•A decline of market capital from Covid-19 will increase the default likelihood.•The mining, construction and retail sectors are most vulnerable to market shock.•Given a moderate deterioration in ...economic profile, a tax deferral is sufficient.•For exacerbating shocks, debt and equity support is essential to avoid a meltdown.
The massive contagion of new coronavirus (Covid-19) has disrupted many businesses across the European Union. This has resulted in an immense drag on the revenues and cash flows that may lead to a significant increase in corporate bankruptcies. In this paper, we investigate the impact of Covid-19 on the solvency profile of the firms in the EU member states. We introduce multiple stress scenarios on the non-financial listed firms and report a progressive increase in the probability of default, an increase of debt payback, and declining coverages. Our results indicate that the solvency profile of all firms deteriorates. The manufacturing, mining, and retail sector are most vulnerable to a decline in market capitalization and a reduction in sales revenues. The paper also examines the possible policy interventions to sustain solvency at a pre Covid-19 level. Our findings suggest that for a moderate deterioration in economic conditions, a tax deferral is sufficient. However, in the event of exacerbating business shocks, there should be hybrid support through debt and equity to avoid a meltdown. This study has important implications for policymakers, corporate managers, and creditors.
•We study the relation between soft factors in P2P loan applications and financing and default outcomes.•We use a data set based on the two leading European P2P lending platforms Smava and ...Auxmoney.•We find that the investors react more strongly to soft information on Auxmoney.•We find that soft factors influence the funding probability but not the default probability.
We examine the relation of soft factors that are derived from the description texts to the probability of successful funding and to the default probability in peer-to-peer lending for two leading European platforms. We find that spelling errors, text length and the mentioning of positive emotion evoking keywords predict the funding probability on the less restrictive of both platforms, which even accepts applications without credit scores. This platform also shows a better risk-return profile. Conditional on being funded, text-related factors hardly predict default probabilities in peer-to-peer lending for both platforms.
We theorize the financial health of a company and the risk of its default. A company is financially healthy as long as its equilibrium in the financial system is maintained, which depends on the cost ...attributable to the probability that equilibrium may decay. The estimate of that probability is based on the credibility and uncertainty of the company’s financial forecasts. Accordingly, we have developed an equilibrium model establishing ranges of interest rates as a function of predictable performance of a company, of changes in its financial structure, and foreseeable trends of its credit supply conditions. As an operating result, ours is a ‘tailored” failure scoring model that abandons stationary settings, where credit, market and idiosyncratic factors of risk interact dynamically in order to estimate intrinsically forward-looking PDs. This model promises significant operational impacts for financial intermediation and for validating the prospective financial information.
•The probability of default (PD) of a company depends on the credibility and uncertainty of its financial forecasts, as well as foreseeable trends of its credit supply conditions.•The distress is irreversible (default) when short-term credit facilities constantly increase in a steady state.•The PD affects interest rates, which in turn affect the future debt service and the PD itself. Our model determines if and at what rates an equilibrium is achieved or not (default).•Each business plan of a company has its own expected default probability, which is to be considered fixed for any future moment of estimation.•Market, idiosyncratic, and credit risk variables interact dynamically and generate a credit scoring system of an intrinsically forward-looking nature.
In this paper we propose a novel credit risk modelling approach where number of days past due is modeled instead of a binary indicator of default. In line with regulatory requirements, the number of ...days overdue on loan repayments are transformed to a binary variable by applying 90-days past due threshold, and use it as the dependent variable in default probability models. However, potentially useful information is lost with this transformation. Lower levels of days past due are expected to be good predictors of future incidence of default. We show that a dynamic Tobit model, where number of days overdue is used as a censored continuous dependent variable, significantly outperforms models based on binary indicators of default. It correctly identifies more than 70% of defaulters and issues less than 1% of false alarms. Its superiority is confirmed also by more accurate rating classification, higher rating stability over the business cycle and more timely identification of defaulted borrowers. The implications for banks are clear. By modelling number of days past due they can significantly improve risk identification and reduce procyclicality of IRB capital requirements. Moreover, we show how modelling of days past due can be used also for stage allocation for the purposes of IFRS9 reporting.
