Predicting Corporate Financial Distress#
Title: Towards a More Robust Model for Corporate Distress Prediction: The Convergence of Textual Analysis and Traditional Indicators
Abstract#
Corporate credit risk prediction is a vital aspect of financial management, and traditionally, it relies heavily on structured financial data. However, this approach often overlooks the wealth of information contained in unstructured textual data. In light of this, we propose an integrated approach to credit risk modeling that combines traditional financial ratio analysis, structural credit risk modeling, and a novel text mining model. Our methodology first leverages financial ratios to evaluate a firm’s financial health. Next, we apply the structural credit risk modeling technique based on the Black-Scholes-Merton model, which interprets a firm’s equity as a call option on its assets. Lastly, our text mining model employs a two-step approach of filtering relevant text through topic modeling and analyzing sentiments using fine-tuned large language models. We integrate these three components to form a holistic model aimed at predicting corporate credit risk more effectively. The key strength of our approach lies in its capability to transform unstructured textual data into quantifiable credit risk scores, thereby unlocking unique insights into a company’s potential for default. Our research demonstrates the predictive power of unstructured data in credit risk modeling, contributing significantly to the literature on credit risk prediction and offering valuable insights to financial institutions and policymakers.
Introduction#
Understanding and predicting corporate credit risk is of paramount importance for financial institutions, investors, and policymakers. It affects lending decisions, investment strategies, and economic planning. Traditional approaches to credit risk assessment have predominantly relied on financial ratios and structural models, which draw upon firm-specific, structured data. However, these conventional methods often overlook a valuable source of information—unstructured textual data.
With the advent of machine learning and the widespread availability of large amounts of data, the analysis of unstructured data, such as news articles, credit rating agency reports, and management statements, has become increasingly feasible and relevant. This project seeks to harness these advances to improve upon existing methods for predicting corporate credit risk.
Our approach integrates traditional financial analysis, structural credit risk modeling, and text mining techniques to create a more holistic and accurate assessment of a company’s credit risk. Firstly, we utilize financial ratio analysis to evaluate a firm’s financial health based on its financial statements. We consider various ratios capturing profitability, leverage, coverage, liquidity, and growth, supplemented by industry-specific information and country or region effects.
Secondly, we draw upon the structural credit risk modeling approach, a concept derived from the Black-Scholes-Merton model, treating a firm’s equity as a call option on its assets. By assessing asset volatility, default points, and drift components, we estimate the probability of a company’s insolvency.
Thirdly, and most innovatively, we delve into the world of unstructured data with our text mining model. We deploy a two-step approach, including filtering relevant text through topic modeling and analyzing sentiments using fine-tuned large language models. This text mining model enables us to transform raw textual data into quantifiable credit risk scores, offering unique, timely insights into a company’s potential for default.
Ultimately, we integrate the results of these three models, appreciating the unique insights and strengths each approach provides. This integration creates a more comprehensive and accurate assessment of a company’s credit risk, giving us the ability to predict defaults more effectively.
We believe our work contributes significantly to the existing body of literature on credit risk modeling. Firstly, by demonstrating the effectiveness of combining different models and data sources. Secondly, by showing the practical feasibility and predictive power of using text mining in credit risk prediction. Lastly, by shedding light on the dynamics of credit risk, which may lead to more timely and effective interventions by investors and policymakers.
In summary, our research aims to advance the understanding and prediction of corporate credit risk by taking a comprehensive and innovative approach. We hope that our work inspires further research in this field and aids financial institutions in their risk management efforts.
Literature Review#
There is a wealth of literature available on the prediction of corporate default and credit risk management. Historically, such studies have typically relied on numerical financial data, using methods such as ratio analysis or market-based structural models. However, there has been growing interest in recent years in the potential of unstructured data sources and the application of advanced machine learning techniques.
One notable study in this area is that of Matin et al. [2019], “Predicting Distresses using Deep Learning of Text Segments in Annual Reports”. Their research goes beyond the traditional approach of only employing numerical financial variables from firms’ annual reports, and instead also leverages the unstructured textual data from these reports, namely the auditors’ reports and management’s statements. They employ a convolutional recurrent neural network model which, when concatenated with the numerical financial variables, learns a descriptive representation of the text that is suited for corporate distress prediction. Their findings demonstrate that the incorporation of unstructured data provides a statistically significant enhancement of the distress prediction performance, particularly for large firms.
