Modeling and Detecting Company Risks from News (2024)

Abstract: Identifying risks associated with a company is important to investors and the wellbeing of the overall financial markets. In this study, we build a computational framework to automatically extract company risk factors from news articles. Our newly proposed schema comprises seven distinct aspects, such as supply chain, regulations, and competition. We annotate 666 news articles and benchmark various machine learning models. While large language mod- els have achieved remarkable progress in various types of NLP tasks, our experiment shows that zero-shot and few-shot prompting state-of- the-art LLMs (e.g., Llama-2) can only achieve moderate to low performances in identifying risk factors. In contrast, fine-tuning pre-trained language models yields better results on most risk factors. Using this model, we analyze over 277K Bloomberg News articles and demonstrate that identifying risk factors from news could provide extensive insights into the operations of companies and industries.

## introduction risks are inherent and pervasive within companies' operations and our society @xcite @xcite . understanding and identifying corporate risk factors could benefit diverse stakeholders, including investors, regulators, and other relevant entities. notably, publicly listed companies are mandated to disclose their risk factors, as these can inform shareholders and the public when making financial decisions @xcite . nlp models are also built to automatically extract companyrelated risk factors from public filings, providing consolidated and accessible insights for analysts to fathom and integrate these factors @xcite . while company filings offer a systematic view of company-related risks, they are beset by three * work done as an intern at bloomberg. principal issues: (1) limited frequency-owing to mandatory quarterly reporting, risk analysis is confined to three-month intervals, disregarding the reality of swift, even daily, alterations in a company's risk profile. (2) subjectivity-authored by internal personnel, company filings might inadvertently omit pivotal risk factors due to vested interests @xcite . (3) bias towards public entities-only publicly listed companies are obligated to divulge risks through filings, neglecting the imperative to comprehend risks associated with private companies, which may be particularly pertinent when engaged in financial activities such as bond issuance @xcite . to redress these limitations, we propose to model company-related risk factors from news articles. news articles offer the following merits in analyzing company-related risk factors: (1) high frequency-news updates occur in real time, providing a dynamic information stream conducive to measuring companies' risk factors in the everevolving market. (2) external perspective-news articles, devoid of company affiliations, proffer diverse viewpoints, shedding light on risk factors from external vantage points. (3) coverage over both public and private companies-news articles encapsulate both publicly listed and private companies, thereby bridging the information gap present in public filings. while there are existing studies on modeling risk factors, they typically focus on company filings @xcite and their categorization may not be directly applicable to news data. combining existing literature and our manual examination of hundreds of news articles, we propose a novel theoretical framework to analyze company risk factors in news. our taxonomy encompasses seven categories of risk factors: supply chain and product, people and management, finance, legal and regulations, macro, competition, and markets and consumers. we annotate 666 news articles from bloomberg news foot_0 and benchmark a series of models from feature-engineered baselines to prompting large language models (llms). despite their impressive results on various other nlp tasks, llms perform worse than smaller transformer models (e.g., roberta) fine-tuned on in-domain data. by applying the best-performing model to a large sample of 277, 112 news articles, we analyze the risk factor across companies in given industries, and across the entire macroeconomy. our analysis shows that modeling company-related risk factors could reveal important signals of not only companies' operations but also can be used as the indicator of the macro-level risk for society. ## theoretical framework in this study, we focus on modeling company-level risk factors in news articles. we survey existing literature ( § 2.1) and qualitatively examine hundreds of news articles. our proposed framework is summarized as below. supply chain and product risks associated with the company's supply chain, manufacturing, product or core technology. for example, "yum china faces challenges with chicken prices" indicates risks regarding the supply chain, as chicken is an important ingredient in yum china's products. people and management risks regarding a company's internal operations such as layoffs, departures of top management, or specific operation strategies. for example, "tesla pauses hiring, musk says need to cut staff by 10%." finance risks related to the finances of a company such as cash flow, fund procurement, investments, and profits. for example, "nio shares soar as loss shrinks, though cash concerns linger." legal and regulations risks induced by potential policy changes, pressure from regulations or lawsuits. for example, "maple leaf plunges as china's hog suspension impacts