Novel Deep Learning Model with CNN and Bi-Directional LSTM for Improved Stock Market Index Prediction

Predicting variations in stock price index has been an important application area of machine learning research. Due to the non-linear and complex nature of the stock market making predictions on stock price index is a challenging and non-trivial task. Deep learning approaches have become an important method in modeling complex relationships in temporal data. In this paper: (i) we propose a novel deep learning model that combines multiple pipelines of convolutional neural network and bi-directional long short term memory units. (ii) Proposed model improves prediction performance by 9% upon single pipeline deep learning model and by over a factor of six upon support vector machine regressor model on S&P 500 grand challenge dataset. (iii) We illustrate the improvement in prediction accuracy while minimizing the effects of overfitting by presenting several variations of multiple and single pipeline deep learning models based on different CNN kernel sizes and number of bi-directional LSTM units.