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The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

This study aims to examine the impact of intellectual capital (IC) on default risk in Indian companies listed on the National Stock Exchange.

This study applies panel data regression analysis to derive a relationship between IC and default risk for the sample period 2013–2022. The value-added intellectual coefficient (VAIC) of Pulic (2000) has been applied to measure IC performance, and default risk is estimated using the revised Z-score model of Altman (2000).

The results revealed a positive association between Z-score and VAIC. It implies that a higher value of VAIC improves financial stability and leads to a lower likelihood of default. The findings further suggest that new default forecasting models can be experimented with IC indicators for better default prediction.

The findings can have implications for investors and banks. This paper provides evidence of IC performance in improving the financial solvency of firms. Investors and financial institutions should invest their resources in a healthy firm that effectively manages and invests in their IC. It will eventually award investors and creditors high returns through efficient value-creation processes.

This study provides evidence of IC performance in improving the financial solvency of Indian high-defaulting firms, which lacks sufficient evidence in this domain of research. Numerous studies exist examining the relationship between firm performance and IC value, but this area is inadequately focused and underresearched. This study, therefore, fills the research gap from an Indian perspective.

The aim of this study is to analyze the influence mechanism of real estate enterprises' status on debt default risk and explore the heterogeneity effect of the characteristics of enterprises.

Against the background of the “three red lines” regulation of the financing of real estate enterprises and the COVID-19 pandemic, the authors select 123 real estate enterprises listed on China's Shanghai and Shenzhen A-shares markets from the first quarter of 2021 to the second quarter of 2022 as a research sample. The social network analysis method and Z-score financial risk early warning model are used to measure real estate enterprises' status and debt default risk. The authors construct a panel regression model to analyze how the status of real estate enterprises influences their debt default risk.

The results show that the status of real estate enterprises negatively and significantly affects their debt default risk. Economic policy uncertainty and financing constraints play negative moderating and mediating roles, respectively. Further research has found that the effect of real estate enterprises' status on debt default risk is characterized by heterogeneity in equity characteristics, i.e. it is significant in the sample of nonstate-owned enterprises but not in the sample of state-owned enterprises.

It is helpful for real estate enterprises to attach importance to the value of social networks, and the authors provide policy suggestions for real estate enterprises to constantly improve their risk management systems.

Using economic policy uncertainty as the moderating variable and financing constraints as the mediating variable, the authors analyze how the status of real estate enterprises influences debt default risk, which contributes to a better understanding of the formation of the debt default risk of real estate enterprises.

This study aims to demonstrate and measure the impact of liquidity shocks on a bank’s solvency, especially when the bank does not hold sufficient liquid assets.

The proposed model is an extension of Merton’s (1974) model. It assesses the bank’s probability of default over one or two (short) periods relative to liquidity shocks. The shock scenarios are materialised by different net demands for the withdrawal of funds (NDWF) and may lead the bank to sell illiquid assets at a depreciated value. We consider the possibility of second-round effects at the beginning of the second period by introducing the probability of their occurrence. This probability depends on the proportion of illiquid assets put up for sale following the initial shock in different dependency scenarios.

We observe a positive relationship between the initial NDWF and the bank’s probability of default (particularly over the second period, which is conditional on the second-round effects). However, this relationship is not linear, and a significant proportion of liquid assets makes it possible to attenuate or even eliminate the effects of shock scenarios on bank solvency.

The proposed model enables banks to determine the necessary level of liquid assets, allowing them to resist (i.e. remain solvent) different liquidity shock scenarios for both periods (including eventual second-round effects) under the assumptions considered. Therefore, it can contribute to complementing or improving current internal liquidity adequacy assessment processes (ILAAPs).

The proposed microprudential approach consists of measuring the impact of liquidity risk on a bank’s solvency, complementing the current prudential framework in which these two topics are treated separately. It also complements the existing literature, in which the impact of liquidity risk on solvency risk has not been sufficiently studied. Finally, our model allows banks to manage liquidity using a solvency approach.

The blue and green firms are notable contributors to sustainable development. Similar to other businesses in circular economies, blue and green firms also face financing constraints. This paper aims to assess whether blue and green lending help in optimizing the interest rate spreads and the likelihood of default.

