Abstract:
"This research report investigates the potential of neural networks to improve the accuracy of short-term forex predictions. The study integrates economic news events and technical
indicators to address a significant gap in the existing literature, which often treats these factors in isolation. By employing advanced neural network architectures, including LSTM, ANN, FNN, CNN, and RNN, the research aims to develop a robust model for predicting the
GBP/USD exchange rate.
The methodology includes gathering historical and real-time data, hyperparameter tuning, and comprehensive model testing. The primary objective is to enhance predictive precision,
providing valuable insights for traders, financial analysts, and policymakers. The anticipated
outcomes suggest that this innovative approach could outperform traditional methods, offering a more nuanced understanding of forex market behaviour and significantly impacting financial decision-making.
Key findings will be presented in a detailed project report, contributing to academic knowledge and providing practical tools for the financial industry. The study underscores the importance of combining multiple data sources and sophisticated computational techniques to improve the forecasting of short-term currency movements."