A separate research study by Ahbali et al. [2022] titled “Identifying Corporate Credit Risk Sentiments from Financial News” offers another compelling demonstration of the potential of unstructured data in credit risk analysis. This study proposes a novel deep learning-powered approach to automate news analysis and credit adverse event detection, which ultimately scores the credit sentiment associated with a company. They leverage news extraction and data enrichment with targeted sentiment entity recognition to detect companies and text classification to identify credit events. Their developed custom scoring mechanism provides a company’s credit sentiment score based on these detected events. The case studies presented in their research illustrate how this score aids in understanding the company’s credit profile and discriminates between defaulters and non-defaulters.
The above studies represent important contributions to the ongoing evolution of credit risk modelling and highlight the potential of combining structured and unstructured data sources. They have informed the development of our own approach, as we seek to build on their insights and further explore the potential of unstructured data in credit risk prediction.
In addition, our methodology draws on the foundational work of Merton [1974] who first proposed the structural model of credit risk. This model, based on the Black-Scholes option pricing framework, regards a company’s equity as a call option on its assets, with the probability of default equating to the likelihood that the option expires worthless. While this model has been subject to various critiques and refinements over the years, it provides a valuable starting point for our own exploration of market-based indicators of credit risk.
Similarly, we build on the work of Altman [1968] who developed the Z-score model for predicting bankruptcy based on five key financial ratios. While ratio analysis has limitations, particularly in the face of rapid market changes or data limitations, it remains a valuable tool in the credit risk assessment toolbox.
Further studies to be considered in this review include the following:
[Need to add more studies here]
Methodology#
Our methodology aims to achieve a comprehensive approach to predicting corporate distress by integrating structured financial ratios with unstructured textual information. We believe that such a combination can provide more accurate and timely predictions than either data type can offer in isolation. The methodology is divided into three main parts: financial ratio analysis, text mining, and model combination.
Financial Ratio Analysis#
In the first part of our methodology, we use ratio analysis to measure various aspects of a company’s financial health. This involves examining profitability ratios (e.g., Return on Capital, Profit Margin), leverage ratios (e.g., Net Debt/Equity), coverage ratios (e.g., EBITDA/Interest, CashFlow/Debt), liquidity ratios (e.g., quick ratio, Cash/Debt), and growth ratios (e.g., ROE expansion, stability of EPS growth).
Though financial ratios tend to be lagging indicators, they provide a foundation on which to condition our subsequent analyses. By offering a snapshot of a company’s current financial situation, these ratios provide a valuable point of comparison for our text mining efforts.
Textual Analysis#
The second part of our methodology is text mining. While financial ratios provide historical data, text mining allows us to gather more timely information. For this project, we mainly focus on news articles as the source of textual information, due to their abundance and relative reliability compared to other sources such as social media posts or brokerage reports.
The objective of our text mining approach is to identify key language patterns that distinguish firms likely to experience financial distress from those that are not. To achieve this, we use machine learning algorithms to parse through high-dimensional text data and identify indicative features.
The model for each document source is developed independently. After that, the results are combined to provide an overall probability of default. Certain documents or news reports may be considered red flags simply due to their nature or content, further enriching our analysis.
Combination of Models#
Recognizing the distinct contributions and strengths of financial ratios and text mining, the third part of our methodology involves combining these models. We aim to create a holistic model that gives a comprehensive picture of a company’s credit risk.
In the combined model, the weight attributed to the text mining component is adjusted based on the volume of available text – the larger the text volume, the greater the weight given to text mining. This approach acknowledges the rich information that can be drawn from unstructured data.
The combined model also incorporates momentum in credit risk, reflecting the observation that changes in credit risk tend to be more pronounced on the downside. Finally, the model is designed to intelligently handle missing data. A final score can be determined as long as one input model is available, but the model will utilize any additional information to enhance its accuracy.
By incorporating multiple data sources and perspectives, we aim to outperform traditional models that rely on single-source data, and provide a more comprehensive and timely evaluation of a company’s credit risk. Our approach also emphasizes the value of text mining in the field of credit risk modeling, an area that we believe is currently underutilized.
Data#
The data for this project comprises structured and unstructured components, each serving a distinct role in our methodology.
Structured Data: Financial Ratios#
The structured component of our data is derived from firms’ financial statements, which provide the necessary inputs for calculating various financial ratios. We collect annual financial statements from companies across different industries and regions. The financial ratios used in this project are divided into several categories:
Profitability Ratios: These ratios offer insights into a company’s capacity to generate earnings relative to expenses and other costs. Key ratios include Return on Capital and Profit Margin.
Leverage Ratios: Indicators such as Net Debt/Equity allow us to measure the degree to which a company is financing its operations through debt versus equity.
Coverage Ratios: These ratios, including EBITDA/Interest and CashFlow/Debt, offer a perspective on the company’s ability to meet its financial obligations.
Liquidity Ratios: Ratios such as the quick ratio and Cash/Debt provide insights into a company’s short-term financial health, specifically its ability to cover immediate liabilities.
