ct 21(28): e5

Research Article

Karachi Stock Exchange Price Prediction using Machine Learning Regression Techniques

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  • @ARTICLE{10.4108/eai.24-8-2021.170753,
        author={Mazhar Hameed and Khurum Iqbal and Rozaida Ghazali and Fawwad Hassan Jaskani and Zunaira Saman},
        title={Karachi Stock Exchange Price Prediction using Machine  Learning Regression Techniques},
        journal={EAI Endorsed Transactions on Creative Technologies},
        keywords={LSTM, LR, Machine Learning},
  • Mazhar Hameed
    Khurum Iqbal
    Rozaida Ghazali
    Fawwad Hassan Jaskani
    Zunaira Saman
    Year: 2021
    Karachi Stock Exchange Price Prediction using Machine Learning Regression Techniques
    DOI: 10.4108/eai.24-8-2021.170753
Mazhar Hameed1, Khurum Iqbal2, Rozaida Ghazali3, Fawwad Hassan Jaskani4,*, Zunaira Saman5
  • 1: School of Information and Communication Engineering, North University of China
  • 2: Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Science and Technology, Rawalpindi, Pakistan
  • 3: Fakulti Sains Komputer Dan Teknologi Maklumat, Universiti Tun Hussein Onn Malaysia
  • 4: Department of Computer Engineering, Islamia University of Bahawalpur, Pakistan
  • 5: Ghazi University, Dera Ghazi Khan, Pakistan
*Contact email: favadhassanjaskani@gmail.com


Accurate stock market returns are quite difficult for the company because of the unpredictable and non-linear nature of the financial stock markets. With the development of artificial intelligence and increased computer power, programmed prediction approaches have demonstrated that they are increasingly effective in predicting stock values. In this study, the Artificial Neural Network, LSTM, and LR techniques were used to predict the closing price for the following day for five companies belonging to different business sectors. In today's economy, the stock market or equity market has a profound influence. The prediction of stock prices is quite complex, chaotic, and it is a big challenge to have a dynamic environment. Behavioural finance means that investors' decision-making processes are affected by emotions and attitudes in response to particular news. In order to help investors' judgements, we have supplied a technology for the analysis of the stock exchange. The method combines historical price prediction. For predicting, LSTM (Long Short-Term Memory), ANN and LR are employed. It includes the latest information on trade and analytical indicators. Financial data: Open, high, low and close stock prices are used to build new variables needed for model input. The models are validated with standard strategic indicators: RMSE and MAPE. The low values of these two variables indicate that the models are cost-effective.