LEST: Large language models and spatio-temporal data analysis for enhanced Sino-US exchange rate forecasting

Exchange rate forecasting has a significant impact on a country’s balance of international payments, financial security and stability. Under the context of Sino-US trade war, a series of news events have happened thus increasing the abnormal fluctuations in the RMB exchange rate, and these fluctuations have made it more difficult to forecast the exchange rate. The analysis of news events’ impact on exchange rate fluctuations focused on assigning emotional labels to different economic news and political events by building an emotional lexicon. However, the previous method of using an offline emotional lexicon to analyze events could not be updated promptly making it difficult to better exploit implicit information of news events. Also, the offline emotional lexicon requires manual annotation of events which is unavoidable and subjective and therefore influences the sentiment analysis of the event. Therefore, we build a fusion forecasting framework of spatio-temporal and emotional information for accurate, real-time exchange rate forecasting. First, in the preprocessing phase, we leveraged the situational learning capabilities of Large Language Models (LLMs), specifically ChatGPT, combined with prompt engineering to enable ChatGPT to provide real-time, online, and precise automated sentiment analyses of Sino-US trade events. Second, we proposed the SG-TL fusion model, which integrates Spatio-Temporal Graph Convolutional Networks (STGCN) and Gated Recurrent Unit (GRU) models in the feature processing approach to accurately extract exchange rate features in spatial and temporal dimensions. We also employed Transformer and Long Short-Term Memory (LSTM) models in the forecasting phase to forecast the Sino-US exchange rates. Finally, Experiment results demonstrate that the exchange rate forecasting framework LEST can accurately capture the exchange rate fluctuations during the period of Sino-US trade war. Furthermore, its forecasting performance is more precise, efficient, and stable than existing works. The source code of our developed method is publicly available at https://gitee.com/andyham_andy.ham/lest-forecasting-framework.