Venue: NAACL 2022

# Identifying Corporate Credit Risk Sentiments from Financial News

 Noujoud Ahbali\({}^{*}\), Xinyuan Liu1 , Albert Aristotle Nanda2

Jamie Stark, Ashit Talukder, Rupinder Paul Khandpur

Moody's Analytics, 7 World Trade Center, New York, NY 10007, USA

{noujoud.ahbali, xinyuan.liu, albert.nanda jamie.stark, ashit.talukder, rupinder.khandpur}@moodys.com

Footnote 1: These authors contributed equally to this work

###### 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.

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Footnote †: July 10-15, 2022 ©2022 Association for Computational Linguistics

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Footnote †: July 10-15, 2022 ©2022 Association for Computational Linguistics

## 1 Introduction

**Motivation.** Historically, financial institutions performed credit risk management with techniques based on two different approaches Chatterjee (2015). The first approach is structural models, based on Black and Scholes (1973) and Merton (1974), 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 Jarrow and Turnbull (1995) and Grundke and Riedel (2004), which measure the default event as a statistical process, a random event following Poisson law, without considering the company's assets or liabilities.

These historic methods focus primarily on assessing the probability of default, which is useful in credit risk management. However, they are not designed to gain insights about a company's credit overall situation or identify the negative and credit adverse events the company has experienced or is likely to experience. This task falls under the responsibility of financial experts who may rely on news to identify such events, but this activity is considered highly tedious and time-consuming. Moreover, companies are increasingly covered in the press and journalists nowadays not only report facts, but go beyond in their analysis by making predictions, releasing warnings as well as establishing connections between companies.

* credit downgrade in Tran-The (2020). However, none tackles news analysis automation and uses

Figure 1: A screenshot of our deployed application.



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, target-specific 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 Contributions.** The key contributions of this paper are:

* A novel, data-driven approach to detecting credit adverse events with targeted-entity sentiments
* A custom credit scoring methodology for companies from news, traditionally performed by financial experts.
* Extensive experimentation on real-world data on which our modeling approach performs well: including studies for defaulters VS nondefaulters and analysis of the discriminatory power of CSS between defaulters and nondefaulters.

## 2 Related Work

### Aspect-Level Sentiment Analysis

When scientists prepare fine-grained sentiment models, they usually tackle the tasks of Aspect-based sentiment analysis (ABSA) Do et al. (2019) and Targeted ABSA (TABSA) Ma et al. (2018), where the latter considers the sentiment regarding a specific entity. Researchers have added context-dependencies to pretrained self-attention based language models called QACG-BERT Wu and Ong (2021) to improve the performance better. A mutual learning framework is used to take advantage of unlabeled data to assist the aspect-level sentiment-controllable review generation, consisting of a generator and a classifier that utilize confidence mechanism and reconstruction reward to enhance each other Chen et al. (2021).

### Deep Learning in Text Sentiment Analysis

A RNN model with LSTM units is trained based on Glove Embeddings of 400K words to predict the polarity (i.e., positive or negative sentiment) of the news Souma et al. (2019). Moreover, an ensemble of CNN Kim (2014), LSTM Hochreiter and Schmidhuber (1997) and GRU Chung et al. (2014) and a classical supervised model based on Support Vector Regression (SVR) is constructed which performs impressively on Microblog Twitter and StockTwits) and news headlines datasets Akhtar et al. (2017). Researchers have found that CNN is an effective model for predicting the sentiment of authors in the StockTwits dataset, among other models of logistic regression, doc2vec, and LSTM Sohangir et al. (2018). A BERT model for the financial domain (FinBERT) pre-trained on a financial corpus and fine-tuned for sentiment analysis has shown promising results Araci (2019).

