A Deep Neural Network Model for Forecasting Variable Stock Movement Intervals

Forecasting results for stock markets exhibit variabilities in forecasting accuracy as the tenure of prediction is varied. Typically stocks are predicted for long, short as well as mid term tenures based on the tenure of prediction. While longer tenures have relatively much larger data to be trained as training data, divergences are also potentially large as the forecasting period may render higher randomness due to unprecedented events. The short term forecasting is relatively less prone to unprecedented events due to the tenure of forecasting. However, the lesser amount of training data may result in less accurate pattern recognition. A common ground is typically found in terms of mid term forecasting. This paper presents an experimental evaluation of all three formats of forecasting based on the training, testing split. The deep neural network model is used for the forecasting purpose and the forecasting MAPE and accuracy has been tabulated for a multitude of stocks. It has been shown that the proposed approach attains an MAPE of only 2.18% outperforming existing work in the domain.