Fuzzy Rule-Based Prediction of Gold Prices using News Affect

Abstract

Because of gold's value, systems for predicting its price have attracted extensive interest in the scientific and industrial communities. Diverse artificial intelligence methods outperform traditional statistical methods in predicting short- and long-term gold price. However, previous research has neglected the transparency of these systems, nor have these systems incorporated the potentially important effect of media sentiment on investment decisions. Therefore, we here propose a fuzzy rule-based prediction system with a component that processes various aspects of news stories. This system is trained on historical data to provide investors with one- and five-days-ahead gold price predictions while achieving a highly interpretable trading strategy in terms of rule complexity. We demonstrate that the proposed system is effective in terms of both prediction accuracy and interpretability compared with state-of-the-art models, such as extreme learning machines and neural networks with deep learning. Our findings suggest that the component of news affect is particularly important for one-day-ahead predictions. We also show that the proposed system performs well in terms of average annual return while providing an interpretable set of linguistic trading rules. This has important implications for investors.