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This study examines the financial implications of religious restrictions by analyzing how changes in the Shariah compliance status of stocks affect their price and trading volume. Employing the market model in an event study approach and using data from Malaysian firms between 2000 and 2016, the study focuses on 30 announcements from the Security Commission Malaysia’s List of Shariah Compliant Securities (LSCS), resulting in 370 additions and 284 deletions from the compliance list. The findings indicate that the inclusion of stocks in the compliance list generates longterm demand driven by Islamic institutional and Muslim retail investors, leading to a permanent increase in abnormal returns and trading volume. Conversely, the removal of stocks from the compliance list induces short-term negative abnormal returns and increased trading volume, reflecting selling pressure. These results contribute to understanding the financial consequences of religious restrictions, highlighting the influence of Shariah compliance on stock market performance and investor behaviour.

Escalating utility costs and challenging financial circumstances make it increasingly difficult for consumers to afford essential services. This financial strain also spills over into the workplace, where employees resort to pilfering company assets, resulting in significant annual losses for these utility firms. Our study aims to identify the organizational factors and individual personality traits that motivate employees to engage in cable theft and vandalism, subsequently driving up tariffs. Utilizing the frameworks of the Reasoned Action Theory (TRA) and the Theory of Planned Behavior (TPB), we seek to elucidate the underlying motivations for such theft and destruction of property. Factors such as an unsatisfactory work environment, perceived unfair treatment by management, subpar wages, and easy opportunities for theft are identified as key motivators. To effectively mitigate these issues, targeted policy measures and strategies need to be implemented to influence employee attitudes and perceptions, thereby reducing incidents of theft and vandalism.

AbstractDrawing upon the resource-based view of the firm, this article employs this perspective to examine how new venture adaptiveness and innovative ambidexterity mediate the relationship between entrepreneurial bricolage and new venture growth within Bangladesh’s Ready-Made Garment Industry. The research employed purposive, convenience and snowball sampling techniques to gather data from 303 individuals engaged in wholesale and retail businesses. The study’s findings indicate a positive correlation between entrepreneurial bricolage and new venture growth. Moreover, the mediating effects of new venture adaptiveness and innovative ambidexterity in the relationship between entrepreneurial bricolage and new venture growth are statistically significant. This article proposes a set of tactics aimed at enhancing resource reconfiguration through adaptability and explores novel approaches to optimize resource effectiveness. The study offers substantial insights into new venture growth that could prove valuable to policymakers, entrepreneurs and authorities operating within Bangladesh’s Ready-Made Garment Industry.

AbstractOne of the most pressing issues facing developing economies is how to provide expansion capital for existing businesses. However, this issue is more pressing in China, where private enterprises suffer significant financial constraints from capital market limitations. Therefore, the significance of obtaining third-party loan guarantees rises among private firms in the secondary loan market. This study investigates the relationship between loan guarantees and the firm’s cost of debt and the moderating effect of management earnings forecasts. We find that loan guarantees have a significant negative relationship with the firm’s cost of debt. However, a positive relationship between information asymmetry measures and loan guarantees is more pronounced, suggesting that loan guarantees reduce the significance of information asymmetry issues, which impair borrowing firms’ re-payment ability and increase the credit risk of guarantors and banks. In contrast, frequent and quality management earnings forecasts help firms to build their reputation in the market by reducing the concerns of information asymmetry, information risk, agency problems, and loan repayment with banks, which, in turn, benefit firms in reducing their cost of debt. Our study results are robust to the use of two-stage least square analysis, and Heckman two-stage treatment effect model. This work offers the latest contribution to the recent understanding of the effects of loan guarantees in reducing the cost of debt and the vital role of management earnings forecasts in firms’ growth.

Abstract Using a wavelet coherence approach, this study investigates the relationship between Bitcoin return and Bitcoin-specific sentiment from January 1, 2016 to June 30, 2021, covering the COVID-19 pandemic period. The results reveal that before the pandemic, sentiment positively drove prices, especially for relatively higher frequencies (2–18 weeks). During the pandemic, the relationship was still positive, but interestingly, the lead-lag relationship disappeared. Employing partial wavelet tools, we factor out the number of COVID-19 cases and deaths and the Equity Market Volatility Infectious Disease Tracker index to observe the direct relationship between a change in sentiment and return. Our results robustly reveal that, before the pandemic, sentiment had a positive effect on return. Although positive coherence still existed during the pandemic, the lead-lag relationship disappeared again. Thus, the causal relationship that states that sentiment leads to return can only be integrated into short-term trading strategies (up to six weeks frequency).

