Covid-19, Credit Risk Management Modeling, and Government Support.

We investigate rating and default risk dynamics over the covid-19 crisis from a credit risk modeling perspective. We find that growth dynamics remain a stable and sufficient predictor of credit risk incidence over the pandemic period, despite its large, short-lived swings due to government intervention and lockdown. Unobserved component models as used in the recent credit risk literature appear mainly helpful for explaining the high-default wave in the early 2000s, but less so for default prediction above and beyond growth dynamics during the 2008 financial crisis or the early 2020 covid default peak. Government support variables do not reduce the impact of either growth proxies or unobserved components. Correlations between government support and credit risk are different, however, during the financial and the covid crisis. Using the empirical models in this paper as credit risk management tools, we show that growth factors also suffice to predict credit risk quantiles out-of-sample during covid times.

Influence of institutional differences on trade credit use during pandemics

This study examines the formal and informal institutions that affected trade credit during the pandemic periods. To this end, we analyze 590,025 firm-year observations across 107 countries during six recent pandemic crises: SARS (2003), H1N1 (2009), MERS (2012), Ebola (2014), Zika (2016), and COVID-19 (2020). The study finds that formal legal institutions and firms' information transparency during pandemic periods act as a "brake” for trade credit usage. By contrast, informal institutions with religious connotations or attributes, social trust, and policy stability play a "cushion” role in softening the impact of pandemic crises when a firm applies for trade credit. These results remain robust after alternating the estimation techniques, trade credits, pandemic variables, and different samples. This study offers new evidence on the role of trade credit from the perspectives of formal and informal institutions during pandemic crises. The outcomes thus provide information worthy of consideration by policymakers when faced with informal institutional conditions and support government efforts to improve unstable formal systems and prevent severe shocks in the future. © 2022

How can banks and finance companies incorporate value chain factors in their risk management strategy? The case of agro-food firms

A value chain framework for guiding the financial firms in their credit decisions is urgent, as the current COVID-19 pandemic has highlighted, but missing in the extant literature, particularly for those that lend to industries sensitive to value and supply chain bottlenecks. This study creates knowledge in value chain finance, a big untapped and un-researched market. It constructs, confirms, and validates a value chain framework for assessing risks in lending to Agro and Food Processing firms in which value chain risks are major business concerns globally. To pursue the objectives of the study, we use a novel methodology that integrates the Modified Delphi technique, exploratory factor analysis, confirmatory factor analysis, and discriminant analysis. Based on testing and analysis of primary data, including loan data, a framework comprising six factors is proposed for use in conjunction with existing risk assessment models of finance companies to improve the quality of their credit decisions, contributing to their performance sustainability. © 2022 ERP Environment and John Wiley & Sons Ltd.

The role of web browsing in credit risk prediction

Online mail order and online retail purchases have increased rapidly in recent years worldwide, with Covid-19 forcing almost all non-grocery shopping to move online. These practices have facilitated the availability of new data sources, such as web behavioural variables providing scope for innovation in credit risk analysis and decision practices. This paper examines new web browsing variables and incorporates them into survival analysis as predictors of probability of default (PD). Using a large sample of purchase and repayment credit accounts from a major digital retailer and financial services provider, we show that these new variables enhance the predictive accuracy of probability of default (PD) models at account level. This also holds in the absence of credit bureau data, therefore, the new information can help people who may not have a credit history (thin file) who cannot be assessed using traditional variables. Moreover, we leverage on the dynamic nature of these new web variables and explore their predictive value in short and long- term horizons. By adding macroeconomic variables, the possibility for stress-testing is provided. Our empirical findings provide insights into web browsing behaviour, highlight how the inclusion of non-standard variables can improve credit risk scoring models and lending decisions and may provide a solution to the thin files problem. Our results also suggest a direct value added to the online retail credit industry as firms should leverage the increasing trend of consumers embracing the digital environment. © 2022 The Authors

Predicting Credit Ratings using Deep Learning Models - An Analysis of the Indian IT Industry

Due to the complexity of transactions and the availability of Big Data, many banks and financial institutions are reviewing their business models. Various tasks get involved in determining the credit worthiness like working with spreadsheets, manually gathering data from customers and corporations, etc. In this research paper, we aim to automate and analyze the credit ratings of the Information and technology industry in India. Various Deep-Learning models are incorporated to predict the credit rankings from highest to lowest separately for each company to find the best fit Margin, inventory valuation, etc., are the parameters that contribute to the credit rating predictions. The data collected for the study spans between the years FY-2015 to FY-2020. As per the research been carried out with efficiencies of different Deep Learning models been tested and compared, MLP gained the highest efficiency for predicting the same. This research contributes to identifying how we can predict the ratings for several IT companies in India based on their Financial risk, Business risk, Industrial risk, and Macroeconomic environment using various neural network models for better accuracy. Also it helps us understand the significance of Artificial Neural Networks in credit rating predictions using unstructured and real time Financial data consisting the influence of COVID-19 in Indian IT industry.

