Stock Market Value Prediction using Deep Learning

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Published Dec 31, 2020

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Seyda Kalyoncu
Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkey
Akhtar Jamil
Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkey
Enes Karataş
Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkey
Jawad Rasheed
Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkey
Chawki Djeddi
Department of Mathematics and Computer Science, Larbi Tebessi University, Tebessa, Algeria

Abstract

The stock market is a key indicator of the economic conditions of a country. Stock exchange provides a neutral ground for brokers and companies to invest. Due to high investment return, people tend to invest in stock markets rather than traditional banks. However, there is high risk is investment in stock markets due to high fluctuations in exchange rates. Therefore, developing a highly robust stock prediction system can help investors to make a better decision about investment. In this study, a deep learning-based approach is applied on the stock historical data to predict the future market value. Specifically, we used Long-Short Term Memory (LSTM) for prediction of stock value of five well known Turkish companies in the stock market. As a result of RMSE, MSE accuracy tests made using these data, it has been seen that stock market prediction can be made successfully with LSTM.


 

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How to Cite
[1]
S. Kalyoncu, A. Jamil, E. Karataş, J. Rasheed, and C. Djeddi, “Stock Market Value Prediction using Deep Learning”, DataSCI, vol. 3, no. 2, pp. 10-14, Dec. 2020.
Issue
Vol 3 No 2 (2020)
Section
Research Articles

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