Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction

Recently, artificial intelligence technologies have received considerable attention because of their practical applications in various fields. The key factor in this prosperity is deep learning which is inspired by the information processing in biological brains. In this study, we apply one of the representative deep learning techniques multi-channel convolutional neural networks (CNNs) to predict the fluctuation of the stock index. Furthermore, we optimize the network topology of CNN to improve the model performance. CNN has many hyper-parameters that need to be adjusted for constructing an optimal model that can learn the data patterns efficiently. In particular, we focus on the optimization of feature extraction part of CNN, because this is the most important part of the computational procedure of CNN. This study proposes a method to systematically optimize the parameters for the CNN model by using genetic algorithm (GA). To verify the effectiveness of our model, we compare the prediction result with standard artificial neural networks (ANNs) and CNN models. The experimental results show that the GA-CNN outperforms the comparative models and demonstrate the effectiveness of the hybrid approach of GA and CNN.