Investigating the relationship between monetary policy, macro-prudential policy and credit risk in Indonesia banking industry

Using a novel panel data set we study the influence of monetary and macro-prudential policies on non-performing loans as a measure of credit risk in Indonesian banking industry from Q1 2010 to Q4 2022. The panel homogeneity assumption was verified through the utilization of the Chow and Roy-Zellner tests. The findings showed that the model was not homogenous, necessitating the use of the Pooled Mean Group (PMG) estimator. The results indicated that monetary and macro-prudential policies significantly impacted credit risk. Furthermore, tight monetary and macro-prudential policies increased and reduced credit risk in the long run, respectively. The findings also showed that a loosening monetary policy reduced credit risk in the short run. Therefore, higher authorities must establish effective monetary and macro-prudential policies to reduce the non-performing loan ratio and maintain credit risk in Indonesia's banking industry.

How credit default swap market measures carbon risk

Given the increasing concerns about the carbon risk's influence on economy, this paper is aimed at exploring the impact of carbon emission on credit risk, measured by credit default swap. By using monthly updated data of 363 unique US companies among a period between 2007 and 2020, we uncover that firm's direct carbon emission increases its CDS spreads, whereas indirect emission is not priced by credit market seriously. Considering dynamic effects of carbon risk, we find a positive correlation between carbon risk and the CDS term structure, which implies that carbon risk's influence on long-term concern of credit risk can be more pronounced. Using exogenous shock: Paris Agreement, our finding remains robust. Finally, we also examine potential channels, including companies' sustainability awareness, green transition willingness, and ability, through which carbon risk is priced among the credit market. This paper provides further evidence of carbon credit premium and contributes to the implications of carbon cutting activities.

Does country risk impact the banking sectors' non-performing loans? Evidence from BRICS emerging economies

This study aims to fill the gap in the literature by specifically investigating the impact of country risk on the credit risk of the banking sectors operating in Brazil, Russia, India, China, and South Africa (BRICS), emerging countries. More specifically, we explore whether the country-specific risks, namely financial, economic, and political risks significantly impact the BRICS banking sectors' non-performing loans and also probe which risk has the most outstanding effect on credit risk. To do so, we perform panel data analysis using the quantile estimation approach covering the period 2004-2020. The empirical results reveal that the country risk significantly leads to increasing the banking sector's credit risk and this effect is prominent in the banking sector of countries with a higher degree of non-performing loans (Q.25 = - 0.105, Q.50 = - 0.131, Q.75 = - 0.153, Q.95 = - 0.175). Furthermore, the results underscore that an emerging country's political, economic, and financial instabilities are strongly associated with increasing the banking sector's credit risk and a rise in political risk in particular has the most positive prominent impact on the banking sector of countries with a higher degree of non-performing loans (Q.25 = - 0.122, Q.50 = - 0.141, Q.75 = - 0.163, Q.95 = - 0.172). Moreover, the results suggest that, in addition to the banking sector-specific determinants, credit risk is significantly impacted by the financial market development, lending interest rate, and global risk. The results are robust and have significant policy suggestions for many policymakers, bank executives, researchers, and analysts.

How does non-interest income affect bank credit risk? Evidence before and during the COVID-19 pandemic

This paper considers the COVID-19 pandemic's role and investigates the impact of non-interest income on bank credit risk. Specifically, it performs a comparative analysis between before and during the pandemic periods. The data of listed banks are extracted from the BankFocus for 14 Asian emerging markets. The regression results indicate the positive influence of non-interest income on bank credit risk. Interestingly, the magnitude of the impact is higher in the pre-pandemic period, and it significantly reduces during the pandemic period. This study provides implications for bank practitioners and regulators.

How does government-backed finance affect SMEs' crisis predictors?

This paper estimates the impact of public guarantees on crisis predictive indicators among small and mid-size enterprises (SMEs). We use a confidential database provided by the Italian Ministry of Economic Development on the universe of guarantees granted by the Central Guarantee Fund. We apply difference-in-difference regressions and propensity-score matching estimators to a sample of approximately 40,000 SMEs over the 2010-2018 period. We find that obtaining a public guarantee improves profitability both in the short- and medium-term. On the other hand, SMEs' financial health worsens in the short run, but financial burdens are alleviated 2 years after the issuance of a guarantee. The economic and financial effects of government-backed loans are amplified for micro-sized firms, companies operating in the service sector and direct guarantees. Our results can thus support public authorities in designing credit guarantee schemes capable of preventing SMEs' zombification and protecting them from the risk of debt overhang.

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.

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.

