Deep Learning for Forex and Stock Price Forecasting: Uncovering Market Secrets

Stock and foreign currency (Forex) forecasting has long been a fascinating and active field of study. Neural network uses have regularly led to higher accuracy and profits in financial forecasting. This paper also thoroughly reviews the dataset, components, models, and findings of each publication. The root means square errors (RMSE), the mean absolute percent error median absolute error (MAE), Average Squared Error (MSE), reliability, Beta ratio, and return rate are the most widely used performance metrics in our study. Our analysis identifies a recurring pattern in new models that combine LSTM with various approaches, particularly DNN, which has drawn significant research interest. Various deep learning techniques, including reinforcement learning, have continuously shown remarkable performance and returns. In conclusion, it is clear that the use of deep learning-based approaches for modeling finances has skyrocketed in recent years.