Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News
This work addresses the challenge of interpreting financial news for market forecasting, which is important for investors and analysts, but it is incremental as it builds on existing language model methods.
The paper tackled the problem of forecasting market impact from financial news by incorporating historical context, finding that it consistently and significantly improved performance across methods and time horizons, with substantial gains in simulated investment performance.
Financial news plays a critical role in the information diffusion process in financial markets and is a known driver of stock prices. However, the information in each news article is not necessarily self-contained, often requiring a broader understanding of the historical news coverage for accurate interpretation. Further, identifying and incorporating the most relevant contextual information presents significant challenges. In this work, we explore the value of historical context in the ability of large language models to understand the market impact of financial news. We find that historical context provides a consistent and significant improvement in performance across methods and time horizons. To this end, we propose an efficient and effective contextualization method that uses a large LM to process the main article, while a small LM encodes the historical context into concise summary embeddings that are then aligned with the large model's representation space. We explore the behavior of the model through multiple qualitative and quantitative interpretability tests and reveal insights into the value of contextualization. Finally, we demonstrate that the value of historical context in model predictions has real-world applications, translating to substantial improvements in simulated investment performance.