Stock Market Value Prediction using Deep Learning

Yıl 2020, Cilt: 3 Sayı: 2, 10 - 14, 31.12.2020

Şeyda Kalyoncu Akhtar Jamil Enes Karataş Jawad Rasheed Chawki Djeddi

Öz

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. The trained proposed model is later tested on corresponding data, and performance metrics such as accuracy, RMSE and MSE reveals that the proposed LSTM model successfully predicts stock prices

Anahtar Kelimeler

Stock market prediction, machine learning, LSTM, deep learning

Kaynakça

• Tabii, işte düzeltilmiş referanslar:
• [1] J. Liu, F. Chao, Y.-C. Lin, and C.-M. Lin, “Stock Prices Prediction using Deep Learning Models,” Sep. 2019.
• [2] M. Roondiwala, H. Patel, and S. Varma, “Predicting Stock Prices Using LSTM,” International Journal of Scientific Research, vol. 6, no. 4, pp. 2319–7064, 2015.
• [3] A. M. El-Masry, M. F. Ghaly, M. A. Khalafallah, and Y. A. El-Fayed, “Deep Learning for Event-Driven Stock Prediction,” Xiao J. Sci. Ind. Res. (India), vol. 61, no. 9, pp. 719–725, 2002.
• [4] Y. Baek and H. Y. Kim, “ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module,” Expert Systems with Applications, vol. 113, pp. 457–480, 2018, doi: 10.1016/j.eswa.2018.07.019.
• [5] K. Chen, Y. Zhou, and F. Dai, “A LSTM-based method for stock returns prediction: A case study of China stock market,” Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, pp. 2823–2824, 2015, doi: 10.1109/BigData.2015.7364089.
• [6] J. Li, H. Bu, and J. Wu, “Sentiment-aware stock market prediction: A deep learning method,” 14th International Conference on Service Systems and Service Management, ICSSSM 2017 - Proceedings, 2017, doi: 10.1109/ICSSSM.2017.7996306.
• [7] D. M. Q. Nelson, A. C. M. Pereira, and R. A. De Oliveira, “Stock market’s price movement prediction with LSTM neural networks,” Proceedings - International Joint Conference on Neural Networks, vol. 2017-May, no. Dcc, pp. 1419–1426, 2017, doi: 10.1109/IJCNN.2017.7966019.
• [8] W. Bao, J. Yue, and Y. Rao, “A deep learning framework for financial time series using stacked autoencoders and long-short term memory,” PLoS One, vol. 12, no. 7, 2017, doi: 10.1371/journal.pone.0180944.
• [9] P. Yu and X. Yan, “Stock price prediction based on deep neural networks,” Neural Computing and Applications, vol. 32, no. 6, pp. 1609–1628, 2020, doi: 10.1007/s00521-019-04212-x.
• [10] T. Fischer and C. Krauss, “Networks for Financial Market Predictions,” FAU Discussion Papers in Economics No. 11/2017, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Economics Erlangen, pp. 1–34, 2017.