
• Date of Listing: The date when the company was listed on the PSX.
• Email: The contact email address of the representative or company.
• URL: The company's website.
• Registrar Details: Information about the company's registrar, which handles the company's share-related matters.

Sectors Represented

The dataset includes companies from a wide variety of sectors, reflecting the diversity of industries on the PSX. Some key sectors include: - Automobile Assembler - Cement - Commercial Banks - Fertilizer - Food & Personal Care Products - Pharmaceuticals - Technology & Communication - Textile Composite

And many more, totaling 37 different sectors.

Usage

This dataset can be used for multiple purposes: 1. Financial Analysis: Explore the performance of different sectors and companies listed on the PSX. 2. Investment Research: Identify key players in different industries for investment opportunities. 3. Business Development: Build contact lists for companies within a specific sector. 4. Data Science & Machine Learning Projects: Use this dataset for clustering, classification, or sentiment analysis in financial markets.

Data Format

The dataset is available in CSV format, making it easy to load into data analysis tools like Pandas, Excel, or Power BI. It's structured for easy exploration and can be integrated into financial models or research projects.

Acknowledgements

The data was scraped from the official PSX website using a custom Python script. Special thanks to the open-source community for tools like Selenium, BeautifulSoup, and Pandas, which made this project possible.

License

This dataset is provided for educational and research purposes. Please give proper attribution when using this dataset in your work.

Feel free to explore, analyze, and share your insights!

Prices for Karachi Stock Exchange KSE100 Index including live quotes, historical charts and news. Karachi Stock Exchange KSE100 Index was last updated by Trading Economics this November 30 of 2025.

KSE 100 Index Historical Data (2004-2024) (Accurately Curated, no missing data)

This dataset contains historical data for the KSE 100 Index, covering the past 20 years (2004-2024). It includes daily trading information such as opening, high, low, and closing prices, as well as trading volume. This dataset is ideal for financial analysis, stock market research, and developing predictive models for market trends. It provides a comprehensive view of the performance and fluctuations of Pakistan's leading stock market index over two decades.

A significant correlation between financial news with stock market trends has been explored extensively. However, very little research has been conducted for stock prediction models that utilize news categories, weighted according to their relevance with the target stock. In this paper, we show that prediction accuracy can be enhanced by incorporating weighted news categories simultaneously into the prediction model. We suggest utilizing news categories associated with the structural hierarchy of the stock market: that is, news categories for the market, sector, and stock-related news. In this context, Long Short-Term Memory (LSTM) based Weighted and Categorized News Stock prediction model (WCN-LSTM) is proposed. The model incorporates news categories with their learned weights simultaneously. To enhance the effectiveness, sophisticated features are integrated into WCN-LSTM. These include, hybrid input, lexicon-based sentiment analysis, and deep learning to impose sequential learning. Experiments have been performed for the case of the Pakistan Stock Exchange (PSX) using different sentiment dictionaries and time steps. Accuracy and F1-score are used to evaluate the prediction model. We have analyzed the WCN-LSTM results thoroughly and identified that WCN-LSTM performs better than the baseline model. Moreover, the sentiment lexicon HIV4 along with time steps 3 and 7, optimized the prediction accuracy. We have conducted statistical analysis to quantitatively assess our findings. A qualitative comparison of WCN-LSTM with existing prediction models is also presented to highlight its superiority and novelty over its counterparts.

The Pakistani market for toilet paper, napkins, towels and tissue stock amounted to $9.2B in 2024, remaining relatively unchanged against the previous year. Over the period under review, the total consumption indicated buoyant growth from 2012 to 2024: its value increased at an average annual rate of +5.5% over the last twelve years. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period.

Key information about Pakistan Market Capitalization

• Pakistan Market Capitalization accounted for 67.258 USD bn in Nov 2025, compared with a percentage of 66.080 USD bn in the previous month
• Pakistan Market Capitalization is updated monthly, available from Mar 1999 to Nov 2025
• The data reached an all-time high of 96.158 USD bn in May 2017 and a record low of 4.459 USD bn in Sep 2001

Technical indicators selected for stock trend prediction (Adopted from [8]).

