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There is a need for research examining how governments and firms responded to the coronavirus disease (COVID-19) pandemic. This study investigates the interdependence between governments and innovative small and medium-sized enterprises (SMEs) during the pandemic in relation to the dynamic capabilities and resource dependence theories.

We use World Bank survey data collected immediately before and after the COVID-19 outbreak and a generalized structural equation model to examine the mediating role of government support in the relationship between firm innovation, resilience and survival.

Innovative SMEs exhibited higher resilience and a better chance of survival during the pandemic, partly due to attracting more government support.

This study offers a novel understanding of the government’s role in supporting innovative SMEs during the pandemic. The findings have implications for how government support policies can limit the deadweight effect and the substitution effect.

Introduction: According to the existing research, one of the key determinants of a company’s survival and market development is its ability to get bank loans or other external sources of finance for business expansion.

Purpose: The study aims to explore the factors affecting access to finance and their effects on the development of medium- and small-sized businesses. These factors include business size and age, profitability, the length of a company’s association with a commercial bank, and banking sector characteristics.

Need for the study: It is particularly crucial for small- and medium-sized businesses since they often have trouble getting funding from banks because they don’t supply the banks with the information they need to assess their loan application prospects, however, when a company’s economic and financial situation improves, banks get access to more information about the firms, and financing is thus more readily available.

Methodology: This research is based on qualitative methods, focus on an elaborative study of the existing literature, and provide suggestions based on the same.

Findings: The findings show that small- and medium-sized businesses, like those in other European nations, have less access to finance than large businesses. It revealed that the company’s size, liquidity, profitability, and banking industry state significantly influence the availability of bank loans.

This study empirically demonstrates a contradiction between pillar 3 of Basel norms III and the designation of Systemically Important Banks (SIBs), also known as Too Big to Fail (TBTF). The objective of this study is threefold, which has been approached in a phased manner. The first is to determine the systemic importance of the banks under study; second, to examine if market discipline exists at different levels of systemic importance of banks and lastly, to examine if the strength of market discipline varies at different levels of systemic importance.

This study is based on all the public and private sector banks operating in the Indian banking sector. The Gaussian Mixture Model algorithm has been utilized to classify banks into distinct levels of systemic importance. Thereafter, market discipline has been observed by analyzing depositors' sentiments toward banks' risk (CAMEL indicators). The analysis has been performed by employing the system Generalized Method of Moments (GMM) to estimate models with different dependent variables.

The findings affirm the existence of market discipline across all levels of systemic importance. However, the strength of market discipline varies with the systemic importance of the banks, with weak market discipline being a negative externality of the SIBs designation.

By employing the Gaussian Mixture Model algorithm to develop a framework for categorizing banks on the basis of their systemic importance, this study is the first to go beyond the conventional method as outlined by the Reserve Bank of India (RBI).

We explore the impact of equity liquidity on a firm’s dynamic leverage adjustments and the moderating impacts of leverage deviation and target instability on the link between equity liquidity and dynamic leverage in the UK market.

In applying the two-step system GMM, we estimate our model by exploring suitable instruments for the dynamic variable(s), i.e. lagged values of the dynamic term(s).

Our analyses document that a firm’s equity liquidity has a positive impact on the speed of adjustment (SOA) of its leverage ratio back to the target ratio in the UK market. We also demonstrate that the positive relationship between liquidity and SOA is more pronounced for firms whose current position is relatively close to their target leverage ratio and whose target ratio is relatively stable.

This study provides important implications for both firms’ managers and investors. Particularly, firms’ managers who wish to increase the leverage SOA to enhance firms’ value need to give great attention to their equity liquidity. Investors who want to evaluate firms’ performance could also consider their equity liquidity and leverage SOA.

We are the first to enrich the literature on leverage adjustments by identifying equity liquidity as a new determinant of SOA in a single developed country with many differences in the structure and development of capital markets, ownership concentration and institutional characteristics. We also provide new empirical evidence of the joint effect of equity liquidity, leverage deviation and target instability on leverage SOA.

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.

This paper aims to investigate the determinants that contribute to the financial stability and banking sector of Palestinian banks listed on the Palestine Stock Exchange (PEX) by using yearly data for the years 2012–2022.

Pooled ordinary least squares (OLS) and two-stage least squares (2SLS) were used to identify the variables and factors affecting the financial stability and banking sector of Palestinian banks. The study’s data were collected from the banks listed on PEX and from the yearly reports posted on the Palestine Monetary Authority’s (PMA) webpage over the years from 2012–2022. According to this research’s analysis, SMEs loans and capital sufficiency have a statistically significant positive impact on the stability of Palestinian banks. Unobserved heterogeneity, simultaneity and dynamic endogeneity are taken into account when using the 2SLS regression approach to adjust for the study endogeneity factor.

The study’s findings show that some factors and determinants might have both good and negative effects on financial stability and banking sector. Loans to small and medium-sized businesses (SMEs) and enough capital are two characteristics that statistically have a major favourable impact on the stability of Palestinian banks since they help the banks withstand deficits. A further potential discovery relates to the favourable effects of financial inclusion (FI) and digital financial services (DFS) on the stability of banks.

This research has faced some limitations, such as the lack of a defined index from the regulatory organizations, this research is based on information from bank annual accounts. It has mostly relied on self-developed or World Bank indexes. Furthermore, the research solely used information from the supply side (banks); demand-side data were not taken into consideration.

This paper has managerial implications for stability of banking sector. The Palestine Monetary Authority, as the central bank, must increase the percentage of bank loans directed to small and medium-sized companies and oblige bank management to adhere to adequate capital standards, which contributes to strengthening the Palestinian banking sector and increasing its profits. The study findings advise banks that are enjoying financial stability to speed up the pace of FI and DFSs because most of these reliable banks have relatively low FI ratios. PMA is responsible for preserving the stability of the financial system. PMA, decision makers and banks management must retain adequate liquidity in their institutions and raise client collateral expectations to raise credit conditions.

This paper adds some contributions to the literature. To adjust for discrepancies between various types of banks, the authors concentrate on conventional and Islamic banks, which enables us to use a homogenous data set as opposed to depending on dichotomous variables. The authors used Z-scores, which have recently been used in research, to measure stability and FI at the level of specific institutions. This research contributes in some key aspects that no prior research has addressed. Conventional banks are different from Islamic banks, and a number of issues might impact their stability. To evaluate the connection between FI and DFSs, it is important to consider the actions of bank regulators.

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.