This analysis is based on an unbalanced panel of banks from 20 eurozone countries for eleven years between 2012 and 2022. The key indicators of banking include interest rate spread and a market-based probability of default. The paper assesses how these indicators are influenced by exposure to green and blue firms after controlling for several exogenous factors.

The results show a positive relationship between green and blue lending and spread, while there is a negative link with the probability of default. This confirms that the blue and green exposure positively supports the credit portfolio both in terms of profitability and risk management.

The banking system is among the key contributors to corporate finance and to enable continuous access to sustainable finance, the banking firms must be incentivized. While many studies analyze the impact of green lending, to the best of the authors’ knowledge, this study is among the very few that extend this analysis to blue economy firms.

The novel coronavirus (COVID-19) has caused financial stress and limited their lending agility, resulting in more non-performing loans (NPLs) and lower performance during the II wave of the coronavirus crisis. Therefore, it is essential to identify the risky factors influencing the financial performance of Indian banks spanning 2018–2022.

Our sample consists of a balanced panel dataset of 75 scheduled commercial banks from three different ownership groups, including public, private and foreign banks, that were actively engaged in their operations during 2018–2022. Factor identification is performed via a fixed-effects model (FEM) that solves the issue of heterogeneity across different with banks over time. Additionally, to ensure the robustness of our findings, we also identify the risky drivers of the financial performance of Indian banks using an alternative measure, the pooled ordinary least squares (OLS) model.

Empirical evidence indicates that default risk, solvency risk and COVAR reduce financial performance in India. However, high liquidity, Z-score and the COVID-19 crisis enhance the financial performance of Indian banks. Unsystematic risk and systemic risk factors play an important role in determining the prognosis of COVID-19. The study supports the “bad-management,” “moral hazard” and “tail risk spillover of a single bank to the system” hypotheses. Public sector banks (PSBs) have considerable potential to achieve financial performance while controlling unsystematic risk and exogenous shocks relative to their peer group. Finally, robustness check estimates confirm the coefficients of the main model.

This study contributes to the knowledge in the banking literature by identifying risk factors that may affect financial performance during a crisis nexus and providing information about preventive measures. These insights are valuable to bankers, academics, managers and regulators for policy formulation. The findings of this paper provide important insights by considering all the risk factors that may be responsible for reducing the probability of financial performance in the banking system of an emerging market economy.

The empirical analysis has been done with a fresh perspective to consider unsystematic risk, systemic risk and exogenous risk (COVID-19) with the financial performance of Indian banks. Furthermore, none of the existing banking literature explicitly explores the drivers of the I and II waves of COVID-19 while considering COVID-19 as a dependent variable. Therefore, the aim of the present study is to make efforts in this direction.

This study aims to empirically investigate the impact of political instability on the banking stability of the dual banking system in the Middle East and North African (MENA) countries.

The study measures banking stability with probability of default (PD) and Zscore by employing the generalised method of moment (GMM) between 2007 and 2021 on the dual banking system in the region. The authors further estimate short-long-run situations coupled with a robustness test using a generalised least square (GLS) model.

The authors' findings indicate that institutional factors of political stability, crisis period, high-crisis countries, law and order and macroeconomic indicators influence the two types of banking stability in the region. The authors found the consistency of the factors explaining stability in the region in both short-and long-run situations. Consequently, the study also reveals the adverse effects of crisis periods and high-crisis countries on banking stability.

The results of this study explicitly identify the critical need for sustaining political stability and abiding by laws and order to achieve dual banking stability in the region. Therefore, policymakers may consider allowing the region's banks to operate beyond retail banking since diversification enhances banking stability.

The authors' study balances by employing dual stability measurement in predicting the impact of political instability, law and order and other indicators on the MENA region's two banking models. This study uncovers the effect of the global crisis period on banking stability and high-crisis countries in the region and verifies the models' robustness.

This paper aims to propose the mechanism of cross-network effect embedded, which can help cross-border e-commerce (CBEC) platforms strengthen cooperative relationships with sellers more equitably and effectively by using the network structural characteristics of the platforms themselves.

