Corporate Default Research Articles

Scenario Analysis with the DD-PD Mapping Approach: Stock Market Shocks and U.S. Corporate Default Risk

This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms’ DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from quantitative and narrative scenarios informed by expert judgment. At end-2020, risks from stock market corrections in the U.S. are concentrated in the energy, financial and technology sectors, and additional bank capital needs could be large. The paper concludes discussing uses of the mapping beyond PD valuation suitable for capital structure analysis, credit portfolio management, and long-term scenario planning analysis.

Short Selling Threats and Corporate Default Risk: Evidence from the Chinese Stock Market

Short Selling Threats and Corporate Default Risk: Evidence from the Chinese Stock Market

The Bloomberg Corporate Default Risk Model (DRSK) for Private Firms

The Bloomberg Corporate Default Risk Model (DRSK) for Private Firms

Failure prediction models: development and comparison between the multivariate discriminant analysis and the support vector machine for Tunisian companies

In this study, we try to develop a model that would predict corporate default using a multivariate discriminant analysis (MDA) and a support vector machine (SVM). The two models are applied on the Tunisian context. Our sample consists of 212 companies operating in different industries, of which 106 are 'performing' companies and 106 are 'failing' companies, observed over the 2005-2010 period. The results of the use of a battery of 87 ratios showed that 16 ratios can build the model and that liquidity and solvency have more weight than profitability and management in predicting distress. Despite the slight superiority of the results provided by the SVM model, on the control sample, the results provided by the two models are good either in terms of correct classification percentage or in terms of the stability of discriminating power over time and space.

Which Sustainability Dimensions Affect Credit Risk? Evidence from Corporate and Country-Level Measures

Amid growing concern over sustainability issues, there is increasing demand to incorporate environmental and social issues into assessments of credit risk, the possibility of loss resulting from a borrower’s failure to meet their financial obligations. In this paper, we sought to identify empirical evidence of a relationship between sustainability measures and credit risk. We contribute to this literature in three main ways: firstly, by using a measure that considers the financial materiality of sustainability issues across different industries; secondly, by using corporate default swap (CDS) spreads as a market-based measure of credit risk; and thirdly, by exploring the context-dependent nature of the relationship. Though the extent differs across industries, our results suggest risk-reducing effects across several corporate sustainability dimensions: climate change; natural resource use; human capital and corporate governance. Furthermore, we found that country sustainability plays a moderating role in the nexus between corporate sustainability and credit risk. Hence, a one-size-fits-all policy may not be suitable in developing the credit-relevant standardization of sustainability factors. Nevertheless, the robustness of corporate governance throughout our findings suggests that corporations should strengthen governance frameworks and procedures prior to embarking on environmental and social objectives to mitigate credit risk.

Corporate Default Forecasting with Machine Learning

We compare statistical models usually employed in credit risk forecasting with machine learning algorithms (ML). Using a large dataset which includes financial ratios and credit behavioral indicators for about 300,000 Italian non-financial firms from 2011 to 2017, we show that training the models on financial statement data only, ML models record a significant improvement in discriminatory power and precision with respect to statistical models; however, this improvement is less pronounced when we enlarge the training dataset to include also credit behavioral data

Understanding Credit Risk for Chinese Companies

In response to the recent elevated corporate credit risk environment in China, we develop a probability of default (PD) measure based on corporate bond defaults using the machine learning technique. We document a large pricing effect of corporate credit risk based on our PD measure in the primary and secondary corporate bond markets especially following the first bond default in 2014. In the cross section of corporate bond returns, there is a positive credit risk premium even after controlling for common risk factors. Finally, stocks of low PD firms outperform those of high PD firms during the COVID-19 pandemic.

The role of the leverage effect in the price discovery process of credit markets

Starting from the analysis of a levered firm’s capital structure, I show that corporate default risk becomes measurable through the leverage effect, i.e., the negative correlation observed between stock returns and changes in stock volatility. In this model, the firm’s debt-to-asset ratio governs the elasticity of default probabilities relative to equity prices. I use a large dataset of S&P 500 firms and an extended timeframe (2008–2019) to examine the model’s empirical implications. First, the firm’s corporate leverage enhances the transmission of pricing information from the stock to the credit default swap (CDS) market. Second, equity and credit markets are more likely to be co-integrated when firms employ a higher debt-to-asset ratio. Although the stock market generally dominates the price discovery process, a small cluster of highly-leveraged firms exhibits a dominant CDS market share. Under the effect of corporate leverage, the credit market attracts informed trading and arbitrage resources.

