Research on the Exchange Rate Forecast of the Pound Sterling and the Dollar Based on Neural Networks - Chen Jiantao; Song Yining (ITM Web of Conferences)
The dynamics of the British Pound (GBP) and the US Dollar (USD), as reflected in their exchange rate, profoundly shape global trade and investment trends. Traditional forecasting methodologies, grounded in statistical analysis and economic indicators, often struggle with accurately capturing the dynamic fluctuations of this currency's value, given the complex interplay of influencing variables. In response to this challenge, this paper proposes a neural network-based prediction model to forecast future GBP/USD exchange rates. Utilizing Matlab as the computational platform, the paper collects extensive historical exchange rate data and designs a multilayer feed forward neural network architecture. Through meticulous adjustments, the paper determines the optimal number of neurons in the concealed layers to achieve a harmonious equilibrium between the model's complexity and its aptitude for generalization. During the training phase, the back-propagation algorithm is employed to reduce prediction errors, and rigorous cross-validation techniques are utilized to precisely evaluate the model's performance. The optimized model is then utilized to predict future exchange rates, and its accuracy and practicality are validated through comparisons with actual market data. This research not only provides investors with a novel and effective tool for exchange rate forecasting but also paves the way for further exploration and application of neural networks in the realm of financial market prediction.