Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics

Building on recent advances in Natural Language Processing and modeling of sequences, we study how a multimodal Transformer-based deep learning architecture can be used for measurement and structural narrative attribution in macroeconomics. The framework we propose combines (news) text and (macroeconomic) time series information using cross-attention mechanisms, easily incorporates differences in data frequencies and reporting delays, and can be used together with Reinforcement Learning to produce structurally coherent summaries of high-frequency news flows. Applied and tested on both simulated and real-world data out-of-sample, the results we obtain are encouraging.