profits." macro risks caused by the macro socioeconomic environment such as inflation, pandemics or a financial crisis. for example, "absa drops on profit miss as south african economy struggles." markets and consumers risks or challenges from the market or consumer sales. for example, "hong kong protests cut demand for hilton, hyatt hotel rooms" suggests that the demand for hotel space is shrinking, which indicates markets and consumer risk for both hilton and hyatt. competition risks from a company's competitors in the market. for example, "apple revamping smart-home efforts to challenge amazon, google." ## data and annotation bn dataset we draw five years of articles published by bloomberg news foot_1 (hereafter bn), covering diverse events and opinions pertaining to companies across the world. we choose news published between 2018 and 2022 to allow for covidrelated comparisons. this initial dataset is filtered by removing machine-generated and non-natural language content, and is preprocessed with a rulebased entity extraction pipeline. we further removed articles where no company is mentioned. this results in a collection of 277, 112 news articles covering 14, 972 public and 11, 413 private companies. for the sake of simplicity, we keep only the headline and first five sentences of each article for our study. these articles range from 20 to 4, 430 tokens long, with the average article being 151 tokens. risk-related pre-filtering our pilot study shows that risk factors can be sparse in news. directly annotating over a random subset of bn articles will therefore yield a very high negative ratio. we apply a lexicon-based filter before the sampling. concretely, we iteratively curate 53 unigrams to capture various aspects of risk events, such as "challenge," "layoff," "shrink." 3 we consider annotating an article only if its headline matches at least one of the keywords. we also experimented with hedges similar to @xcite . however, we found that while hedges are a good proxy for uncertainty, they are not able to reliably recall news articles regarding risks, highlighting the difference between uncertainty and risk factors. annotation we conduct an annotation study based on the seven risk factors mentioned in section 3, using a multi-label classification setting. we hire three u.s.-based annotators who are experienced in the finance domain. they are first instructed to label 100 articles independently, followed by a discussion to resolve disagreements and make modifications to the annotation guideline. the adjudicated set is used as test data. they further annotate 200 articles each. after removing samples with wrong mentions or lowquality text, the final dataset includes 716 samples from 666 unique news articles. in this dataset, 49% of the samples have exactly one label, while more than 20% mention multiple factors 4 . in figure 1 we show the distribution of risk factors along with the number of news without any risk factors ("no- risk"). supp mgmt fin legal macro comp mrkt supp n/a 8 6 12 17 8 19 mgmt 8 n/a 15 16 12 1 12 fin 6 15 n/a 8 22 0 9 legal 12 16 8 n/a 11 4 8 macro 17 12 22 11 n/a 3 31 comp 8 1 0 4 3 n/a 11 mrkt 19 12 9 8 31 11 n/a statistics to better understand the characteristics of our newly annotated dataset, we first show the distribution of industry sectors to which the detected companies belong. we match each company to one of the 12 high-level industry sectors defined by the bloomberg industry classification standard (bics) 5 . as shown on the right side of figure 1 , our dataset contains samples over all of these sectors. the distribution of risk types differs across industry sectors. for instance, there are more market related risks for companies in "consumer discretionary," while more "legal" risks are mentioned for companies in the "financials" and "communications" industries. for "real estate", the majority of the risks fall under finance. in figure 2 we further illustrate the co-occurrence of risk factors. notably, we observe higher cooccurrence of (finance, macro) and (market and macro) pairs. ## benchmark we formulate the risk prediction task as a multilabel classification problem: given a news article and a mentioned company, we aim to predict whether each of the seven risk factors is mentioned. we consider non-neural baseline models, fine-tuning pre-trained transformers, and largelanguage models (llm) with in-context learning. we split the dataset into 484 samples for training, 126 for validation, and 106 for testing. ## application and analysis identifying company-related risk factors in news opens many potential applications. in this section, we explore the applications of our model over a large-scale bloomberg news dataset. the term "risk" inherently carries negative connotations. in practice, are risk factors simply negative sentiment? in this study, we explore the connection between company-level sentiment and risk factors. we run an off-the-shelf sentiment analysis model foot_7 over the large 5-year bn dataset. for each company mentioned in a news article, a probabilistic distribution over "positive," "neutral," and "negative" is estimated. as shown in figure 5 , the overall sentiment for a company tends to be more negative when it faces risks. the largest gap of sentiment occurs for legal and regulations and markets and consumers, where risks are usually mentioned with negative sentiment. nevertheless, risk factors can be mentioned even when the overall sentiment regarding a company is neutral or positive (see