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 July 13 of 2025.

Pakistan's main stock market index, the KSE 100, rose to 134300 points on July 11, 2025, gaining 0.39% from the previous session. Over the past month, the index has climbed 8.23% and is up 67.99% 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 July of 2025.

Pakistan Market Cap: PSX: All Shares data was reported at 13,520,596.056 PKR mn in Apr 2025. This records a decrease from the previous number of 14,374,200.085 PKR mn for Mar 2025. Pakistan Market Cap: PSX: All Shares data is updated monthly, averaging 4,126,558.206 PKR mn from Mar 1999 (Median) to Apr 2025, with 314 observations. The data reached an all-time high of 14,495,888.757 PKR mn in Dec 2024 and a record low of 285,126.330 PKR mn in Sep 2001. Pakistan Market Cap: PSX: All Shares data remains active status in CEIC and is reported by Pakistan Stock Exchange Limited. The data is categorized under Global Database’s Pakistan – Table PK.Z003: Karachi Stock Exchange: Market Capitalization (New Classification).

Pakistan Karachi Stock Exchange: Index: KSE 100 Index data was reported at 81,114.200 NA in Sep 2024. This records an increase from the previous number of 78,488.220 NA for Aug 2024. Pakistan Karachi Stock Exchange: Index: KSE 100 Index data is updated monthly, averaging 40,556.645 NA from Jun 2013 (Median) to Sep 2024, with 134 observations. The data reached an all-time high of 81,114.200 NA in Sep 2024 and a record low of 21,005.690 NA in Jun 2013. Pakistan Karachi Stock Exchange: Index: KSE 100 Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Pakistan – Table PK.EDI.SE: Karachi Stock Exchange: Monthly.

Stock market return (%, year-on-year) in Pakistan was reported at 16.44 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Should You Buy, Sell, or Hold? (Karachi 100 Index Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Key information about Pakistan Market Capitalization

• Pakistan Market Capitalization accounted for 49.986 USD bn in Feb 2025, compared with a percentage of 50.382 USD bn in the previous month
• Pakistan Market Capitalization is updated monthly, available from Mar 1999 to Feb 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

CEIC converts monthly Market Capitalization into USD. Pakistan Stock Exchange Limited provides Market Capitalization in local currency. The State Bank of Pakistan period end market exchange rate is used for currency conversions.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Karachi 100 Index Target Price Forecast

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Stability of the economy is always a great challenge across the world, especially in under developed countries. Many researchers have contributed to forecasting the Stock Market and controlling the situation to ensure economic stability over the past several decades. For this purpose, many researchers have built various models and gained benefits. This journey continues to date and will persist for the betterment of the stock market. This study is also a part of this journey, where four learning-based models are tailored for stock price prediction. Daily business data from the Karachi Stock Exchange (100 Index), covering from February 22, 2008 to February 23, 2021, is used for training and testing these models. This paper presenting four deep learning models with different architectures, namely the Artificial Neural Network model, the Recurrent Neural Network with Attention model, the Long Short-Term Memory Network with Attention model, and the Gated Recurrent Unit with Attention model. The Long Short-Term Memory with attention model was found to be the top-performing technique for accurately predicting stock exchange prices. During the Training, Validation and Testing Sessions, we observed the R-Squared values of the proposed model to be 0.9996, 0.9980 and 0.9921, respectively, making it the best-performing model among those mentioned above.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Short/Long Term Stocks: Karachi 100 Index Stock Forecast

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

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.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Karachi 100 Index Stock Price Prediction

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Metrics of Testing Data generated by LSTM-Attention,ANN, RNN-attention and GRU-Attention. .

Training and testing loss of LSTM-attention model, ANN model, RNN-attention model and GRU-attention model.