### Machine Learning in Credit Risk

A study has shown that tree-based models are more stable than the models based on multilayer artificial neural networks in predicting loan default probability with structural features of financial conditions of a company Addo et al. (2018). In addition, researchers have provided further evidence that regardless of the number of features used, boosted models outperform Linear Models, Decision Trees, and Neural Networks Torrent et al. (2020). Further studies have stated that deep learning lends itself particularly well to analyzing textual data, but the improvement on numerical data is limited compared to traditional data mining models Mai et al. (2019). Regarding Micro, Small, and Medium Enterprise (mSME) credit risk modeling, deep learning models, including the BERT model, appear to be robust concerning the quality of the text and therefore suitable for partly automating the mSME lending process because of their power to predict default based on textual assessments provided by a lender Stevenson et al. (2021). In this study TranThe (2020) a more NLP-focused approach is taken, using a combination of topic modeling and sentiment lexicons 



## 3 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 NewsEdge1) 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.

Footnote 1: [https://newsedge.com/](https://newsedge.com/)

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.

### News Enrichment Pipeline

In the pipeline, news articles are enriched with the output of the three following models.

**Credit Relevance Model.** We define credit-relevant news as any news story that contains business & finance-related topics which mention one or more corporate entities. We trained a binary relevance classification model using news data to identify relevant news. We leveraged the Reuters 2 news topics classification system, where we mapped Reuters codes into two classes: (1) in-domain (such as Merger/Acquisition, Sales, and promotions) and (2) out-of-domain (such asArt, Sports). In Table 1 we show the label distribution in both the train and test sets for the model.

Footnote 2: [https://liaison.reuters.com/tools/topic-codes](https://liaison.reuters.com/tools/topic-codes)

After the text pre-processing (removal of Html links, numbers, and stop words removed), it was used with TF-IDF weighted features [4]. Due to the train set size of over 30 Million articles, we chose a linear Support Vector Machine (SVM) model, trained with stochastic gradient descent (SGD) in out-of-core learning [6] setup.

**Target Entity Sentiment Model.** The raw documents are first split into sentences using syntok 3 and then on each sentence a pre-trained WordPiece tokenizer [8] is applied. Finally, each sentence is represented as \(\{t_{1},t_{2},\ldots\}\) and the corresponding case tags \(\{t^{c}_{1},t^{c}_{2},\ldots\}\). Token case tags used in the model are described in Table 2. Then given this sequence \(\{t_{1},t_{2},\ldots\}\), we feed it to pre-trained Electra Base model4 (Clark

\begin{table}
\begin{tabular}{l l l} \hline \hline Label & Train set & Test set \\ \hline Relevant & 13,323,062 & 3,291,751 \\ Not Relevant & 10,442,654 & 2,647,689 \\ \hline Total & 23,765,716 & 5,939,440 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of annotated dataset for Credit Relevance model.

Figure 2: Annotated sentences by Credit Relevance, Target Entity Sentiment and Risk Categorization models.



et al., 2020) to obtain contextual embeddings for each token \(\{e_{1},e_{2},\ldots\}\). As shown in Figure 3, the contextualized embeddings are concentented with case embeddings \(\{e^{c}_{1},e^{c}_{2},\ldots\}\) and fed to a linear layer to obtain the labels \(\{\hat{y_{1}},\hat{y_{2}},\ldots\}\). To compute the loss, we used masked cross-entropy. And a dropout layer for regularization was added as well. The network was optimized using AdamW (Loshchilov and Hutter, 2019) optimizer.

To annotate data, each sentence was shown to 5 analysts, and those with majority consensus were selected; those with no clear majority were discarded. In Table 3 we show the overall distribution of labels across our final annotated dataset of 9,859 unique sentences, with an 80:20 split for training and evaluating the model.

**Risk Categorization Model.** With similar pre-processing steps as inputs to the TES model, we trained a multi-label classification model, with an pre-trained Electra base model5Clark et al. (2020), followed by convolutional layers Kim (2014) and a linear layer. We also used dropout to reduce overfitting and a sigmoid layer to generate the final prediction output. The final tuned hyperparameters for the model are listed in Table 7 in Appendix. We trained the model with different model architectures and hyperparameters over 30 epochs in each model training iteration and saved the best epoch on the test set. Our team of 4 annotators (among the paper's authors) was engaged in data labeling and cross-review activities for more than 60 hours to build the dataset. A tagging guideline was first discussed and agreed upon with explicit definitions of the seven labels. The risk categories labels and examples are listed in Table 4. Around 7000 sentences were collected and labeled according to the tagging guidelines to form the train and test sets (using stratified sampling). The distribution of labels in the train and test sets are listed in Table 8 in Appendix.