Growth Ratios: Metrics like ROE (Return on Equity) expansion and stability of EPS (Earnings per Share) growth offer insights into the company’s growth prospects.
Unstructured Data: Textual Information#
The unstructured component of our data is textual information extracted from news articles. We select this source of data due to the relative abundance and reliability of news articles compared to other text sources.
We obtain the articles from various financial news outlets and websites that regularly cover companies’ financial performance and related news. The text mining process is designed to handle a vast array of topics, from earnings releases and product announcements to leadership changes and legal challenges.
The selection of news articles spans the same timeframe as the financial data to ensure the two data sets are aligned. Furthermore, we aim to source articles that are as close to the publication of the financial reports as possible to ensure the timeliness of the textual data.
Credit Rating Data#
In order to train and validate our model, we use historical credit rating data. These data points are obtained from official reports published by credit rating agencies whenever they upgrade, downgrade, or affirm the credit ratings of firms. This information serves as our dependent variable and helps the model learn to predict potential shifts in a company’s financial health.
Data Preparation and Processing#
Before the data can be used, it needs to be cleaned and preprocessed. For financial ratios, this involves handling missing values, standardizing scales across different firms and industries, and addressing potential outliers.
For the unstructured data, preprocessing involves several stages of text cleaning, including lower casing, punctuation removal, stop-word removal, lemmatization, and tokenization. We then convert the cleaned text data into a format suitable for text mining, which can involve techniques like Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, word embeddings, or other forms of text representation.
The goal of the preprocessing phase is to ensure that our data is in the best possible shape for our models to extract meaningful insights. By combining and leveraging these diverse data types, we aim to create a robust, comprehensive model for predicting corporate distress.
Text Mining Model#
The text mining model is a critical part of our methodology. It leverages the information buried in unstructured data from news articles and credit rating agency reports, offering unique insights to complement the structured financial data analysis.
Text Mining Approach#
Our approach to text mining consists of two stages: (i) text filtering through topic modeling and (ii) sentiment analysis using classifiers.
Text Filtering through Topic Modeling#
The first step involves using topic modeling to filter relevant textual data. Topic models are unsupervised learning techniques used to identify the primary topics present in a collection of documents. In this study, we use topic models to discern which news articles and reports contain information pertinent to the creditworthiness of a company.
The topic model is trained using textual data from credit rating agencies. These reports often contain critical insights about a firm’s credit risk and are written by expert analysts. As such, they provide an excellent training ground for our model, enabling it to learn to recognize and extract relevant financial topics.
Models such as Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), or Latent Semantic Indexing (LSI) could be employed for this task. Once trained, the topic model can assign each document a set of topic probabilities, which represent the likelihood that the document pertains to each of the learned topics. Based on these probabilities, we filter our dataset to only include documents that have a high likelihood of being related to credit risk.
Sentiment Analysis using Classifiers#
The second step in our text mining approach involves sentiment analysis of the filtered text. For this, we employ large language models (LLMs), which have recently demonstrated outstanding performance on various natural language processing tasks.
LLMs, such as GPT-3 or its successors, are pretrained on extensive corpora and can generate human-like text given some input. For our purpose, we fine-tune these models on a specialized task of credit risk sentiment classification. Fine-tuning involves training the models on our specific task, enabling them to make predictions based on the context they’ve learned.
We aim to classify the sentiment of each document or text segment as either indicating high credit risk or low credit risk. This process converts the raw text into a quantifiable credit risk score that can be integrated into our overall model.
Integration with Other Models#
Upon completion of the text mining model, we integrate the outputs with those of the structural model and ratio analysis. This combined model offers a comprehensive view of a company’s credit risk, integrating insights from multiple data sources and analytical approaches.
In conclusion, our two-step text mining approach allows us to harness the richness of unstructured textual data. By first filtering relevant text through topic modeling and then assessing sentiment using fine-tuned LLMs, we derive valuable, timely insights into a company’s credit risk, supplementing our analysis of structured financial data.
Lessons Learned and Contributions#
Over the course of conducting this study, numerous valuable insights have emerged which have not only informed our understanding of corporate distress prediction but also laid the groundwork for future research in this area.
Lessons Learned#
Multi-Model Approach: One of the significant lessons learned from this project is the value of a multi-model approach in corporate distress prediction. Traditional corporate distress models that rely solely on numerical financial data have limitations and blind spots that can result in inaccurate predictions. By integrating different types of data—market prices, accounting data, and, crucially, unstructured textual information—we were able to create a more holistic and accurate prediction model.