Research on influencing factors and transmission mechanisms of green credit risk

Controlling green credit risk is conducive to increasing the confidence of financial institutions, improving the enthusiasm of enterprises for green innovation, and promoting the sustainable development of green credit and the high-quality development of green economy. This paper puts government intervention, green technology innovation, and regulatory action into the same theoretical framework and puts forward the green credit risk transmission mechanisms which involve the transmission relationship and transmission path on the basis of the influencing factors. Then, this research uses stationary time series data (from the "China Statistical Yearbook," the "Statistical Yearbook" of various provinces, the annual reports of listed companies, etc.) to analyze the current situation of green credit risk in different regions. From the systematic perspective, this study verifies the transmission path of green credit risk and tests the rationality and effectiveness of the green credit risk transmission mechanisms. The research shows that the management of green credit risk requires the active actions of government departments, financial institutions, green enterprises, and regulatory departments.

Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness

Assessing the financial stability of the banking industry, particularly in credit risk management, has become extremely crucial in times of uncertainty. Given that, this paper aims to investigate the determinants of the interconnectedness of sectoral credit risk default for developing countries. To that purpose, we employ a dynamic credit risk model that considers a variety of macroeconomic indicators, bank-specific variables, and household characteristics. Moreover, the SURE model is used to analyze empirical data. We find the connection between macroeconomic, bank-specific, and household characteristics, and sectoral default risk. The outcomes of macroeconomic factors demonstrate that few macroeconomic determinants significantly influence the sector's default risk. The empirical results of household components reveal that educated households play a substantial role in decreasing sectoral loan defaults interconnectedness and vice versa. While for bank-specific characteristic, we find that greater bank profitability and specialization have substantially reduced loan defaults.

A novel framework of credit risk feature selection for SMEs during industry 4.0

With the development of industry 4.0, the credit data of SMEs are characterized by a large volume, high speed, diversity and low-value density. How to select the key features that affect the credit risk from the high-dimensional data has become the critical point to accurately measure the credit risk of SMEs and alleviate their financing constraints. In doing so, this paper proposes a credit risk feature selection approach that integrates the binary opposite whale optimization algorithm (BOWOA) and the Kolmogorov-Smirnov (KS) statistic. Furthermore, we use seven machine learning classifiers and three discriminant methods to verify the robustness of the proposed model by using three actual bank data from SMEs. The empirical results show that although no one artificial intelligence credit evaluation method is universal for different SMEs' credit data, the performance of the BOWOA-KS model proposed in this paper is better than other methods if the number of indicators in the optimal subset of indicators and the prediction performance of the classifier are considered simultaneously. By providing a high-dimensional data feature selection method and improving the predictive performance of credit risk, it could help SMEs focus on the factors that will allow them to improve their creditworthiness and more easily access loans from financial institutions. Moreover, it will also help government agencies and policymakers develop policies to help SMEs reduce their credit risks.

COVID-19 and credit risk: A long memory perspective

The COVID-19 pandemic shows significant impacts on credit risk, which is the key concern of corporate bond holders such as insurance companies. Credit risk, quantified by agency credit ratings and credit default swaps (CDS), usually exhibits long-range dependence (LRD) due to potential credit rating persistence. With rescaled range analysis and a novel affine forward intensity model embracing a flexible range of Hurst parameters, our studies on Moody's rating data and CDS prices reveal that default intensities have shifted from the long-range to the short-range dependence regime during the COVID-19 period, implying that the historical credit performance becomes much less relevant for credit prediction during the pandemic. This phenomenon contrasts sharply with previous financial-related crises. Specifically, both the 2008 subprime mortgage and the Eurozone crises did not experience such a great decline in the level of LRD in sovereign CDS. Our work also sheds light on the use of historical series in credit risk prediction for insurers' investment.

Impact of credit, liquidity, and systematic risk on financial structure: comparative investigation from sustainable production

The role of risk assessment and capital structure is vital for the sustainable growth of firms and increasing the shareholders' wealth. This research explores the correlation between firm risk and capital structure using datasets from the sugar and cement sectors of Pakistan as a developing economy. This study is unique as it involved two firms of different nature (sugar firms operate seasonally while cement firms operate yearly) to view the real picture on the impact of risk and structure assessment on firms' credibility and shareholders' wealth. For this purpose, 15-year data (2000-2014) containing the financial statements of the target sectors were collected and the ANOVA analysis was applied with credit risk, liquidity risk, systematic risk, and firm size were used as the regressor variables, firm growth and dividend payout ratio as the control variables, and leverage as the regression variable. The findings showed that credit risk and liquidity risk are significantly correlated with leverage. This suggests that decision-makers pertaining to firms' risk and efficiency must focus more on risk to pursue a stronger and sustainable increase in shareholder wealth.

