Stock Market Value Prediction using Deep Learning
Main Article Content
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.
 M. Roondiwala, H. Patel, and S. Varma, “Predicting Stock Prices Using LSTM,” Int. J. Sci. Res., vol. 6, no. 4, pp. 2319–7064, 2015.
 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.
 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 Syst. Appl., vol. 113, pp. 457–480, 2018, doi: 10.1016/j.eswa.2018.07.019.
 K. Chen, Y. Zhou, and F. Dai, “A LSTM-based method for stock returns prediction: A case study of China stock market,” Proc. - 2015 IEEE Int. Conf. Big Data, IEEE Big Data 2015, pp. 2823–2824, 2015, doi: 10.1109/BigData.2015.7364089.
 J. Li, H. Bu, and J. Wu, “Sentiment-aware stock market prediction: A deep learning method,” 14th Int. Conf. Serv. Syst. Serv. Manag. ICSSSM 2017 - Proc., 2017, doi: 10.1109/ICSSSM.2017.7996306.
 D. M. Q. Nelson, A. C. M. Pereira, and R. A. De Oliveira, “Stock market’s price movement prediction with LSTM neural networks,” Proc. Int. Jt. Conf. Neural Networks, vol. 2017-May, no. Dcc, pp. 1419–1426, 2017, doi: 10.1109/IJCNN.2017.7966019.
 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.
 P. Yu and X. Yan, “Stock price prediction based on deep neural networks,” Neural Comput. Appl., vol. 32, no. 6, pp. 1609–1628, 2020, doi: 10.1007/s00521-019-04212-x.
 T. Fischer and C. Krauss, “Networks for Financial Market Predictions,” FAU Discuss. Pap. Econ. No. 11/2017, Friedrich-Alexander-Universität Erlangen-Nürnberg, Inst. Econ. Erlangen, pp. 1–34, 2017.