Footnote 5: [https://huggingface.co/google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator)

**Credit Risk Scoring Model.** Each company is scored daily using credit adverse news articles for the company, as tagged by Risk Categorization Model.

**Step 1**: For each date, we calculate the category weights \(w_{cat}^{date}\) over a fixed window of days. This is done by counting the number of articles in each category and using an exponential decay so more recent counts have more weights, as shown in Formula 1.

\[w_{cat}^{date}=\sum_{i=from}^{date}\mathrm{count}_{cat}^{i}*e^{( date-i)/k} \tag{1}\]

where:

\begin{tabular}{l l l} \(from\quad\) & = start date of the fixed window used \\  & & for the calculations \\ \(\mathrm{count}_{cat}^{i}\) & = count of all articles found for a \\  & & given category (cat) on day (i) \\ \(k\qquad\) & = decay constant \\ \end{tabular}

**Step 2**: For each date, we calculate the category scores \(score_{cat}^{date}\) by transforming the weights using a sigmoid function, which has the effect of capping the weight and also ensuring that only one or two articles mentioned will have limited impact.

Figure 3: Architecture of Target Entity Sentiment Model.

 

We multiply by a fixed score for that category as described in equation 2.

\[{\rm score}_{cat}^{date}={\rm fixed}_{cat}/(1+e^{-m*(w_{cat}^{date}-c)}) \tag{2}\]

where:

\begin{tabular}{l l} \(m\) & = steepness of sigmoid function \\ \(c\) & = number of articles needed to reach \\  & the midpoint of sigmoid function \\ \({\rm fixed}_{cat}=\) & fixed score for a given risk category \\ \end{tabular}

The more severe the credit event is, the higher the fixed score is, as shown in Table 4.

**Step 3**: The Credit Sentiment Score at date \(t\) is the maximum category scores:

\[CSS^{date}=max({\rm score}_{cat}^{date}) \tag{3}\]

Our scoring function has an exponential decay which recognizes that news has a lasting value and impact during a specific period. It is reactive to the latest news as it weights recent news higher than older ones. The risk scores in the Credit Risk Scoring model are calculated via heuristics, as we do not have enough training data for a supervised approach. The fixed category score of each risk category in the Credit Risk Scoring model is shown in Table 4.

## 4 Evaluation

This section regroups the models evaluation as well as examples of case studies conducted on real-world 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 \(CSS^{TM}\) 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 default-related events, respectively, while the Adverse News model deals with other credit events such as credit rating downgrade and illiquidity.

\[(C*m_{t}+\sum_{i=1}^{n}{\rm article}_{i}^{T})/(C+n) \tag{4}\]

where:

\begin{tabular}{l l} \(C\) & = average number of articles per day \\  & in the last 10 days \\ \(m_{t}\) & = historical daily score mean in last \\  & 10 days \\ \(n\) & = number of articles in day t \\ \({\rm article}_{i}^{T}\) & = i-th article score on day t \\ \hline \end{tabular}

\begin{table}
\begin{tabular}{p{56.9pt} p{87.1pt} p{113.8pt} c} \hline \hline Risk Category & Definitions & Example Sentences & Fixed Score \\ \hline Bankruptcy / Insolvency & Proceedings of bankruptcy, insolvency or foreclosure, mentions of restructuring, administration or refinancing due to liquidity issues. Any mention of unpaid debts by a entity or the prospect of default for an entity. Downgrades from rating organizations. Revenue, sales or EPS fall. & The British firm filed for Chapter 7bankruptcy protection late Thursday. & 100 \\ Default / Missed Payments & Any kind of financial crime, investigations, lawsuits, or violations. Any type of company or credit relevant risks not covered in one of five risk categories above. Any text that is not evolved with credit risk. & Levitt home-building unit gets loan default notices. Standard Chartered’s Shares Plunge 7\% After Fitch Downgrade. Carillion has been fighting for survival after contract delays and a drop in new business led to three profit warnings last year. TransAtlantic Petroleum Announces Notice of Noncompliance With NYSE MK Continued Listing Standards. On June 28, 2017, Southern Company and its subsidiary, Mississippi Power, suspended operations involving the coal gasifier portion of the Kemper County energy facility. Marks \& Spencer to issue its first junk bond after reporting its first loss since joining the stock market in 1926. & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Risk Categories definitions with examples and weights in entity scoring 

