Firm risk relations are crucial in financial applications, including hedging and portfolio construction. However, the complexity of extracting relevant information from financial reports poses significant challenges in quantifying these relations. To this end, we introduce SURF, a System to Unveil Explainable Risk Relations between Firms. SURF employs a domain-specific encoder and an innovative scoring mechanism to uncover latent risk connections from financial reports. It constructs a network graph to visualize these firm-level risk interactions and incorporates a rationale explainer to elucidate the underlying links. Our evaluation using stock data shows that SURF outperforms baseline methods in effectively capturing firm risk relations. The demo video of the system is publicly available.

In this paper, we address the challenge of discovering financial signals in narrative financial reports. As these documents are often lengthy and tend to blend routine information with new information, it is challenging for professionals to discern critical financial signals. To this end, we leverage the inherent nature of the year-to-year structure of reports to define a novel signal-highlighting task; more importantly, we propose a compare-and-contrast multistage pipeline that recognizes different relationships between the reports and locates relevant rationales for these relationships. We also create and publicly release a human-annotated dataset for our task. Our experiments on the dataset validate the effectiveness of our pipeline, and we provide detailed analyses and ablation studies to support our findings.

In this system demonstration, we seek to streamline the process of reviewing financial statements and provide insightful information for practitioners. We develop FISH, an interactive system that extracts and highlights crucial textual signals from financial statements efficiently and precisely. To achieve our goal, we integrate pre-trained BERT representations and a fine-tuned BERT highlighting model with a newly-proposed two-stage classify-then-highlight pipeline. We also conduct the human evaluation, showing FISH can provide accurate financial signals. FISH overcomes the limitations of existing research andmore importantly benefits both academics and practitioners in finance as they can leverage state-of-the-art contextualized language models with their newly gained insights. The system is available online at https://fish-web-fish.de.r.appspot.com/, and a short video for introduction is at https://youtu.be/ZbvZQ09i6aw.