Utility Indifference Valuation for Defaultable Corporate Bond with Credit Rating Migration

Credit risk modeling by debt pricing has been a popular theme in both academia and practice since the subprime crisis. In this paper, we devote our study to the indifferent price of a corporate bond with credit risk involving both default risk and credit rating migration risk in an incomplete market. The firm’s stock and a financial index on the market as tradable assets are introduced to hedge the credit risk, and the bond price is determined by the indifference of investors’ utilities with and without holding the bond. The models are established under the structural framework and result in Hamilton–Jacobi–Bellman (HJB) systems regarding utilities subject to default boundary and multiple migration boundaries. According to dynamic programming theory, closed-form solutions for pricing formulas are derived by implementing an inverted iteration program to overcome the joint effect of default and multiple credit rating migration. Therefore, with the derived explicit pricing formulas for the corporate bond, the models can be easily applied in practice, and investors can generate their strategies of hedging the credit risk by easily analyzing the impacts of the parameters on the bond price.

Sovereign Risk, Public Investment and the Fiscal Policy Stance

Of the different types of government outlays, since the 2000s public investment has been the main variable of adjustment during recessions in advanced and emerging economies. These contractions (expansions) have been associated with relatively medium-high (low) sovereign spreads, especially in advanced economies. To rationalize these issues, we develop a model of fiscal policy and sovereign default, with corporate default risk. Policymakers must decide between the provision of an unproductive public good and public investment, weighting their respective net benefits in terms of short-term stabilization and debt sustainability. In our model, investment follows a countercyclical stance only in the case of low levels of debt and moderate negative shocks, and otherwise contracts during recessions. The policy stance, along with the mix between different outlays, is determined by how sovereign risk responds to adverse economic shocks.

Corporate Default Predictions Using Machine Learning: Literature Review

Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical models and machine learning methodologies, we identify the key studies used in corporate default prediction. By comparing these methods with findings from the interdisciplinary literature, our review suggests some new tasks in the field of machine learning for predicting corporate defaults. First, a corporate default prediction model should be a multi-period model in which future outcomes are affected by past decisions. Second, the stock price and the corporate value determined by the stock market are important factors to use in default predictions. Finally, a corporate default prediction model should be able to suggest the cause of default.

The nexus of judicial efficiency, social burden and default risk: Cross-country evidence

The paper investigates heterogeneous institutional costs related to firm bankruptcy and default risk over a comprehensive sample of 65 economies between 2003 and 2016. We find that corporate default risk is decreasing in judicial efficiency whereas it is increasing in potential social costs related to high unemployment rate. In the cross-sectional comparison, we find that firms with less cash reserves are more likely affected by judicial efficiency. Similarly, the impact of potential social costs induced by employment regulation is more pronounced in firms with higher staff expenses. Our main findings indicate that creditor protection is a crucial channel to lower corporate default risk, through which judicial efficiency alleviates the pressure from accelerating debt repayment. Besides, pro-labor regulations imposed by local governments facing high unemployment make it difficult for firms to reconcile the conflicts of labor input. It is conditional on contextual factors that whether the two types of institutional costs are complementary on default risk.

Determinants of corporate default risk in China: The role of financial constraints

Corporate default risk can affect financial stability and the macroeconomy. However, the determinants of corporate default risk in China are not well defined in the literature. We address this issue by using a rich credit event dataset of 981 Chinese listed firms over the period 1998–2013 and study the factors that affect default risk. We demonstrate that leverage, liquidity, firm size are the key firm-specific factors in determining default risk in China, along with macroeconomic factors like interest rate and stock return. Moreover, ‘Too big to fail’ only applies to non-SOEs, as default risk of SOEs is not affected by the firm size. We further find that high liquidity fails to reduce firms default risk, because small-sized firms which are financially constrained have limited cash to prevent financial distress, whereas large firms with greater cash holdings are able to mitigate their default risk as they are unconstrained.

Do measures of systemic risk predict U.S. corporate bond default rates?

Using univariate and multivariate Mixed Data Sampling (MIDAS) and LASSO estimation methodologies, we explore whether the U.S. annual average corporate bond default rate can be predicted by 12 monthly systemic risk measures proposed in the literature. We find that nearly all of the systemic risk indicators have predictive power for the default rate. Granger causality tests based on multivariate mixed frequency VAR models further support this conclusion. On the basis of MIDAS models, we illustrate that five of these indicators are able to forecast out-of-sample the 2009 corporate default crisis. Using a LASSO multivariate model, it is further shown that the systemic risk indicators can forecast out-of-sample both the 2009 default rate and the default rates during the buildup before the crisis and in the aftermath of the crisis. Institution-specific and volatility systemic risk measures are the most relevant for modeling U.S. corporate bond default rates, with the Conditional VaR measure of Adrian and Brunnermeier (2016) exhibiting the best performance.