An ensemble machine learning approach for forecasting credit risk of agricultural SMEs' investments in agriculture 4.0 through supply chain finance

Credit risk imposes itself as a significant barrier of agriculture 4.0 investments in the supply chain finance (SCF) especially for Small and Medium-sized Enterprises. Therefore, it is important for financial service providers (FSPs) to differentiate between low- and high-quality SMEs to accurately forecast the credit risk. This study proposes a novel hybrid ensemble machine learning approach to forecast the credit risk associated with SMEs' agriculture 4.0 investments in SCF. Two core approaches were used, i.e., Rotation Forest algorithm and Logit Boosting algorithm. Key variables influencing the credit risk of agriculture 4.0 investments in SMEs were identified and evaluated using data collected from 216 agricultural SMEs, 195 Leading Enterprises and 104 FSPs operating in African agriculture sector. Besides the classical measures of credit risk assessment without involving SCF, the findings indicate that current ratio, financial leverage, profit margin on sales and growth rate of the agricultural SME are the upmost important variables that SCF actors need to focus on, in order to accurately and optimistically forecast and alleviate credit risk. The output of our study provides useful guidelines for SMEs, as it highlights the conditions under which they would be seen as creditworthy by FSPs. On the other hand, this study encourages the wide application of SCF in financing agriculture 4.0 investments. Due to the model's performance, credit risk forecasting accuracy is improved, which results in future savings and credit risk mitigation in agriculture 4.0 investments of SMEs in SCF.

Carbon neutrality, bank lending, and credit risk: Evidence from the Eurozone

The development of a green financial intermediation channel is imperative to achieve zero-carbon economies. In this study, we assess the impact of carbon-neutral lending on the credit risk in the Eurozone. We employ quarterly data for a sample of 344 lending institutions of 19 member states spanning over ten years from 2011 to 2020. Using two specific credit risk measures, the findings show that the exposure to carbon-neutral lending is negatively related to the default risk. The results remain consistent for the various size sorts, depicting that regardless of the bank size, the impact of green financing on the credit risk is the same. We attribute the credit risk reduction to the lower volatility of the borrowers' earnings and cash flows emanating from their sustainable business model. As a consequence of lower credit risk, financial institutions can benefit from lower loan loss provisions and economic capital requirements. This incentive is vital to increase the carbon neutral credit and contribute towards pro-environmental goals.

Rethinking SME default prediction: a systematic literature review and future perspectives

Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.

COVID-19 implications for banks: evidence from an emerging economy

The COVID-19 pandemic is damaging economies across the world, including financial markets and institutions in all possible dimensions. For banks in particular, the pandemic generates multifaceted crises, mostly through increases in default rates. This is likely to be worse in developing economies with poor financial market architecture. This paper utilizes Bangladesh as a case study of an emerging economy and examines the possible impacts of the pandemic on the country's banking sector. Bangladesh's banking sector already has a high level of non-performing loans (NPLs) and the pandemic is likely to worsen the situation. Using a state-designed stress testing model, the paper estimates the impacts of the COVID-19 pandemic on three particular dimensions-firm value, capital adequacy, and interest income-under different NPL shock scenarios. Findings suggest that all banks are likely to see a fall in risk-weighted asset values, capital adequacy ratios, and interest income at the individual bank and sectoral levels. However, estimates show that larger banks are relatively more vulnerable. The decline in all three dimensions will increase disproportionately if NPL shocks become larger. Findings further show that a 10% NPL shock could force capital adequacy of all banks to go below the minimum BASEL-III requirement, while a shock of 13% or more could turn it to zero or negative at the sectoral level. Findings call for immediate and innovative policy measures to prevent a large-scale and contagious banking crisis in Bangladesh. The paper offers lessons for other developing and emerging economies similar to Bangladesh.

Credit risk evaluation based on social media

Social media has been playing an increasingly important role in the sharing of individuals' opinions on many financial issues, including credit risk in investment decisions. This paper analyzes whether these opinions, which are transmitted through social media, can accurately predict enterprises' future credit risk. We consider financial statements oriented evaluation results based on logit and probit approaches as the benchmarks. We then conduct textual analysis to retrieve both posts and their corresponding commentaries published on two of the most popular social media platforms for financial investors in China. Professional advice from financial analysts is also investigated in this paper. We surprisingly find that the opinions extracted from both posts and commentaries surpass opinions of analysts in terms of credit risk prediction.