Since the first case of COVID-19 in Turkey, there has been a lingering question as elsewhere in the world: “When will or should the government impose severe restrictions to protect public health?” From a public health perspective, there is value in developing a model to support proactive implementation of social policies. This study aimed to show the benefits of using a novel econometric test (the Generalized Supremum Augmented Dickey-Fuller Test) to detect explosive behavior (bubbles) in Turkey’s daily COVID-19 cases and deaths. Results from the analysis demonstrated a link between identified explosive episodes and critical public health decisions, especially in the case of daily new deaths. They also showed a negative relationship between the formation of exuberant behavior during the pandemic and the vaccination rate. Public health policymakers can incorporate this method into their arsenal to evaluate the overall health situation in combating the pandemic and respond accordingly. Furthermore, among the lessons learned from the Turkish experience is the importance of having a coronavirus scientific advisory board in the decision-making process and the ability to promptly implement policy measures. JEL Codes: C22, C58, I10, I18

This study analyzes the impact of investor sentiment on firm's stock price crash risk by using Chinese A-Share firms data this study assesses the potency and existence of a relationship between crash risk and investor sentiment in the Chinese stock market and introduces analyst herding as a mediating variable for explaining the relationship between crash risk and investor sentiment. By utilizing a large data set of A-share listed firms on Chinese stock exchanges, comprising of 19,371 firm-year observations for the period of 2004–2019, an investor sentiment index is constructed. Results point towards a positive significant relation between stock price crash risk and investor sentiment. Furthermore, stock price crash is positively correlated with analyst herding i.e. it significantly mediates between stock price crash risk and investor sentiment. By measuring the relationship between crash risk, investor sentiment, and analyst herding this study provides systematic support on the mediating role of analyst herding in deepening the market sentiment which results in crash risk. These findings are robust by utilizing alternate proxies and controlling for firm specific variables, economy-wide shocks, and time trends year fixed effects.

This study examines how blockchain and digital currency have affected the supply of credit and financial stability. It pays particular attention to industry-based analyses and options presented by cryptocurrencies, stablecoins, and digital currencies for the credit supply and financial stability. A positivistic or quantitative research design is employed. The method of data collection is a survey-based questionnaire, as well as the time interval data method, from December 2021 to December 2022. The study sample comprises of five industrial zones of Punjab. The respondents are businessmen, managers, and employees (N = 449). The study finds that the use of various digital currencies quickly transforms business. The study shows that most industries do not require central banks and, instead, concentrate on modern digital currency and blockchain systems for monetary transfers. The private and public models of physical money will likely fail in the future. Rather, central banks should adopt digital currency and blockchain with an online technological payment strategy in order to enhance domestic financial stability and payment systems.

This paper examines how small and medium-size enterprises (SMEs) in Istanbul managed their financial needs during the COVID-19 pandemic. A unique survey was conducted in May–June 2021 to analyze the effect of the pandemic on financial conditions and access to finance. The paper maps the differences between firms in terms of their financing conditions and behavior based on their size during the pandemic. The novel data set helps to conceptualize the impact of the COVID-19 pandemic on SMEs. The paper makes a contribution to the literature through using a large number of variables related to firms’ financial conditions and opportunities (e.g., credit restructuring, debt postponing, capital injection). The paper hypothesizes that SMEs are less likely than large firms to access formal finance opportunities, but they tend to rely more on informal financing. The empirical findings suggest that, during the pandemic, micro and small firms tend to borrow more from their acquaintances, such as relatives and friends. Micro firms are less likely to restructure their outstanding loans, borrow from banks, or inject capital. Furthermore, micro firms tend to cut their costs more to avoid further difficulty in their financiaVl positions. Micro and small firms tend to apply for bank loans less than large firms, while medium-size firms are more likely to apply. Micro and small firms are more inclined to report difficulty in accessing credit.