A two-stage evolutionary game model has been used to confirm the influence factors. The mathematical derivation of evolutionary game analysis is combined with the simulation method to examine the role of cross-network effect in cooperation. The evolutionary game model based on the cross-network effect is proposed to achieve better adaptability to the study of cooperation strategy from the two-sided market perspective.

The evolutionary game model captures the interactions of cross-network effect and the influence factors from a dynamic perspective. The cross-network effect has a certain substitution on the revenue-sharing rate of SMEs. CBEC platforms can enhance the connection between consumers and the website by improving the level of construction, which is a good way to attract sellers more cost-effectively and efficiently.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specificCBEC platforms.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specific CBEC platforms.

Investigations that study cooperation strategy from the cross-network effect perspective in CBEC are limited. The research figured out which influence factors are affected by the cross-network effect in cooperation. A two-stage evolutionary game model was proposed to explain the interaction of the factors. The evolutionary game analysis with a simulation method was combined to highlight the role of cross-network effect on cooperation strategy to give a deeper investigation into the sustainable cooperation ofCBEC.

This study aims to examine the nexus of corporate governance with firms' financial risk-taking behavior under the corporate social responsibility (CSR) disclosures in the context of non-financial listed firms of an emerging economy.

This study investigates the relationship between corporate governance as evaluated by an index and several financial risks, including idiosyncratic, default and systematic risks. The connection of corporate governance with financial risks is also studied while considering the moderation of CSR disclosures. The data are collected from 2014 to 2018 of 73 top 100-index listed non-financial firms of Pakistan Stock Exchange (PSX). Panel regression fixed effect and 2-step generalized method of moments techniques are applied to confirm the hypothesis along with the diagnostic tests to confirm that all outcomes of models must be authentic and reliable.

The study’s findings confirm that enhancing the overall corporate governance measures resulted in an augment in the firm’s risk due to weak control and regulations prevailing in emerging economies. Moreover, CSR disclosures enhance stakeholder information, lessen information asymmetry about management policies and mitigate the risk associated with operational uncertainties.

This study has a practical implementation to policymakers that effective monitoring and controlling measures facilitate the corporate management for minimizing the financial risks. Further, the study’s findings shed light that implementing corporate governance measures is not enough to mitigate financial risks until supervisory measures in the form of CSR disclosures are not taken to analyse corporate governance effectiveness.

This paper enhances the key findings in the literature by examining the role of corporate governance measures with respect to firms’ financial risks considering the moderating role of CSR disclosures. Furthermore, this research adds to the body of knowledge regarding the implementation of monitoring measures that assist in the mitigation of firms’ financial risks hence firm value.

The main purpose of this paper is to establish the mediating effect of credit counselling in the relationship between access to microcredit and survival of micro small and medium-sized enterprises (MSMEs) in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.

Structural equation modelling (SEM) through SmartPLS 4.0 was used to generate the standardized parameters to test whether credit counselling mediates the relationship between access to microcredit and survival of MSMEs in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.

The SEM bootstrap results revealed that credit counselling enhances access to microcredit by 27% to promote survival of MSMEs in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.

The current study focused only on women MSMEs. Future studies may possibly collect data from all the MSMEs to draw better generalization of the findings within the sector.

The findings can help public finance policy to ensure provision of credit counselling to microentrepreneurs who borrow from different financial institutions to reduce the problem of loan defaults and delinquency rampant in lending. This could be done through conducting routine business education and counselling sessions for microentrepreneurs who often need credit to grow their businesses.

This study is amongst the first few studies to establish the mediating effect of credit counselling in the relationship between access to microcredit and survival of MSMEs in developing countries in sub-Saharan Africa in the aftermath of COVID-19 pandemic with data collected from rural Uganda. There is a dearth in literature and theory on the rehabilitative and preventive role of credit counselling in reducing repayment defaults amongst borrowers within the credit market to spur survival of MSMEs seen as the main enabler of economic growth, especially in developing countries. In fact, credit counselling acts as a safety net by substituting financial literacy and education to solve the rampant problem of overindebtedness amongst borrowers who are debt illiterate within the credit market.