Dataset

This dataset was created by Syed Owais Ali Chishti

Released under GPL 2

Contents

A significant correlation between financial news with stock market trends has been explored extensively. However, very little research has been conducted for stock prediction models that utilize news categories, weighted according to their relevance with the target stock. In this paper, we show that prediction accuracy can be enhanced by incorporating weighted news categories simultaneously into the prediction model. We suggest utilizing news categories associated with the structural hierarchy of the stock market: that is, news categories for the market, sector, and stock-related news. In this context, Long Short-Term Memory (LSTM) based Weighted and Categorized News Stock prediction model (WCN-LSTM) is proposed. The model incorporates news categories with their learned weights simultaneously. To enhance the effectiveness, sophisticated features are integrated into WCN-LSTM. These include, hybrid input, lexicon-based sentiment analysis, and deep learning to impose sequential learning. Experiments have been performed for the case of the Pakistan Stock Exchange (PSX) using different sentiment dictionaries and time steps. Accuracy and F1-score are used to evaluate the prediction model. We have analyzed the WCN-LSTM results thoroughly and identified that WCN-LSTM performs better than the baseline model. Moreover, the sentiment lexicon HIV4 along with time steps 3 and 7, optimized the prediction accuracy. We have conducted statistical analysis to quantitatively assess our findings. A qualitative comparison of WCN-LSTM with existing prediction models is also presented to highlight its superiority and novelty over its counterparts.

The dataset contains stock prices of more than 100 top companies of Pakistan. The data is extracted from and starts ftom Nov-2017 to Dec-2022

Each record (company) in the data inclused information on the following: High : the highest price at which a stock traded during the course of the trading day Low: the lowest price at which a stock traded during the course of the trading day Open: The price when the stock opens for the day. Low: Open: The price when the stock closes for the day.

After four years of decline, the Pakistani date market increased by 13% to $223M in 2024. Overall, the total consumption indicated a slight expansion from 2012 to 2024: its value increased at an average annual rate of +1.5% over the last twelve years. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period. As a result, consumption reached the peak level of $312M. From 2020 to 2024, the growth of the market remained at a somewhat lower figure.

Baseline model’s (LSTM) optimized hyper-parameters values.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 379 companies listed on the Pakistan Stock Exchange (XKAR) in Pakistan. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Pakistan:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Pakistan:

Karachi Stock Exchange (KSE) 100 Index: The main index that tracks the performance of the top 100 companies listed on the Pakistan Stock Exchange (formerly known as the Karachi Stock Exchange). This index provides a broad view of the overall market performance in Pakistan.

Karachi Stock Exchange (KSE) 30 Index: The index that tracks the performance of the top 30 companies listed on the Pakistan Stock Exchange. This index represents the performance of the most influential and actively traded companies in Pakistan.

Company A: A leading Pakistani company in sectors such as textiles, manufacturing, or technology. This company's stock is among the most actively traded on the Pakistan Stock Exchange and has a significant impact on market trends.

Company B: A major financial institution in Pakistan, offering banking, insurance, or investment services. This company's stock is influential in the financial sector and reflects the health of Pakistan's economy.

Company C: A significant player in Pakistan's energy sector, involved in oil and gas exploration, production, or distribution. This company's stock is actively traded and has an impact on Pakistan's energy landscape.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Pakistan, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Pakistan ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Pakistan?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Pakistan exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for t...

ARCH and DCC GARCH for Pakistan stock market and bank deposits.

Actually, I prepare this dataset for students on my Deep Learning and Machine Learning course.

But I am also very happy to see kagglers play around with it.

Have fun!

Context

High-quality financial data is expensive to acquire and is therefore rarely shared for free. Here I provide the full historical daily price and volume data for all US-based stocks in Karachi stock. It's one of the best datasets of its kind you can obtain.

This Data Contain 801 companies that are registered in Karachi Stock Market, Pakistan. I want to analysis the analysis the Karachi stock market.

Data Details

This dataset contain data from Jan, 01, 2003 to Aug ,30 2019. In each company contain 7 Columns, that are follows 1. Symbol 2. Date 3. Open 4. High 5. Low 6. Close 7. Volume

Objective

• Predict stock share price single variable value. • Predict stock share price multiple variable value. • To find a correlation or forecast time-series data.

The PSX 100 Index is a key benchmark index of the Pakistan Stock Exchange, representing the top 100 companies by market capitalization. It includes a diverse range of industries and provides a comprehensive view of Pakistan's market performance.

Dataset Features: - Date: The trading date. - Open: The opening price of the stock on the given day. - High: The highest price of the stock on the given day. - Low: The lowest price of the stock on the given day. - Close: The closing price of the stock on the given day. - Volume: The number of shares traded on the given day.

Purpose: This dataset aims to offer detailed insights into the stock price trends of PSX 100 companies over the past decade. It is intended for financial analysis, academic research, and investment decision-making.

In 2024, the Pakistani tissue paper market decreased by -2.3% to $3.8B, falling for the third consecutive year after two years of growth. The market value increased at an average annual rate of +1.7% over the period from 2012 to 2024; however, the trend pattern indicated some noticeable fluctuations being recorded throughout the analyzed period. As a result, consumption attained the peak level of $4.9B. From 2022 to 2024, the growth of the market remained at a somewhat lower figure.