In times of crisis, stock markets experience a significant increase in return volatility, which leads to spillovers across equity sectors. The purpose of this study is to investigate the asymmetric spillovers across ten U.S. equity sectors, representing different industries. Daily prices of sector indices were collected from 02 January 2018 to 22 October 2021 for the analysis. In addition, the study applied Diebold and Yilmaz's (2012) dynamic spillover methodology, along with the static and rolling windows phenomena, to examine the daily returns spillovers across sector indices. The results indicate that 82 % of volatility forecast error variance in U.S sector indices is due to the spillover effect. Moreover, both industrials and financials exhibit the highest gross spillovers to other sectors, while they also receive the highest spillovers from other sectors. Furthermore, the oil and gas sector and utilities sector receive the highest net returns spillovers. These empirical findings provide crucial information regarding the interdependence of U.S. sector indices during the COVID-19 pandemic, which is relevant for investors and practitioners.

The COVID-19 pandemic has resulted in significant financial losses globally, increasing the volatility of financial assets. Thus, this study models the stock market volatility of developed economies during the COVID-19 pandemic. For this purpose, we used the GJR-GARCH (Albulescu, 2020; Albulescu, 2020) [1,1] econometric model on the daily time series returns data ranging from 01st-July-2019 to 18th-November-2020. The entire dataset was equally divided into two subsets; before COVID-19, and after the COVID-19. The empirical results of this study showed the presence of volatility clustering, leverage effect, and excess kurtosis indicating leptokurtic phenomena in all stock indices returns. In addition to this, it can be noted that compared to before COVID-19, the stock markets showed negative returns, and increased volatility during the COVID-19. Hence, based on these findings, this study provides significant insights for global stock market investors and economic policymakers regarding financial portfolio construction particularly during crises times.

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.

Companies use either internal or external sources of financing to fund their operating or capital expenditure. When internal resources prove inadequate, businesses may turn to external financing options, such as issuing debt or equity in the capital market. In bank-dominated financial markets, cash credit is the primary funding source for most companies. In addition to cash credit, banks provide noncash credit instruments, such as letters of credit and letters of guarantee for international trading activities, obtaining working capital, or supporting participation in public and private tenders, for a fee. Surprisingly, prior research has not thoroughly explored the impact of noncash credit and the maturity breakdown of cash credit on corporate financial performance. Our study fills this gap by analyzing the effects of these factors on profitability of firms traded in Borsa Istanbul. The findings reveal a significant positive impact on firms' profitability of both noncash credit and long-term cash credit, but cash credit with shorter maturity does not significantly contribute to profitability. Moreover, financial risk, cash ratio, firm size, and inflation are identified as strong drivers of firms' financial performance. These findings have important implications for firms, banks, and regulators.

AbstractRemittances have become an increasingly important source of external financing for the low-income countries of Central Asia. The impact of Foreign remittances upon poverty alleviation has received substantial importance in academic writings, but very few studies have investigated the Central Asian region. Tajikistan was one of the poorest state of the former Soviet Republics, and it was further devastated by the twin shocks of the post-Soviet transition in 1991 and the civil war from to 1992–1997. The labors of Tajikistan mainly migrate to Russia and other countries of Central Asia for earning their livelihoods. The remittances from Russia to Tajikistan amounted to approximately $ 2.5 Billion in 2019 (accounting for 33% of their GDP). In this study we explored the impact of remittances on poverty alleviation of Tajikistan by taking the data for the variables like remittances, poverty size, capital formation, Per Capita GDP, inflation, and unemployment. We took the data from the World Development Bank Indicators for the time period 2010–2019. Initially, we ran the diagnostic tests including the unit root test for stationarity, the Pearson correlation test for multicollinearity, the Bruesch-Pagan/Cook-Weisberg test for heteroscedasticity, and the Wooldridge test for autocorrelation. After running the diagnostic test we applied the gowth-poverty model for gauging the relationship between the remittances and the poverty and we found a significant negative relationship between foreign remittances and poverty alleviation in case of Tajikistan.

AbstractGlobally the COVID-19 pandemic has had a significant impact on both individuals and organizations. Challenges on the financial, psychological, and physical levels have been felt, especially in the business sector. The purpose of this research is to examine how COVID-19 has financially affected firms in Pakistan and the United States. In order to do this, surveys and interviews with experts in various business disciplines were carried out to utilize convenience and snowball sampling techniques. Partial least squares (Smart PLS) statistical analysis employing structural equation modeling was used in the research. The results showed that COVID-19 greatly impacted the workers’ financial and psychological health. The research also demonstrated that moderators were crucial in lessening the pandemic’s effects. Discussion in this research also includes the study’s ramifications and practical implications.