Do going concern opinions provide incremental information to predict corporate defaults?

Investors, regulators, and academics question the usefulness of going concern opinions (GCOs). We assess whether GCOs provide incremental information, relative to other predictors of corporate default. Our measure of incremental information is the additional predictive power that GCOs give to a default model. Using data from 1996 to 2015, initially we find no difference in predictive power between GCOs alone and a default model that includes financial ratios. However, there is an imperfect overlap between GCOs and other predictors. We show that GCOs increase the predictive power of several models that include ratios, market variables, probability of default estimates, and credit ratings. Using a model that includes ratios and market variables, GCOs increase the number of predicted defaults by 4.4%, without increasing Type II errors. Our findings suggest that GCOs summarize a complex set of conditions not captured by other predictors of default.

Default count-based network models for credit contagion

Interconnectedness between economic institution and sectors, already recognised as a trigger of the great financial crisis in 2008–2009, is assuming growing importance in financial systems. In this article, we study contagion effects between corporate sectors using financial network models, in which the significant links are identified through conditional independence testing. While the existing financial network literature is mostly focused on Gaussian processes, our approach is based on discrete data. We indeed test dependence in the conditional mean (and volatility) of default counts in different economic sector estimated from Poisson autoregressive models, and in their shocks. Our empirical application to Italian corporate defaults in the 1996–2018 period reveals evidence of a high inter-sector vulnerability, especially at the onset of the global financial crisis in 2008 and in the following years. Many contagion effects between corporate sectors are indeed found in the shock component of the default count dynamics.

Corporate default forecasting with machine learning

We analyze the performance of a set of machine learning models in predicting default risk, using standard statistical models, such as the logistic regression, as a benchmark. When only a limited information set is available, for example in the case of an external assessment of credit risk, we find that machine learning models provide substantial gains in discriminatory power and precision, relative to statistical models. This advantage diminishes when confidential information, such as credit behavioral indicators, is also available, and it becomes negligible when the dataset is small. Moreover, we evaluate the consequences of using a credit allocation rule based on machine learning ratings on the overall supply of credit and the number of borrowers gaining access to credit. Machine learning models concentrate a greater extent of credit towards safer and larger borrowers, which would result in lower credit losses for their lenders.

Credit Derivatives and Corporate Default Prediction

There have been 128 defaults among U.S. CDS reference entities between 2001 and 2020. Within this sample, the five-year CDS spread is a significant predictor of corporate default in models with equity market covariates and firm attributes. This finding holds for forecast horizons up to 12 months, among financial and non-financial firms, within and without the great financial crisis, and is robust to the inclusion of corporate bond and equity options market information. A decomposition of the CDS spread into liquidity, physical default, and risk premium components shows that most of its predictive power for corporate default comes from the physical default component, both in- and out-of-sample. These results confirm the relevance of information contained in single-name CDS pricing to corporate default prediction.

Climate change and credit risk

We investigate the relationship between exposure to climate change and firm credit risk. We show that the distance-to-default, a widely used market-based measure of corporate default risk, is negatively associated with the amount of a firm’s carbon emissions and carbon intensity. Therefore, companies with high carbon footprint are perceived by the market as more likely to default, ceteris paribus. The carbon footprint decreases the distance-to-default following shocks - such as the Paris Agreement - that reveal policymakers’ intention to implement stricter climate policies. Overall, these results indicate that the exposure to climate risks affects the creditworthiness of loans and bonds issued by corporates. Financial regulators and policymakers should consider carefully the impact of climate change risks on the stability of both lending intermediaries and corporate bond markets.

A hybrid model to estimate corporate default probabilities in China based on zero-price probability model and long short-term memory

ABSTRACT This article proposes a hybrid model by combining zero-price probability model with long short-term memory (ZPP-LSTM) to estimate corporate default probabilities. The ZPP-LSTM model enhances the time-series data forecast by introducing LSTM in ZPP model, which can better estimate the corporate default probabilities in the industry sensitive to an uncertain environment. The full samples of Chinese listed companies in construction and real estate industries are selected to evaluate the performance of ZPP-LSTM model. The results show that our proposed model outperforms other benchmark models in terms of the default probability estimation.