Karachi Stock Exchange Price Prediction using Machine Learning Regression Techniques

Hameed, Mazhar and Iqbal, Khurum and Ghazali, Rozaida and Jaskani, Fawwad Hassan and Saman, Zunaira (2021) Karachi Stock Exchange Price Prediction using Machine Learning Regression Techniques. EAI Endorsed Transactions on Creative Technologies, 8 (28). e5. ISSN 2409-9708

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: LSTM, LR, Machine Learning
Subjects: R Medicine > R Medicine (General)
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 29 Sep 2021 10:19
Last Modified: 29 Sep 2021 10:19
URI: https://eprints.eudl.eu/id/eprint/7132

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