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Combining LSTM and CNN methods and fundamental analysis for stock price trend prediction

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Abstract

Stock market trend prediction has always been a major challenge for investors. In this paper, the combination of Convolutional Neural Network and long short-term memory methods, as well as fundamental analysis components such as P/E ratio, profitability and the number of company transactions have been used to increase the performance and reduce the model error in stock price trend prediction. To evaluate the model, the parameters of evaluating mean absolute error and mean absolute percentage error in four groups of financial, petroleum, basic metals and non-metallic minerals were employed, the results of which indicated an increase in the performance and a reduction in error. According to the results, in the financial group, we obtained 0.49 for the mean absolute percentage error and 4.30 the for mean absolute error. In petroleum group, mean absolute percentage error is 0.33 and mean absolute error equals 3.64. In basic metals group, mean absolute percentage error is 0.29 and mean absolute error equals 2.39. Finally, in non-metallic minerals group, we achieved 0.73 for mean absolute percentage error and 6.16 for mean absolute error. The values obtained in the proposed method show the effect of the model on the performance and prediction of error.

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Code availability

Code is available at https://github.com/ZahraNourbakhsh/stock-market-prediction-lstm-and-cnn

Notes

  1. Auto Regressive Integer Moving Average

  2. Generalized Autoregressive Conditional heteroskedasticity

  3. back propagation

  4. improved bacterial chemotaxis optimization

  5. Bank of Central Asia

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Authors and Affiliations

  1. Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

    Zahra Nourbakhsh & Narges Habibi

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  1. Zahra Nourbakhsh
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  2. Narges Habibi
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Correspondence to Narges Habibi.

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Nourbakhsh, Z., Habibi, N. Combining LSTM and CNN methods and fundamental analysis for stock price trend prediction. Multimed Tools Appl 82, 17769–17799 (2023). https://doi.org/10.1007/s11042-022-13963-0

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