STOCK PRICE PREDICTION USING DEEP NEURAL NETWORK

Abstract

Forecasting the financial market is one of the practical problems in the economic field. The noisy and the volatility are the two characteristic that hinders the timely prediction of the stock future price. In order to further resolve the drawbacks of the existing models in dealing with non-stationary and non-linear characteristics of high frequency financial time series data, this research work proposes the Wavelet transform based data preprocessing and developing the LSTM-attention model including the human sentiment for stock price prediction. The financial time series is smoothened by Wavelet transform, LSTM and attention mechanism is used to extract and train its features. Also the impact of human sentiment is investigated by adding the sentiment polarity score to historical dataset. The results of the proposed model are compared with the other two models, including LSTM and GRU on four different stocks ADBL, NIB, NABIL and SCB datasets. The Performance of the different models is evaluated based on RMSE, MAE, coefficient of determination R2, and MDA. The results from the experiments on all the stock datasets shows that RMSE, and MAE is less than 2.5 and 2.2 respectively and R2 is greater than 0.94 and MDA greater than 0.79. The results show that the proposed model along with the addition of human sentiment outperforms other similar models.