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A deep comprehensive model for stock price prediction

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Abstract

Due to many factors and noise involved in stock market, predicting stock prices is a challenging task. Existing approaches for stock price prediction use only a subset of features and factors that are effective in stock market. In this paper, we present a stock prediction model, which takes into account a diverse and comprehensive set of factors and elements that affect stock market. We accordingly present a comprehensive input model, as a generalization and improvement of existing input models, and feed it into a learning model to predict stock prices. Our learning model is a neural network, consisting of 4 hidden LSTM layers. We evaluate our proposed stock price prediction model over eight real-world datasets, and show its high performance and accuracy.

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Notes

  1. In our experiments, the exchange rate of Iranian rial with the US dollar is used, as our studied companies are in Iran.

  2. We call it comprehensive because it takes into account a diverse and comprehensive set of features and factors. We call it deep because it exploits a deep neural network for prediction.

  3. http://www.msa.ir/index.aspx?siteid=3

  4. https://www.eorc.ir/

  5. http://www.saipacorp.com/en

  6. https://www.msc.ir/en-US/Portal/1/page/Home

  7. https://www.ikco.ir/en/

  8. https://www.nicico.com/

  9. https://www.baorco.ir/index.aspx?fkeyid= &siteid=1 &fkeyid= &siteid=1 &pageid=218

  10. https://www.bsi.ir/en/Pages/HomePage.aspx

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Authors and Affiliations

  1. Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, 1591634311, Iran

    Mehdi Salemi Mottaghi & Mostafa Haghir Chehreghani

Authors
  1. Mehdi Salemi Mottaghi
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  2. Mostafa Haghir Chehreghani
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Contributions

MSM: developed ideas, collected data, implemented algorithms, ran experiments, wrote paper. MHC: developed ideas, wrote paper, supervised work.

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Correspondence to Mostafa Haghir Chehreghani.

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Salemi Mottaghi, M., Haghir Chehreghani, M. A deep comprehensive model for stock price prediction. J Ambient Intell Human Comput 14, 11385–11395 (2023). https://doi.org/10.1007/s12652-023-04653-2

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