The COVID Cash Transfer Study: The Impacts of a One-Time Unconditional Cash Transfer on the Well-Being of Families Receiving SNAP in Twelve States

There is growing interest in the use of unconditional cash transfers as a means to alleviate poverty, yet little is known about the effects of such transfers in the U.S. This paper reports on the results of a randomized controlled study of a one-time $1,000 unconditional cash transfer in May 2020 to families with low incomes in 12 U.S. states. The families were receiving, or had recently received, Supplemental Nutrition Assistance Program benefits. We examine the impact of the cash transfer on five pre-registered outcomes (material hardship, mental health, parenting, child behavior, partner relationships) and several secondary outcomes (hardship avoidance, consumption, employment, benefit use). We find no statistically significant effects (powered to detect effects of 0.09 standard deviations) of the cash transfer on any outcomes for the full sample. In pre-specified exploratory analyses, we find significant reductions in material hardship (-0.17 standard deviations) among families with less than $500 of earnings in the previous month, roughly the bottom 50 percent of monthly earnings for the study sample.

The 2023 CPT Coding and Medicare Payment Update

There are a host of changes that will affect family physicians, including new vaccine codes and bundled Medicare payments for chronic pain management.

Can trade credit rejuvenate Islamic banking?

This study proposes a renewal of the contemporary Islamic banking Murabaha financing model as it aggravates financial fragility with waning economic efficiency. We adapt the working capital framework of successful US companies like Amazon and Walmart and model an innovative Murabaha facility as trade credit within the real sector of the economy. We then test its robustness in a range of simulation tests. Our approach is novel and stands in contrast to the familiar financial sector fixed-income facilities, characteristic of Western economies, stealthily mimicked as mark-up (interest rate based) Murabaha by Islamic banks. We argue that this is neither appropriate nor effective for Islamic economies, making them fragile under monetary pressures in crises like the current coronavirus and energy ones. Our simulation results indicate that the trade credit Murabaha not only transforms debt into a risk-sharing one but also offers more competitive financing rates, reduces systemic risk, and improves financial stability. Furthermore, our results imply that the trade credit Murabaha can increase the efficiency of Islamic financial systems and make them more resilient to shocks. Consequently, this paper discusses the integration of our novel Murabaha within a recreated architecture of Universal Banking. As an implication, this should promote business activity and contribute to global growth. Finally, we recommend how to deploy our novel Murabaha based on trade credit (as opposed to the currently deployed fixed-income-mimicked Murabaha) to alleviate twin agency debt costs (risk shifting, underinvestment) and solve the ownership transfer problem of modern Islamic banking. © 2022, The Author(s).

The collateralized personality: creditability and resistance in the age of automated credit-scoring and lending

This paper explores the force of automation and its contradictions and resistances within (and beyond) the financial sector, with a specific focus on computational practices of credit-scoring and lending. It examines the operations and promotional discourses of fintech start-ups LendUp.com and Elevate.com that offer small loans to the sub-prime consumers in exchange for access to their online social media and mobile data, and Zest AI and LenddoEFL that sell automated decision-making tools to verify identity and assess risk. Reviewing their disciplinary reputational demands and impacts on users and communities, especially women and people of colour, the paper argues that the automated reimagination of credit and creditability disavows the formative design of its AI and redefines moral imperatives about character to align with the interests of digital capitalism. The economic, social and cultural crises precipitated by the Covid-19 pandemic have only underscored the internal contradictions of these developments, and a variety of debt resistance initiatives have emerged, aligned with broader movements for social, economic, and climate justice around the globe. Cooperative lending circles such as the Mission Asset Fund, activist groups like #NotMyDebt, and Debt Collective, a radical debt abolition movement, are examples of collective attempts to rehumanize credit and debt and resist the appropriative practices of contemporary digital finance capitalism in general. Running the gamut from accommodationist to entirely radical, these experiments in mutual aid, debt refusal, and community-building provide us with roadmaps for challenging capitalism and re-thinking credit, debt, power, and personhood within and beyond the current crises. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Nowcasting the output gap

