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This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.

This study empirically investigates the interdependency of select Asian emerging economies along with the financial stress index during the times of the global financial crisis, the Euro crisis and the COVID-19 period. Moreover, it inspects the long-memory effects of the different crises during the study period.

To address the objectives of the study, the authors apply different statistical tools, namely the adjusted correlation coefficient, fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and wavelet coherence model, along with descriptive statistics.

Financial stress is having a prodigious effect on the economic growth of select economies. From the data analysis, it is found that the long-memory effect is noted in the gross domestic product (GDP) for India and Korea only, which implies that the volatility in the GDP series for these two nations demonstrates persistence and dependency on previous values over a lengthy period.

The study is unique of its kind to consider multi-segments within the period of the study to get a clear idea about the effects of the financial stress index on select Asian emerging economies by applying different econometric tools.

The purpose of this study is to understand how managers in entrepreneurial small businesses (ESBs) deal with exogenous (macro) crises, particularly in relation to the breakdown of intra- and inter-stakeholder trust.

Utilising a qualitative approach, we draw lessons from Greek ESBs greatly affected by the 2008–2019 economic and 2020–2022 health crises. Based on 54 in-depth, longitudinal investigations of four ESBs at three time points, this research offers insights on overcoming organisation-stakeholder trust breakdowns that emerg due to crises.

The findings suggest that macro-level crises undermined the foundations of trust-based relationships, creating a trust gap between organisations and their stakeholders and threatening ESBs’ business practices. Our framework suggests that ESBs repair trust relationships, both intra- and inter-organisational, through sense-making of trust breakdown, implementing trust-repair strategies, and then maintaining trust in those stakeholder relationships through challenging crisis periods.

Practitioners can use the suggested framework in relation to overcoming intra- and inter-stakeholder trust breakdowns during macro-level crises.

The paper offers a new framework that can aid entrepreneurs and managers in making sense of repairing and maintaining trust in stakeholder relationships during turbulent times.

The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.

This study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.

The empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.

This study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.

This study aims to measure impact of several firm-specific factors on alternative measures of leverage. The authors also aim to study impact of the subprime crisis on such associations.

The authors utilized an unbalanced panel data of 973 firm-year observations on 47 UK listed non-financial firms for the years 1990–2019. Book-based and market-based long-term and total leverage measures have been used as explained variables. The explanatory variables are profitability, size, two measures of growth, asset tangibility, non-debt tax shields, firm age and product uniqueness. Fixed effect and random effect models with clustered robust standard errors have been utilized for data analysis. To find the effect of subprime crisis, original dataset was split to create pre-crisis and post-crisis datasets.

The authors find that profitability significantly reduces leverage while firms having more tangible assets use significantly more debt in capital structure. Firm size and non-debt tax shield have statistically insignificant positive impact on leverage. Having more unique products reduces use of external debt, albeit insignificantly. Growth, when measured as market-to-book ratio, has inconsistent impact, whereas capital expenditure insignificantly reduces leverage. Age is found to be an insignificant predictor of leverage. After the subprime crisis, firms started relying more on internal fund instead of external debt, more particularly short-term debt. Having more collateral is gradually becoming more important for availing external debt.

Data limitations restrict generalization of the findings.

This is one of the pioneering attempts to show how subprime crisis altered the theoretical domain of capital structure research in the UK.

The current research aims to investigate the impact of coronavirus 2019 (COVID-19) evolution, government stringency measures and economic resilience on stock market volatility in the Middle East and North African (MENA) emerging markets. Other macroeconomic factors were also taken into account.

Based on financial data from 10 selected MENA countries, we tested an integrated framework that has not yet been explored in prior research. The exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) was adopted to analyze data from March 2020 to February 2022.

Our research illustrates the direct and indirect effects of the virus outbreak on stock market stability and reports that economic resilience could alleviate the volatility shock. This finding is robust across the various proxies of economic resilience used in this study. We also argue that the negative impact of the pandemic on equity market variation gets more pronounced in countries with higher level of stringency scores.

Policymakers ought to strengthen their economic structures and reinforce the economic governance at the national level to gain existing and potential investors’ trust and ensure lower stock market volatilities in times of crisis. Our study also recommends some key economic factors to consider while establishing efficient policies to tackle unexpected shocks and prevent financial meltdowns.

Our findings add to the evolving literature on the reaction of economic and financial markets to the sanitary crisis, particularly in developing countries where research is still scarce. This study is the first of its kind to investigate the stock market reaction to stringency measures in the understudied MENA region.

The study attempts to examine the effect of the COVID-19 pandemic on the economic growth and public debt of the Indian economy. The authors also attempt to make quarterly projections of economic growth and external debt (ED) for the next five years. The objective is to understand how much time the economy takes to recover and at what pace. Consequently, this study elucidates the composition of debt after the crisis in the next five years.

