Stock Market Value Prediction using Deep Learning - Kalyoncu, Şeyda; Jamil, Akhtar; Karataş, Enes; Rasheed, Jawad; Djeddi, Chawki; Rasheed, Jawad (Sakarya Uygulamalı Bilimler Üniversitesi, 2020)
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.
Keywords: Stock market prediction; Machine learning; LSTM; Deep learning