Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA) for stock market prices prediction

Deep learning has achieved greater success in optimizing solutions associated with Artificial Intelligence (AI). In the financial domain, it is widely used for stock market prediction, trade execution strategies and portfolio optimization. Stock market prediction is a very significant use case in this domain. Generative Adversarial Networks (GANs) with advanced AI models have gained significance of late. However, it is used in image-image-translation and other computer vision scenarios. GANs are not used much for stock market prediction due to its difficulty in setting the right set of hyperparameters. In this paper, overcome this problem with reinforcement learning and Bayesian optimization. A deep learning framework based on GAN, named Stock-GAN, is implemented with generator and discriminator. The former is realized with LSTM, a variant of Recurrent Neural Network (RNN), while the latter uses Convolutional Neural Network. An algorithm named Generative Adversarial Network based Hybrid Prediction Algorithm (GAN-HPA) is proposed. An empirical study revealed that Stock-GAN achieves promising performance in stock price prediction when compared with the state of the art model known as Multi-Model based Hybrid Prediction Algorithm (MM-HPA). Afterwards, MM-HPA and GAN-HPA combined to form yet another hybrid model known as MMGAN-HPA for improved performance over MM-HPA and GAN-HPA.