Stock Price Prediction Using Recurrent Neural Networks and Genetic Algorithm

In the realm of finance, accurate projections of stock prices carry immense significance. Multiple factors influence stock prices, including external variables such as influential opinions, demand, and geopolitical events. These dynamics hold critical implications from a business perspective, rendering technical analysis of sequential stock price data complex due to their ever-changing nature. Investors consistently seek precise prognostications. In this study, we present an innovative approach utilizing Deep Learning Techniques, specifically Neural Networks, to forecast stock prices. Our primary objective is to construct a robust Recurrent Neural Network (RNN) model capable of delivering accurate stock price predictions while conducting a comprehensive comparison and analysis of outcomes against existing models. Ultimately, our proposed solution amalgamates two RNN architectures, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), resulting in a hybrid model. Additionally, we integrate Genetic Algorithms (GA) to optimize the hyperparameters of the model, enhancing its predictive performance. We systematically assess the influence of each network component and the impact of GA optimization to ascertain their contributions to the overall accuracy and robustness of stock price predictions.