We propose a way to directly nowcast the output gap using the Beveridge–Nelson decomposition based on a mixed-frequency Bayesian VAR. The mixed-frequency approach produces similar but more timely estimates of the U.S. output gap compared to those based on a quarterly model, the CBO measure of potential, or the HP filter. We find that within-quarter nowcasts for the output gap are more reliable than for output growth, with monthly indicators for a credit risk spread, consumer sentiment, and the unemployment rate providing particularly useful new information about the final estimate of the output gap. An out-of-sample analysis of the COVID-19 crisis anticipates the exceptionally large negative output gap of −8.3% in 2020Q2 before the release of real GDP data for the quarter, with both conditional and scenario nowcasts tracking a dramatic decline in the output gap given the April data. © 2022 The Authors

Firm entry and exit during recessions

We analyze aggregate shocks in a general equilibrium model of firm dynamics with entry and exit and financial frictions. Compared to the productivity shock, a shock to the collateral constraint (credit shock) generates a larger change in firm entry and exit. Calibrating the credit and productivity shocks to the Great Recession, we find that the credit shock accounts for lower entry, higher exit, and the concentration of exit among young firms during the Great Recession. The changes in entry and exit account for 19 and 24 percent of the fall in output and hours, respectively. Furthermore, we discuss how the modeling of potential entrants matters for the quantitative results, and perform a COVID-19 lockdown experiment. © 2021 Elsevier Inc.

The Impact of the Corona Pandemic on Bank Credit: Case of Jordan

This study examines the impact of macroeconomic variables in addition to the Corona pandemic on bank lending in Jordan during 2008:01–2021:02, and thus using the Autoregressive Distributed Lag (ARDL) model, in order to reveal the long-term relationship between macroeconomic variables and bank lending. However, it became clear that all these macroeconomic variables contributed to bank credit in Jordan, and it was proven that there is a long-term relationship between them and bank credit. On one hand, the GDP, inflation, interest rate and coronavirus pandemic negatively affected the lending activities of commercial banks in Jordan, on the other hand, inflation had positively affected these activities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

'Vast Majority' of Mild SARS-CoV-2 Cases Clear Long COVID by One Year

The article presents the discussion on COVID examining the association of mild SARS-CoV-2 infection and longterm health outcomes. Topics include mild disease not leading to serious or chronic long-term morbidity in the vast majority of patients and adding a small, continuous burden on healthcare providers;and showing fairly significant risks for cognitive impairment, dyspnea, and weakness.

Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach.

The economic onslaught of the COVID-19 pandemic has compromised the risk management of financial institutions. The consequences related to such an unprecedented situation are difficult to foresee with certainty using traditional methods. The regulatory credit loss attached to defaulted mortgages, so-called expected loss best estimate (ELBE), is forecasted using a machine learning technique. The projection of two ELBEs for 2022 and their comparison are presented. One accounts for the outbreak's impact, and the other presumes the nonexistence of the pandemic. Then, it is concluded that the referred crisis surely adversely affects said high-risk portfolios. The proposed method has excellent performance and may serve to estimate future expected and unexpected losses amidst any event of extraordinary magnitude.

Regional redistribution through SBA guaranteed loan programs

We document and quantify a new implicit transfer mechanism in the SBA 7(a) loan program that redistributes funds between US states. We use SBA 7(a) loan data in conjunction with Dealscan private corporate loan data to show that SBA 7(a) loan interest rates are much less responsive to predicted local loan default risk compared to private corporate loans. This redistributes funds from states with low default risk to states with high default risk. These transfers are positively correlated with the severity of local economic shocks during the Great Recession and the COVID-19 Recession. Therefore, even though it was unintended, the interest rates on SBA 7(a) loans acted as an automatic stabilizer mitigating regional economic shocks.

Financial Solace: Malaysian Credit Counselling and Debt Management Agency Responses to COVID-19 Challenges

This study evaluates the measures undertaken by the Credit Counselling and Debt Management Agency (AKPK) to assist those financially distressed due to their inability to meet their financial commitments amidst the COVID-19 pandemic. Adopting secondary analysis of qualitative data, relevant secondary data, including journal articles, annual reports, and newspaper articles, were analyzed. The study finds that measures adopted by AKPK in response to the COVID-19 pandemic include reinforcing the workforce, enhancing IT infrastructures, deploying digital platforms, using various media channels, introducing online apps, online portals, online webinars, online learning modules, and online payment facility for all debt management participants. AKPK is also entrusted with handling small and medium enterprises (SMEs) under the Small Debt Resolution Scheme. A dedicated SME Helpdesk is established to facilitate the process. AKPK's continual support to provide financial aid is reflected in its collaborative effort with the banking industry under the Financial Management and Resilience Program and the Financial Resilience Support Program. However, the government should seriously consider strengthening personal data protection laws because of AKPK's significant reliance on digital platforms. Similarly, appropriate government bodies must take quick action to address the digital divide issue and promote inclusion to reduce disparity in terms of access to online services offered by AKPK. Also, since certain individuals or SMEs with credit facilities with entities not regulated by Bank Negara Malaysia are deprived of this incentive, relevant regulators should undertake actions to provide a similar facility. This study is significant in that it provides lessons to be learned by other credit counseling and debt management agencies in adopting effective measures to enable them to adapt to the new normal. © The Authors.