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 Hochreiter and Schmidhuber (1997). And the Adverse News model is an LSTM model with attention mechanisms Bahdanau et al. (2015) 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 Table 4) for our latest version of the Risk Categorization Model. During the inference stage, each article is split into paragraphs fed to the three event relevance models. The paragraph score is the maximum score of the three relevance models, and the article score is the maximum score of all the paragraph scores within the article. Since Bankruptcy events are the most severe events while Adverse News are the least severe ones, we have applied weightings to the article scores of the three events with 100%, 75%, and 50%, respectively. At the company level, related articles are scored and are aggregated using a bayesian averaging, as shown in equation 4, to generate the company's daily sentiment score.

**Models Evaluation.** In Table 5 we show the classification report for the Credit Relevance Model on the test set (an overall F1-Score of 87%).

As reported in Table 6 (detailed report in Table 10 in Appendix), the overall F1-Score of Target Sentiment Model on the test set is 77%, which shows best performance for ORG (Organization), the most relevant entity for our purpose. As reported in Table 6 (detailed report in Table 9 in Appendix), the overall F1-Score of Risk Categorization Model on the test set is 83%. We also notice better results for three of the four major credit events in the Credit Risk Scoring Model (Bankruptcy/ Insolvency, Credit Rating Downgrade, and Profit Warning).

The weights of risk categories in the Credit Risk Scoring model indicate the importance of the related credit events. That analyzes a company's creditworthiness. It coincides with the fact that we have better classification results in the Risk Categorization Model for the credit events that contribute with higher weights in the Credit Risk Scoring Model. As for Default / Missed Payments risk, its performance is close to the average performance.

To validate that our scoring model picks up credit adverse events for more than 6000 companies, we collect 40,000 negative articles over two years (2016 -2018) and corresponding default dates of defaulters. Of these companies, 1192 experienced a severe credit event (Bankruptcy/Insolvency or Default/Missed Payments), and the remaining became our control group. We refer to the former as defaults and the latter as non-defaulters. We further filtered companies based on their newsworthiness to keep the ones with at least an article per month on average. In the end, the defaulters' group contains 1166 companies, whereas the non-defaulters have 3009 companies.

In Figure 4 we show the daily average CSS of the companies a year before and after the credit event (represented as the "0" date on X-axis). For comparison, we show the average score for the control group. The event dates for non-defaulters are chosen randomly during the same period as defaulters. The average CSS moves away from the long-term average towards the credit event. At

\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & Precision & Recall & F1 & Support \\ \hline TES & 76\% & 79\% & 77\% & 5391 \\ RiskCat & 83\% & 82\% & 83\% & 2146 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance (micro average) results for Targeted Entity Sentiment (TES) and Risk Categorization (RiskCat) model results.

Figure 4: CSS Comparison between defaulters and non-defaulters



around three months before the credit event and until five months afterward, the score is around two times compared to the non-defaulters average. The peak of the defaulters after default events is around 35 after taking the average within the defaulters' group. However, not around 80 as in an individual company when a default event happens. Still, in Figure 4 we clearly distinguish defaulters and non-defaulters around default events by their average credit sentiment scores.

Additionally, to validate the discriminatory power of CSS to identify the default and non-defaulting companies, we ran the following statistical tests. With _Kolmogorov-Smirnov_ test (Massey Jr, 1951), we observed the Credit Sentiment Scores of the two groups (defaulters and non-defaulters) were statistically different, with a confidence level of 95%. Meanwhile, a _Mann-Whitney U_ test (Nachar, 2008) showed that the probability of a defaulter's score is more significant than a non-defaulter's score (both selected randomly from the two groups) is statistically higher than 50%, with a confidence level of 95%.

