I dentifying C orporate C redit R isk S entiments from F inancial N ews (2022)

Abstract: Credit risk management is one central practice for financial institutions, and such practice helps them measure and understand the inherent risk within their portfolios. Historically, firms relied on the assessment of default probabilities and used the press as one tool to gather insights on the latest credit event developments of an entity. However, due to the deluge of the current news coverage for companies, analyzing news manually by financial experts is considered a highly laborious task. To this end, we propose a novel deep learning-powered approach to automate news analysis and credit adverse events detection to score the credit sentiment associated with a company. This paper showcases a complete system that leverages news extraction and data enrichment with targeted sentiment entity recognition to detect companies and text classification to identify credit events. We developed a custom scoring mechanism to provide the company’s credit sentiment score ( CSS^{TM} ) based on these detected events. Additionally, using case studies, we illustrate how this score helps understand the company’s credit profile and discriminates between defaulters and non-defaulters.

## introduction motivation. historically, financial institutions performed credit risk management with techniques based on two different approaches @xcite . the first approach is structural models, based on @xcite and @xcite , which use the company's assets and liabilities to derive its probability of default. the second approach is default intensity models, also called reduced form models, developed by @xcite and @xcite , which measure the default event as a statistical process, a random event following poisson law, without considering the company's assets or liabilities. challenges. most of the available news data is unannotated and un-exploitable at its initial state, which requires a significant entry effort for machine learning experiments. furthermore, machine learning experiments in credit risk management has shown to boost accuracy in the default risk measure @xcite to show the effect of news sentiment on that same metric @xcite or to focus on a single event predictioncredit downgrade in (tran-the, 2020). however, none tackles news analysis automation and uses deep learning for credit event detection. our goals. in order to derive explainable knowledge about a company's credit risk, we propose automating news analysis and identifying signals of negative and credit adverse events for companies. such a method enables us to score the negative credit sentiment of companies. our approach is a complete deployed application as shown in figure 1 . the enrichment pipeline starts with news collection (in english). it outputs a credit sentiment score (css) for companies based on the severity, recency, and volume of negative and credit adverse events detected from financial news articles. the custom natural language processing (nlp) based pipeline's hallmarks include automated ingestion & filtering for finance-domain news articles, targetspecific entity sentiment extraction. this pipeline allows high-precision content filtering and classification of the negative and credit adverse events mentioned in news articles (classified into five risk categories). ## our approach in this section, we discuss different components of our scalable nlp pipeline that can ingest and infer english news from a news data source (moody's analytics newsedge foot_0 ) that has over 170m articles, with an average of 150k news articles daily volume. to efficiently process large volumes of data, we have designed a data funnel process. we have a credit relevance model at the head of the funnel, which helps discard irrelevant documents, viz. sports/technology-related articles. this model filters out 70% of the incoming documents. next, the target entity sentiment (tes) model extracts and tags all the entities in a document with positive, negative, and neutral sentiment polarity, respectively. following this step, the risk categorization model then classifies each sentence in the article into appropriate risk categories (discussed later in this section). in figure 2 we illustrate different examples of sentences as annotated by each model. ## evaluation this section regroups the models evaluation as well as examples of case studies conducted on realworld data. the baseline. a simplified set of baseline models consists of three event relevance (binary classification) models instead of the multi-label classification model in the risk categorization model: bankruptcy, default, and adverse news. this baseline method was actually in the earliest version of css t m product, where we only considered the two most severe credit risk events: bankruptcy and default, in addition to a general class of less severe events called adverse news. bankruptcy and default models handle bankruptcy and defaultrelated events, respectively, while the adverse news model deals with other credit events such as credit rating downgrade and illiquidity. each model outputs a score representing the prediction confidence about the underlying event from 0 to 100 for the input paragraph. the bankruptcy and default models are lstm models @xcite . and the adverse news model is an lstm model with attention mechanisms @xcite as these events are usually not as explicitly mentioned in the articles as the bankruptcy and default events. these three classes are only present in the baseline, and we have added new risk categories (as shown in ## conclusion in this paper, we have designed, implemented, and deployed a deep-learning/nlp-powered application. this application can assist credit analysts in processing large amounts of news data and detecting and understanding the negative and credit averse events for companies. the pipeline utilizes various machine learning and deep learning models for data filtering, entity recognition sentiment analysis, and text classification. as validated by the case studies and the modeling evaluation, the output sentiment score can distinguish between defaulted and non-defaulted companies. since we only expose the css product instead of the complete models to the public, we will guarantee the truthiness of our in-house news source so that the system cannot be misused by publishing fake news. we plan to use credit sentiment score as a signal to predict future credit events in future work. for example, given a company's credit sentiment score of a certain level, the probability that the target company will have some credit events within a certain period. we could also explore the sentiment analysis for positive credit events, aggregate company level scores into industry or region level, or focus on entities other than companies.