To predict India's gross domestic product (GDP) and ED for the next five years, the authors used an auto-regressive integrated moving average (ARIMA) model. This model was built under a Box–Jenkins methodology (Box and Jenkins, 1976) and was subjected to an augmented Dickey–Fuller (ADF) test to check the stationarity of the data. The methodology includes three main steps to estimate and forecast the model: identification, estimation, and diagnostic and forecasting.

The study finds that the outbreak of the COVID-19 pandemic has significant implications for economic growth and public debt. The economy faced contraction in the first quarter of the year 2020 due to the suspension of economic activities and still struggling with the negative values of GDP. The forecasting results reveal that ED will continue to grow to meet the increasing health expenditure needs, and GDP will also bounce back slowly after the end of the year 2021. It has been noticed that the recurrent crisis derails the developing economies from the path of sustainable development to a prolonged economic slump with mounting public debt.

The study examines the impact of the COVID-19 pandemic on economic growth and public debt with particular reference to India. To the best of the authors’ knowledge, this is the first time the quarterly projections for GDP and ED have been made after the COVID-19 crisis.

Agricultural banks likely respond differently to economic downturns compared to nonagricultural banks. Limited previous research has examined the performance of agricultural banks under economic crisis and in the presence of banking regulations. This study aims to explore agricultural banks' responses to economic and regulation shocks relative to nonagricultural banks.

This study uses bank-quarter level data from 2002 to 2022 for virtually all commercial banks in the U.S. In this research, the Z-score measures the bank’s default risk, the return on assets measures bank profitability and changes in amount of farm loans indicate the wider impact on the agricultural sector. Effects of the financial crisis, Basel III reforms to banking regulation and the coronavirus (COVID-19) pandemic on these banking measures are assessed using distinct empirical frameworks. The empirical estimations use various subsamples based on bank types, bank sizes and time periods.

Economic downturns are associated with fluctuations in returns and the risk of default of commercial banks. Agricultural banks appeared to be more resilient to economic downturns than nonagricultural banks. However, Basel III regulated agricultural banks were more likely to fail amidst the pandemic-related economic shocks than the regulated non-agricultural banks.

This study examines the resiliency of agricultural banks during economic downturns and under postfinancial crisis regulation. This is one of the first empirical works to analyze the effectiveness of Basel III regulation across bank types and sizes considering the COVID-19 pandemic. The key finding suggests that banking regulation should consider not only size heterogeneity but also the heterogeneity in lending portfolios.

The purpose of this paper is to study the impact of economic factors on foreign direct investment (FDI) inflows into Asian region before and after the COVID-19 pandemic.

The study used the generalized method of moments (GMM) technique to examine the impact of economic growth, domestic investment and trade openness on FDI in the Asian region, in two periods from 1996 to 2018 and from 2019 to 2020.

In the pre-COVID-19 period, the estimated result shows that the economic growth, domestic investment, imports and exports positively impact FDI. In the post-COVID-19 period, the FDI is influenced by the strength of the economic characteristics of the region. The main findings indicate that economic growth has a positive and significant effect on FDI inflows into Asia. The findings also show that the economic resilience to attract FDI in Asia is significantly affected by economic growth and positively affected by trade openness and government responses during the pandemic.

The study suggests the Asian governments increasing the domestic investment and improving the quality of trade openness.

In the fashion of Martin (2012), we develop an innovative application to a standard, well-grounded methodology to investigate resilience in two critical dimensions: recovery and resistance. Our novel approach allows us to investigate the resilience performance to the 2008 financial crisis within countries of this macro-region according to their shock isolation and absorptive capacities.

By individually estimating six open economy DSGE models within the Central Europe and Baltic macro-region, we identify the business-cycle-volatility drivers for each country. Then, we use the outcome of our six estimates to conduct a principal component analysis to determine structural common characteristics required to explain economic resilience in the CEB macro-region.

In terms of resilience, Central European economies exhibit quite similar paths in terms of recovery, meaning they have similar economic structures. By contrast, Baltic countries behave differently, being outliers in opposite extreme positions. The contrary occurs for resistance: Baltic countries share a similar ranking, whereas Central European economies exhibit substantial differences.

It is important to acknowledge that a limitation of the analysis is that we explicitly consider each country as a stand-alone open economy which are subject to stochastic disturbances. Precisely, we do not model trade or other interactions across countries within the CEB region and with the rest of the world. Consequently, spillover effects in the aftermath of the shock are not accounted for.

We estimate the relative vulnerability or sensitivity of economies within the macro-region to disturbances and disruptions (resistance) and the speed and extent of recovery from such a disruption or recession (recovery). First, we built two different kinds of measures of resilience by aggregating the estimated parameters through non-centered and centered principal component analysis. Then, we use our model to investigate the relation between financial shock and the economic resilience across the region. The approach can be applied to several case studies, parsimoniously.