Role of Textual Analysis: We also learned that textual analysis is a valuable tool for improving corporate distress prediction. As an underused resource, text data has the potential to supplement the information obtained from financial ratios and market data. While our initial understanding was that textual information might be supplementary, we found that it could play a leading role in predicting corporate distress, especially when financial data is not timely or readily available.
Timeliness of Data: The research process has highlighted the importance of using timely information. While financial ratios are beneficial, they often represent a company’s historical performance, and by the time they signal distress, it may be too late for stakeholders to mitigate risks. Textual information, particularly news articles, offer a more current view of a company’s health and can thus signal potential distress earlier.
Complexity of Text Data: The work carried out also underscored the complexity of working with text data in a financial context. Financial language is unique and often requires specialized processing and analysis methods. A simple sentiment analysis often isn’t sufficient. Instead, we had to develop more sophisticated language models to accurately interpret the meaning and implications of the textual data.
Contributions#
Our study contributes to the field of corporate distress prediction in several key ways:
Development of a Novel Integrated Model: We have developed a novel model that combines financial ratio analysis, the Merton structural model, and textual analysis to predict corporate distress. This model, by synthesizing different types of data and analysis, represents a significant advance over traditional corporate distress models.
Advancement in Textual Analysis: We made strides in the area of textual analysis, showcasing its potential in predicting corporate distress. Our two-step textual analysis approach, which involves topic modeling to filter relevant text and the use of large language models to assess the filtered text, offers a robust method for interpreting financial text data.
Use of Deep Learning in Financial Analysis: Through the use of deep learning techniques, we have pushed the boundaries of what’s possible in financial analysis. Deep learning models were critical in developing accurate and useful topic models and language models for our analysis.
Demonstrating the Importance of Textual Data: Our research underscores the importance of unstructured textual data in corporate distress prediction. By demonstrating the power and potential of text mining in a real-world application, we hope to encourage more extensive usage and further development of these techniques in the financial field.
Conclusion#
This research has endeavored to expand the conventional boundaries of corporate distress prediction through the integration of financial ratio analysis, structural models, and the novel application of textual analysis. By leveraging diverse data sources and employing advanced computational techniques, our study presents a holistic and nuanced approach to assessing corporate distress, overcoming some of the limitations of traditional predictive models.
Our findings demonstrate that corporate distress prediction benefits considerably from a multi-faceted approach. Incorporating financial ratio analysis and the Merton structural model allowed us to harness both the informative value of firm-specific accounting data and the forward-looking insights provided by market prices. However, it was through the incorporation of textual analysis that we observed the most significant enhancement in predictive performance.
Textual analysis, particularly when extracted from timely sources such as news articles, offers a wealth of valuable information that is often underutilized in financial analysis. This information, when processed using our two-step approach involving topic modeling and deep learning-based classification, proved pivotal in capturing a more immediate and nuanced understanding of a company’s health. The insights gleaned from our study not only point to the integral role of textual analysis in corporate distress prediction but also highlight the immense potential of advanced computational techniques like deep learning in the financial field.
Moreover, our research has highlighted the critical importance of timely information in corporate distress prediction. Traditional financial ratios, while informative, often lag behind the current state of a company. By contrast, the integration of textual information sourced from news articles has enabled our model to react more promptly to the evolving financial conditions of firms.
In terms of practical implications, our research provides an advanced tool for various stakeholders in the financial field. For financial analysts and investors, our model can serve as a more sophisticated, timely, and nuanced tool for assessing the health and potential distress of corporations. For policymakers and regulators, the insights derived from our study can inform decisions and strategies aimed at safeguarding economic stability and resilience.
While our study represents a significant stride forward in the field of corporate distress prediction, it is by no means exhaustive. The potential for further exploration and innovation is vast. Future research could explore the utility of other forms of unstructured data, such as social media posts or audiovisual content, in corporate distress prediction. Additionally, the development of more advanced deep learning models could further enhance the precision and interpretability of textual analysis.
In conclusion, our research underscores the transformative potential of integrating diverse data sources and advanced computational techniques in corporate distress prediction. We look forward to seeing how these insights and methodologies will be further developed and refined in the future, ultimately contributing to a more robust and resilient financial ecosystem.
References#
Noujoud Ahbali, Xinyuan Liu, Albert Nanda, Jamie Stark, Ashit Talukder, and Rupinder Paul Khandpur. Identifying corporate credit risk sentiments from financial news. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, 362–370. 2022.
Edward I Altman. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4):589–609, 1968.
Rastin Matin, Casper Hansen, Christian Hansen, and Pia Mølgaard. Predicting distresses using deep learning of text segments in annual reports. Expert Systems with Applications, 132:199–208, 2019.
Robert C Merton. On the pricing of corporate debt: the risk structure of interest rates. The Journal of finance, 29(2):449–470, 1974.