SURF : A System to Unveil Explainable Risk Relations between Firms (2025)

Abstract: 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.

## introduction and related work financial markets are shaped by many factors, including market conditions and regulatory policies. the intricacies of these influences necessitate expert analysis to assess their impacts accurately. a critical component of such analysis is understanding the strength of risk relations between firms, a key to supporting investors and financial professionals in making well-informed decisions. risk relations refer to the connections between two companies based on shared risk exposure. marriott and hilton, for example, have strong risk relations because they face similar risks, such as competition from other hotel operators and the occurrence of disasters. traditionally, financial analysts manually review extensive financial reports to identify these relations. however, this approach is time-intensive and prone to subjective bias, making efficiently conducting large-scale analyses significantly challenging. previous studies have used deep neural networks to classify relations or predict binary links between firms (wichmann et al., 2020; kosasih and brintrup, 2022). however, these methods often lack interpretability or fail to quantify the strength of relations. in the field of finance, explainability is crucial to users' trust, highlighting the need for systematic approaches that improve transparency and demystify the "black box" nature of these models (bracke et al., 2019; hoepner et al., 2021). to measure and explain the strength of risk relations between firms, we present an interactive system that extracts information from key risk-related sections of form 10-k filings-specifically "item 1. business," "item 1a. risk factors," and "item 7a. quantitative and qualitative disclosures about market risk"-to provide comprehensive insights into inter-firm risk relations. @xcite these sections typically use lexically similar terms, cover related topics, and often detail risk events along with their dates. building on these characteristics, we design a custom encoder and implement a novel retrievalbased approach to identify chronological and lexical similarities among the paragraphs. we define "mutual risk paragraphs (abbreviated as mrps, hereafter)" as those discussing similar risks and compute a risk relation score between two firms based on the proportion of mrps they share. our system computes and explains these scores by summarizing the identified mrps using large language models (llms), enabling users to interactively explore and intuitively understand insights about shared risks between firms. by simultaneously presenting the llm-generated summaries alongside factual paragraphs from 10-k filings, surf further mitigates the risks of hallucination and factual inconsistencies. meanwhile, user feedback is collected and incorporated to dynamically enhance the rationale explainer. the system's ability to reveal latent relations is further validated through experiments and a proposed metric. a conceptual overview of surf's contribution is presented in figure 1. user setting module this module (labeled as partition (1) in figure 2 ) enables users to customize the firm-to-firm network graph through an intuitive side panel. here, users can select the target year, choose up to three companies for analysis, and specify the number of closely related companies to display. after configuring these settings, users can click the "submit" button to rerender the graph based on the seleceted criteria. for example, figure 2 illustrates the top 10 relations of alphabet inc. and meta platforms. note that the graph dynamically adjusts to display only the relations that meet the selected parameters. users may also reset the settings to their default state by clicking the "reset" button, which reverts to a graph showing the relations among the top 50 firms by market capitalization. graph visualization module this module (labeled as partition (2) in figure 2 ) features an inter-active network graph at the center of our system. each firm is represented as a node, while the edges signify the existence of relations between firms. key visual elements include thicker edges reflecting stronger relations and larger nodes representing firms with higher market capitalizations. users can hover over a node to view the name of the company it represents. the system categorizes firms by sectors using different colors, as outlined in the graph legend. @xcite selected edges are highlighted to improve visibility, allowing users to focus on specific connections. the 3d graph visualization further enhances user interactivity, enabling them to customize the view to their preferences using the control instructions displayed at the bottom center of the screen. in figure 2 , the edge between alphabet and meta in the graph is highlighted in red, and details of their relationship are displayed in the adjacent right panel, offering a comprehensive understanding of the selected connection. relation rationale explainer this module (labeled as partition (3) in figure 2 10-k filings. @xcite users can explore these mrps further by clicking on a paragraph to view similar risk discussions from the other firm. to enhance user experience and minimize response time, a predefined prompt is employed (see appendix a) to guide the large language model (llm) in generating concise summaries of the rationale behind the relations. these summaries highlight shared risks and explain the connection between the selected firms for the given year. figure 2 illustrates a summary that outlines shared risks and their relevance to the selected firms. we use @xcite based on the gpt-4o-mini architecture, foot_5 to generate summaries due to its high performance and cost efficiency. the initial prompt instructs the model to summarize the risks faced by the two firms using their mrps, highlight