Prediction And Analysis Using A Hybrid Model For Stock Market

Success in optimising AI-related solutions has been greatly enhanced by the use of deep learning (AI). The stock market, trade execution tactics, and portfolio optimization are just some of the financial applications that make heavy use of this tool. A major use in this field is stock market forecasting. There has been a rise in the importance of Generative Adversarial Networks (GANs) equipped with sophisticated AI models. In computer vision, however, it is employed for translating images. Setting the appropriate hyperparameters for a GAN makes it impractical to utilise for stock market prediction. The authors propose using a combination of Bayesian optimization and reinforcement learning to solve this issue. Stock-GAN is a generator and discriminator that are part of a GAN-based deep learning architecture. LSTM is a recurrent NN, while CNNs are used to implement the latter (RNN). The Hybrid Prediction Algorithm is a new approach to prediction that is based on generative adversarial networks (GANs) (HPA). To predict stock prices, an empirical study found that Stock-GAN was more effective than the current state-of-the-art model; Multi-Model based Hybrid Prediction Algorithm (MM-HPA). After then, MM-HPA and GAN-HPA were fused to develop a new hybrid model called MMGAN-HPA, which has superior performance than both of its parent models.