A Novel CNN-LSTM Model for Predicting the Railway Sector Stock Price in the Tehran Stock Exchange

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Bodaghi Faraz; Owhadi Amin; Khalili Nasr Arash; Khadem Sameni Melody

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

A Novel CNN-LSTM Model for Predicting the Railway Sector Stock Price in the Tehran Stock Exchange

Author(s)

Bodaghi Faraz | Owhadi Amin | Khalili Nasr Arash | Khadem Sameni Melody

Keywords

Stock market prediction

Railway companies

Deep learning

Data analysis

Abstract

As a country's financing arm, the capital market can play a crucial role in long-term projects and be responsible for large-scale financing. While the development of rail transport has exceptionally positive and significant effects on the country's macroeconomy, and investment in this sector leads to the prosperity of other economic sectors, the return on investment may be delayed or unclear. Consequently, this uncertainty diminishes the investment appeal in this field. Thus, predicting the price and future value of railway company stocks on the stock market is vital due to the need for the foresight to take safe investment measures and achieve sustainable financing. To this end, this paper investigated the use of Deep learning methods to forecast the closing price of MAPNA and Toucaril stocks on the Tehran Stock Exchange. Stock prices were predicted using deep neural networks, including Long Short-Term Memory (LSTM), One-dimensional Convolutional Neural Networks (1D-CNN), and CNN-LSTM networks as a hybrid model. Finally, to evaluate the performance of the models, MAE, MSE, RMSE, MAPE, and R2 were used as evaluation criteria. The results indicate that Deep learning models can forecast stock prices with accuracy. In this study, CNN-LSTM neural network produced the best results.

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APA: Copy

Bodaghi, Faraz, Owhadi, Amin, Khalili Nasr, Arash, & Khadem Sameni, Melody. (). . . SID. https://sid.ir/paper/1046850/en

Vancouver: Copy

Bodaghi Faraz, Owhadi Amin, Khalili Nasr Arash, Khadem Sameni Melody. . . Available from: https://sid.ir/paper/1046850/en

IEEE: Copy

Faraz Bodaghi, Amin Owhadi, Arash Khalili Nasr, and Melody Khadem Sameni, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046850/en

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