key sections in the summary @xmath0, and generate three keywords from the summary to clarify the shared risks. the initial prompt is not static. inspired by (sun et al., 2024), we integrate a feedback mechanism to continuously improve the quality of the generated rationales and enhance user experience. specifically, positive and negative feedback on each rationale is collected and analyzed by the llm to identify reasons for inaccuracies. based on this analysis, the prompt is refined to better align with user expectations. @xcite this dynamic feedback loop allows surf to iteratively adapt the prompt, improving both clarity and relevance over time. ## relation identification dense encoder training we aim to fine-tune an encoder capable of producing higher cosine similarity scores for paragraphs discussing mutual risks. however, annotating such paragraphs requires specialized financial expertise and the knowledge of the target companies. identifying shared risks is particularly challenging due to the ambiguous language often used in regulatory documents. even when relevant paragraphs are identified, building and scaling up a supervised dataset remains a significant challenge. to this end, we adopt a self-supervised approach using contrastive learning (hadsell et al., 2006; gao et al., 2021). specifically, we create two distinct views to fine-tune a bert model (devlin et al., 2019) for retrieval and build a self-supervised dataset from sec filings. these views are designed to capture both chronological and lexical representations of the documents, as detailed below. 1. chronological similarity view: we assume that after excluding accounting-related dates, a single firm experiences only one significant event on any given day. for example, the upper section of positive pairs are generated from overlapping text segments within the same paragraph. for a paragraph with n words [w 1 , w 2 , . . . , w n ] where @xmath1, we randomly select indices i and j such that 1 < @xmath2, . . . , w j ], [w i , . . . , w n ]). for each view, we generate 8,500 positive pairs for training and 1,000 positive pairs for validation. to optimize the training process, we employ the in-fonce loss with in-batch negatives (van den oord et al., 2019; karpukhin et al., 2020). training is halted early if there is no improvement in loss after five epochs. additionally, the output vectors are normalized to unit length to facilitate the computation of cosine similarity between paragraph vectors, thereby aiding in the identification of mutual risks. ## case study to demonstrate surf's ability to uncover latent relations, we present a case study on nvidia (nvda), a leader in high-performance gpus and ai processing, and wabtec (wab), a pioneer in advanced rail and transit solutions. in 2022, surf identified a strong risk relation between these companies, ranking in the 95th percentile among 63,190 analyzed relations. while this connection may seem unexpected, a deeper analysis reveals that surf detected underlying links, particularly concerning supply chain disruptions. one key risk highlighted in the llm-generated summary indicates that the covid-19 pandemic exacerbated operational disruptions for both firms, notably through supply chain constraints. similar insights appear in their mrps, reinforcing this shared risk which is further supported by news reports in 2022: • reuters reported that nvidia was impacted by the global chip shortage which kept gpu prices high. as supply constraints eased and prices fell, analysts viewed it as a negative market signal that would lead to a decline in nvidia's stock. foot_10 • in q1 2022, an equity research institution noted that "supply-chain disruptions (including higher commodity costs and shortages of components, chips, and labor) might have also dampened wabtec's first-quarter performance." foot_11 these events underscore the critical role of chip supply chain disruptions as a shared risk factor for both companies and validate surf's effectiveness in identifying hidden interdependencies. ## conclusions in this paper, we introduce surf, a system designed to visualize, quantify, and interpret the risk relations between companies. surf provides users with an intuitive interface to understand how companies are connected through shared risks, why these risks exist, and how they may impact both companies. both experts and non-experts can leverage surf to make more informed decisions. beyond the system design, our technical contribution lies in the development of an encoder that effectively extracts latent information from the text of financial reports. in our experiments, the encoder consistently outperforms all baseline methods. this technical advancement highlights surf's ability to unravel the complexities of financial documents, making them more transparent and accessible to a broad spectrum of users. future work we leverage 10-k filings as a robust and reliable data source. these filings are audited and provide comprehensive narratives of events with material impacts, offering a strong foundation for our analysis. to further enhance the scope of our work, we plan to integrate additional data sources, such as news articles and analyst reports, to complement the 10-k filings and provide users with more timely and diverse insights. in addition, our method and system open new avenues for future research. for example, the practice of disclosing important dates in reports is not unique to the finance field. the chronological similarity view we developed has the potential to discover connections/relations between entities (e.g., geopolitical risks) across various types of corpora. similarly, the rationale explainer in surf can be adapted to other systems to enhance transparency in complex or opaque content. we aim to build on our current system and methodology to expand their application to different fields, fostering broader interdisciplinary insights into future research.