Comparative Study of Deep Learning Models for Predicting Stock Prices

• Autorzy: Duong Van-Thang , Pham Thi-Thu-Hang , Nguyen Duc-Tuan-Anh , Nguyen Van-Hau , Le Van-Quoc Anh

Identyfikatory

DOI

10.15439/2022R02

• Języki publikacji: EN

Abstrakty

EN

The stock market is volatile, dynamic, and nonlinear. Hence, predicting the stock prices has been a challenging task for any researcher in time series forecasting. Accurately predicting stock prices has been a hot topic for both financial and technical researchers. In this paper, we deploy six deep learning models (i.e., MLP, CNN, RNN, LSTM, GRU, and AE) to predict the closing price, one day ahead, of 20 different companies (i.e. 5 groups of 4) in the S&P 500 markets over the 7-years range (Jan 2015 - August 2022). The experimental results do not provide interesting insights, but also help us to deepen our understanding of how to use deep learning models in financial markets.

Słowa kluczowe

EN

stock price prediction; deep learning; machine learning

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 103--108
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr.

Twórcy

autor

Duong Van-Thang

• Faculty of Information Technology University of Transport in Ho Chi Minh City (UTH) Ho Chi Minh, Vietnam

autor

Pham Thi-Thu-Hang

• Faculty of Information Technology Hung Yen University of Technology and Education (UTEHY) Hung Yen, Vietnam

autor

Nguyen Duc-Tuan-Anh

• Faculty of Information Technology Hung Yen University of Technology and Education (UTEHY) Hung Yen, Vietnam

autor

Nguyen Van-Hau

• Faculty of Information Technology Hung Yen University of Technology and Education (UTEHY) Hung Yen, Vietnam

autor

Le Van-Quoc Anh

• Faculty of Information Technology University of Transport in Ho Chi Minh City (UTH) Ho Chi Minh, Vietnam

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