**Case Studies.** To illustrate, we compared our CSS model to the baseline for defaulters and non-defaulters. As shown in Figure 5, CSS for Interserve PLC reacted to an early credit adverse signal (driven by Profit Warning and Default/ Missed Payments) stronger compared to the baseline a year before the company was set for administration. Later, the news picked up a strong Bankruptcy / Insolvency signal as the company was seeking a rescue deal before it was set into administration.

In another example, in Figure 6 we show a consistently low CSS (as expected for the company as it is a non-defaulter company) compared to the baseline. This result is due to the baseline system noise, as the articles often mention Air Lease's partners going into liquidation and insolvency issues. The result also shows that misclassifications on the paragraph level are way noisier than at the sentence level. A paragraph may have multiple sentences which refer to different companies with different sentiments in different contexts.

## 5 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.

Figure 5: CSS and Baseline - INTERSERVE PLC.

Figure 6: CSS and Baseline - AIR LEASE.

 



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## Appendix

### Risk Categories Model

Risk Categorization model final set of hyperparameters are shown in Table 7.

In Table 8 we show the distribution of the model's annotated dataset (train and test sets) along with the detailed classification report on the test set in Table 9.

### Target Entity Sentiment Model

The primary entity label that our pipeline relies on is - _Organization_ (Org) as our focus is on corporate entities. Accurately discerning the sentiment polarity (Pos, Neg, Neu) of these target Organizations is an essential requirement of the pipeline, and in the Table 10 we highlight the F1 score on these three classes.

\begin{table}
\begin{tabular}{l l c c} \hline \hline Entity Type & Precision & Recall & F1-Score & Support \\ \hline Money & 94\% & 96\% & 95\% & 502 \\ Neg Loc & 57\% & 32\% & 41\% & 25 \\ Neg Misc & 36\% & 30\% & 33\% & 30 \\
**Neg Org** & **66\%** & **70\%** & **68\%** & **514** \\ Neg Per & 72\% & 67\% & 69\% & 220 \\ Neu Loc & 85\% & 89\% & 87\% & 890 \\ Neu Misc & 71\% & 76\% & 73\% & 676 \\
**Neu Org** & **74\%** & **79\%** & **77\%** & **1612** \\ Neu Per & 78\% & 80\% & 79\% & 518 \\ Pos Loc & 46\% & 24\% & 32\% & 25 \\ Pos Musc & 44\% & 44\% & 44\% & 27 \\
**Pos Org** & **66\%** & **69\%** & **67\%** & **298** \\ Pos Per & 54\% & 70\% & 61\% & 54 \\ \hline Micro Avg & 76\% & 79\% & 77\% & 5391 \\ Macro Avg & 65\% & 64\% & 64\% & 5391 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Detailed Targeted Sentiment results.

\begin{table}
\begin{tabular}{l l l} \hline \hline Risk Category & Train set & Test set \\ \hline Profit Warning & 688 & 329 \\ Bankruptcy / Insolvency & 853 & 372 \\ Compliance Issue & 326 & 161 \\ Default / Missed Payments & 596 & 309 \\ Credit Rating Downgrade & 426 & 204 \\ Other Risk & 1347 & 544 \\ Not Relevant & 596 & 227 \\ \hline Total & 4832 & 2146 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Tuned Hyperparameters in Risk Categorization Model.

\begin{table}
\begin{tabular}{l|l l l l} \hline \hline Labels & Precision & Recall & F1-Score & Support \\ \hline
**Profit Warning** & **86\%** & **89\%** & **87\%** & **329** \\
**Bankruptcy / Insolvency** & **93\%** & **94\%** & **94\%** & **372** \\ Compliance Issue & 81\% & 60\% & 69\% & 161 \\
**Default / Missed Payment** & **79\%** & **83\%** & **81\%** & **309** \\
**Credit Rating Downgrade** & **95\%** & **95\%** & **95\%** & **204** \\ Other Risk & 75\% & 76\% & 75\% & 544 \\ Not Relevant & 79\% & 68\% & 73\% & 227 \\ \hline Micro Avg & 83\% & 82\% & 83\% & 2146 \\ Macro Avg & 84\% & 81\% & 82\% & 2146 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Detailed Risk Categories results.