•Corporate social responsibility is negatively associated with probability of default.•The effect is stronger in the long term than in the short run.•The effect is more pronounced in countries with ...weaker market-supporting institutions.•The benefits of CSR investment are greater in countries with institutional voids, where transaction costs are higher and access to capital markets is limited.
Using a large sample of firms from 36 countries between 2002 and 2016, this study investigates the relationship between corporate social responsibility (CSR) and default risk, with a focus on the differential effect conditional on the lengths of time horizons. We find that CSR is negatively associated with probability of default and the effect is stronger in the long term than in the short run. Further, the impact of CSR on firm default probability appears to be greater in countries with weaker capital markets and legal institutions. Overall, our findings provide evidence supporting the role of CSR in filling institutional voids.
Many enterprises across the European Union (EU) have been hampered by the massive spread of COVID-19. It has severely impacted revenues and financial flows, potentially leading to an increase in ...corporate insolvency. This study investigates the influence of this new coronavirus on the solvency status of businesses in EU Member States. Several stress scenarios were constructed for non-financial listed enterprises. The results reveal a gradual surge in the possibility of default, a rise in loan repayment, and coverage being refused. According to our findings, the solvency profiles of all firms are deteriorating. Industries, such as mining, mass production, and retail, are the most susceptible to a drop in sales income and market capitalization. Before COVID-19, previous research had looked at policy options for maintaining solvency. Our data imply that a tax delay is adequate if there is a slight deterioration in the economic outlook. There should be hybrid assistance through loans and equity for even a slight deterioration in the state of an economy. This research will benefit policymakers, corporate executives, and creditors.
Boosting credit risk models Baesens, Bart; Smedts, Kristien
The British accounting review,
7/2023
Journal Article
Recenzirano
In this article, we give various recommendations to boost the performance of credit risk models. It is based upon more than two decades of research and consulting on the topic. Building credit risk ...models typically entails four steps: gathering and preprocessing data, modelling of probability of default (PD), Loss Given Default (LGD) and Exposure at Default (EAD), evaluating the credit risk models built and then the deployment step to put them into production. We give recommendations to boost credit risk models during each of these steps. Furthermore, we also define and review model risk as an all-encompassing challenge one needs to be properly aware of during each step of the process. We conclude by presenting a research agenda of topics we believe are in high need for further investigation and study.
This study proposed a method for constructing rating tools using logistic regression and linear discriminant analysis to determine the risk profile of SME portfolios. The objective, firstly, is to ...evaluate the impact of the crisis due to the Covid-19 by readjusting the profile of each company by using the expert opinion and, secondly, to evaluate the efficiency of the measures taken by the Moroccan state to support the companies during the period of the pandemic. The analysis in this paper showed that the performance of the logistic regression and linear discriminant analysis models is almost equivalent based on the ROC curve. However, it was revealed that the logistic regression model minimizes the risk cost represented in this study by the expected loss. For the support measures adopted by the Moroccan government, the study showed that the failure rate (critical situation) of the firms benefiting from the support is largely lower than that of the non-beneficiaries.
The Basel III post-crisis reforms target the application of internal credit risk models for the estimation of the risk weighted assets of banks due to concerns about model risk. We use a unique ...dataset of 4.9 million probability of default (PD) estimates covering the January 2016 - June 2020 period sourced from 28 global banks to provide a deep insight into the comparability of model outputs. Our contribution is four-fold. Firstly, we confirm that there is a substantial variance in credit risk estimates. Secondly, we show that the level of PD variance is dependent on the entity type, industry, and location. Thirdly, we conclude that a considerable part of the variance is systematic, especially for credit risk estimates of funds. Finally, we illustrate the massive impact of the COVID-19 pandemic on the PD variance. The results highlight areas with relatively larger comparability issues, and they can be used by regulators to design more targeted policies.
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