Support for Small Businesses Amid COVID-19

How should the government support small and medium-sized enterprises amid a pandemic crisis while balancing the trade-off between short-run stabilization and long-run allocative efficiency? We develop a two-sector equilibrium model featuring small businesses with private information on their likely future success and a screening contract. Businesses in the sector adversely affected by a pandemic can apply for government loans to stay afloat. A pro-allocation government sets a harsh default sanction to deter entrepreneurs with poorer projects, thereby improving long-run productivity at the cost of persistent unemployment, whereas a pro-stabilization government sets a lenient default sanction. Interest rate effective lower bound leads to involuntary unemployment in the other open sector and shifts the optimal default sanction to a lenient stance. The rise in firm markups exerts the opposite effect. A high creative destruction wedge polarizes the government's hawkish and dovish stances, and optimal default sanction is more lenient, exacerbating resource misallocation. The model illuminates how credit guarantees might be structured in future crises.

COVID-19 and the Care of the Financialized Self

In the United States, the threat of COVID-19 as a public health problem was impossible to separate from the financial threat. From the start, the virus's circulation through human bodies intermingled with all the ways human lives had been defined by neoliberalism's economizing rationality. To unpack the convergence of the pandemic with neoliberal rationality, this article examines the financial advisory discourse produced by credit and fintech companies at the start of the pandemic, focusing on Equifax, Experian, and Mint. This messaging was replete with expressions of care, along with promises of institutional assistance. However, reading further it became clear the companies offered mostly financial self-help advice. The immediate turn to this type of messaging suggested how much the financial system depended on a collective continuation of the individual's sense of moral responsibility for financial self-management and creditworthiness, and especially diligent debt-payment.Alternate :Aux États-Unis la menace de la COVID-19 comme problème de santé publique était indissociable de la menace financière. Dès le début de la pandémie, la circulation du virus dans les corps humains s'est interposée avec toutes les façons dont les vies humaines ont été définies par la rationalité économisante du néolibéralisme. Pour éclaircir la convergence de la pandémie avec la rationalité néolibérale, le présent article examine les messages d'avis financiers produits par les entreprises de crédit et de technologie financière pendant la pandémie, notamment Equifax, Experian et Mint. Ces messages étaient emplis d'expression de soutien, avec des promesses d'aide institutionnelle. Cependant, une lecture plus attentive permet de voir que ces entreprises ont surtout offert des conseils visant l'auto-assistance financière. Le recours immédiat à ce type de message indique le degré auquel le système financier dépend sur la continuation collective du sens de responsabilité morale des personnes pour gérer eux-mêmes leurs finances et leur capacité de crédit, et particulièrement pour continuer à payer leurs dettes.

Global assessment of the COVID-19 impact on IFRS 9 loan loss provisions

PurposeThis article aims to analyze the impact of COVID-19 measures by governments and central banks on International Financial Reporting Standards (IFRS) 9 loan loss provisions (LLPs). Changes in the total amount of LLPs, distribution of outstanding loan balance among IFRS 9 stages and credit risk parameters used for calculation are investigated for each world region where banks report under IFRS.Design/methodology/approachData for a global selection of 105 banks reporting under IFRS were collected from 2019 to 2020 annual reports, financial statements, and Pillar III reports. These data provide the basis to empirically analyze the impact of COVID-19 on LLPs.FindingsIn most world regions Stage 2 balances increase while Stage 3 balances remain comparatively stable. The credit risk parameters used for computing LLPs remained stable in 2020. However, in China, the impact of COVID-19 on banks was not detected. Mean Stage 1 balances for Chinese banks increased slightly during the pandemic. Aside from the COVID-19 impact, we find that LLPs, credit risk parameters, and loss absorption capacities are significantly lower for banks in Canada, Oceania and Western Europe compared to those in the rest of the world.Originality/valueThere exists previous research examining the COVID-19 impact on financial stability, implementation of emergency rules and country-wide analyses to anticipate default rates depending on recovery scenarios. However, this is the first global study on the immediate impact of COVID-19 on LLPs. It reveals the significant differences between world regions and provides implications about their resilience against future credit shocks.