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This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

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 study endeavors to explore whether the financialization of agricultural commodities, traditionally viewed as a catalyst for price volatility, has any repercussions on food security in BRICS economies.

The empirical analysis employs the examination of three agricultural commodities, namely wheat, maize and soybean. Utilizing data from the Chicago Board of Trade on futures trading for these commodities, we focus on parameters such as annual trading volume, annual open interest contracts and the ratio of annual trading volume to annual open interest contracts. The study spans the period 2000–2021, encompassing pre- and post-financial crisis analyses and specifically explores the BRICS countries namely the Brazil, Russia, India, China and South Africa. To scrutinize the connections between financialization indicators and food security measures, the analysis employs econometric techniques such as panel data regression analysis and a moderating effects model.

The results indicate that the financialization of agricultural products contributes to the heightened food price volatility and has adverse effects on food security in emerging economies. Furthermore, the study reveals that the impact of the financialization of agricultural commodities on food security was more pronounced in emerging nations after the global financial crisis of 2008 compared to the pre-crisis period.

This paper seeks to draw increased attention to the financialization of agricultural commodities by presenting empirical evidence of its potential impact on food security in BRICS economies. The findings serve as a valuable guide for policymakers, offering insights to help them safeguard the security and availability of the world’s food supply.

Very few studies have explored the effect of financialization of agricultural commodities on food security covering a sample of developing economies, with sample period from 2000 to 2021, especially at the individual agriculture commodity level. Understanding the evolving effects of financialization is further improved by comparing pre and post-financial crisis times.

This study aims to examine the impact of renewable energy consumption on agricultural productivity while accounting for the effect of financial inclusion and foreign direct investment in Brazil, Russia, India, China and South Africa (BRICS) countries during 2000–2020.

The study has used the latest data from World Bank and International Monetary Fund databases. The dependent variable in the study is agricultural productivity. Renewable energy consumption, carbon emissions, financial inclusion and foreign direct investment are independent variables. Autoregressive distributed lag (ARDL) approach was used to examine the short-run and long-run impact of renewable energy consumption, carbon emissions, foreign direct investment and financial inclusion on agricultural productivity.

The findings imply that consumption of renewable energy, carbon emissions and foreign direct investment have a positive impact on agricultural productivity while financial inclusion in terms of access does not seem to have any significant impact on agricultural productivity. Providing farmers, access to financial services can be beneficial, but its usage holds more importance in impacting rural outcomes. The problem lies in the fact that there is still a gap between access and usage of financial services.

Policymakers should encourage the increase in the usage of renewable energy and become less reliant on non-renewable energy sources which will eventually help in tackling the problems associated with climate change as well as enhance agricultural productivity.

Most of the earlier studies were based on tabular analysis without any empirical base to establish the causal relationship between determinants of agricultural productivity and renewable energy consumption. These studies were also limited to a few regions. The study is one of its kind in exploring the severity of various factors that determine agricultural productivity in the context of emerging economies like BRICS while accounting for the effect of financial inclusion and foreign direct investment.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This paper aims to examine Nigeria’s dynamic output and output volatility connectedness with USA, China and India using quarterly data from 1981Q1 to 2019Q4.

The study adopted the network approach of Diebold and Yilmaz (2014) and used the normalized generalized forecast error variance decomposition from an underlying vector error correction model to build connectedness measures.

The findings show that the global financial crisis (GFC) increased the connectedness index far more than the 2016 Nigeria economic recession. The moderate effect of the 2016 Nigeria economic recession on the connectedness index underscores the fact that Nigeria is a small, open economy with minimal capacity to spread output shock. For both real output and its volatility, the total connectedness index rose smoothly and systematically through time, thereby leaving the economies more connected in the long run.

To the best of the authors’ knowledge, this paper is among the first to examine Nigeria’s dynamic output and output volatility connectedness with the USA, China and India using new empirical insights from the GFC versus 2016 Nigerian recession. The study, therefore, concludes that the Nigerian economy should be diversified immediately as a hedge against future real output shocks, while the USA, China and India should maintain and sustain their current policy frameworks to remain less vulnerable to real output shocks.

In order to achieve sustainable development objectives, safeguard the ecosystem, combat global warming and preserve biodiversity for a more sustainable and secure future, the ecological footprint (EF) must be reduced. Therefore, embracing holistic methods, emphasizing renewable energy (RN) and environmental taxes (ET) is crucial. Therefore, the present study aims to capture the effect of ET and RN on EF in Germany.

To achieve this aim, the novel Fourier-based Autoregressive Distributive Lag (ADL) cointegration and the time and frequency-based connections among the variables are investigated in this work throughout the 1994–2021 time span using the wavelet analytic methods, including wavelet power spectrum (WPS) and wavelet coherence (WC) methods, respectively.

The study’s results express that (1) RN, ET and EF are cointegrated in the long run; (2) EF and RN have volatility; (3) RN use in Germany prevents environmental deterioration and (4) ET decreases EF.

The research findings imply that Germany needs rigorous environmental restrictions and enforcement of alternate energy sources for energy use plans and sustainable production objectives.

To the best of our knowledge, the effect of RN and ET on EF in Germany has not been comprehensively explored by using newly developed econometrics techniques and a single dataset. Therefore, the study provides important policy implementations for the German government and is also likely to open debate on the concept.

This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.

The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.

Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.

The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

This study examines the impact of economic policy uncertainty (EPU) on the investment-cash flow sensitivity (ICFS) of Indian manufacturing firms.

This study uses the fixed-effect method to investigate the effect of EPU on ICFS from 2004 to 2019.

This study finds that EPU increases ICFS, which is more (less) during the crisis (before and post-crisis) period. The authors also find that the effect of EPU on ICFS is more for smaller, younger and standalone (SA) firms than the larger, matured and business group affiliated (BGA) firms. This study also reveals that EPU reduces corporate investment (CI). Further, the authors find that cash flow is more significant for the investment of financially constrained firms and the negative effect of EPU is more for these firms.

This study considers the Indian manufacturing sector. Therefore, this study can be extended by analyzing the relationship between EPU and ICFS for the service sector.

First, this study can be useful for corporates, academicians and government bodies to understand the effect of EPU on ICFS and CI. Second, this study will help corporates to focus on internal funds to finance corporates' investment during the crisis period because EPU increases the cost of external finance which may increase ICFS and reduce CI. Third, lending agencies, investors and stakeholders should also focus on the firm's nature, ownership, size and age because these factors play a crucial role to reduce or increase the negative effect of EPU on ICFS. Fourth, the Government should make appropriate policy measures in terms of concessional interest rates to increase the easy availability of external finance for SA, small size, and young firms to reduce the negative effect of EPU on CI because these firms are considered as more financially constrained firms.

This study adds new inputs to the current literature of EPU in several ways. First, this study is one of the main studies focused on the relationship between EPU and ICFS (CI). Especially in emerging countries like India, examining this relationship extends previous research. Second, this study also examines the impact of EPU on ICFS for BGA, SA, small, large, matured and young firms as well as crisis and non-crisis periods. Third, this study uses the sample of the Indian manufacturing sector which has emerged the qualities to become a global manufacturing hub and attracting global investors. Therefore, examining the effect of EPU on ICFS for these firms will be more interesting.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.