This research investigates the psychological health of tourism industry’s workforce in the midst of COVID-19 in the context of Chinese Tourism Industry. We have employed stress theory for the predictive analysis and hypothesis crafting in terms of mediator (financial dissatisfaction) and moderators (quality of marital relationship and family income). Path analysis has been used to evaluate different relationships while adopting ABCX family stress model. The findings confirm that alongside the economic and livelihood impacts of the unemployment, the tourism industry’s workforce has also suffered from psychological externalities of the phenomenon, not only individually but also in terms of their relationships at different levels. It further elaborates the inculcation of resilience in the tourism workforce on part of the maintenance of stability and harmony in the original family. It implies that the head over heels job responsibilities and joblessness foster work-family conflict equally likely.

Following the global financial crisis, an increasing amount of attention has been directed towards examining the Beveridge curve (BC), which indicates the relationship between unemployment and vacancy rates. This research analyzes the unemployment–vacancy rate dynamics in the Turkiye labor market during both the global financial crisis and COVID-19 periods. The findings from this study demonstrate that the labor market exhibits deteriorating efficiency, as evidenced by movement of BC away from the origin. The unemployment and vacancy rates both increase over time, with a leftward (rightward) shift of BC during the global financial crisis (COVID-19) period. The study also reveals that both crises had no significant effect on unemployment–vacancy rate dynamics. In the Turkish labor market, there exists a situation where the vacancy rate is in shortfall of the unemployment level in Turkiye. This creates a positive relationship between these two factors. The labor market in Turkiye experiences inefficiencies as it struggles to generate a sufficient number of jobs to meet the demand from job seekers.

The current study aims to investigate how index returns of conventional and shariah indices of the USA, Europe, and Asia are affected by changes in oil prices, gold prices, VIX, gold-VIX, and oil-VIX. In our investigation, we used the S&P 500, S&P Europe 350, S&P Pan Asia, and their relevant shariah counterparts for the USA, Europe, and Asia. To examine how the explanatory factors affect the overall distribution of the explained variables, we used OLS and quantile regression. For the time frame prior to Covid-19, we discover that all volatility indices—OVX, GVZ, and VIX—influence returns of all indices simultaneously, and that all variables—aside from the spot price of oil—have a greater impact during the bear phase according to QR findings. Further, Volatility indices have a greater impact on volatility of index returns during the Covid-19 period. This is largely because the Covid-19 outbreak had a rapid impact on economies all around the world, and the only thing that affected financial markets consistently was high volatility. This is further supported by the findings of BEKK, which demonstrate that volatility extends across all markets and originates from commodities like gold, oil, gold-VIX, and VIX. Evidence for this can be seen in the fact that during the COVID-19 period, stock prices reacted more favorably to oil price volatility than to oil spot prices, which even went negative on April 20, 2020. Because of this, market stability can be promoted by reducing volatility through the prompt dissemination of crucial information, even while governments have little direct control over the prices of significant commodities like gold and crude oil.

The launch of the open AI chatbot, ChatGPT, in November 2022 has generated widespread excitement around Generative Artificial Intelligence (AI). While researchers have explored ChatGPT's ability to produce content and respond to input, our study takes a different approach and examines its use cases in the financial industry. We aim to understand what ChatGPT offers the financial industry and how it differs from existing banking and financial chatbots. Financial institutions can use ChatGPT for a variety of purposes, including customer engagement, personalization, up-selling and cross-selling, stock forecasting, product development, and financial education. By focusing on the potential of ChatGPT in finance, we hope to spark discussions about its applications in other domains and explore the possibilities of a larger revolution in the future. Finally, this study identifies the challenges associated with the use of Generative Open AI and LLMs-based chatbots in the financial industry and provides recommendations for addressing these challenges.

Determining the right merger strategy for banks is an important step. In this way, risks can be managed more effectively, and long-term financial performance can be achieved. However, there are many different factors that affect this process. It is not optimal for banks to consider all factors due to budget constraints. In this context, it is important to determine the most important ones among these criteria. Accordingly, the purpose of this study is to evaluate alternative merger strategies for banks. For this purpose, 12 different Sustainable Development Goals (SDGs)-based criteria are selected. Multi stepwise weight assessment ratio analysis (M-SWARA) methodology is used to compute the weights of these items. The main contribution of this study is that the implications of the merger process on SDGs can be examined. Furthermore, a new methodology (M-SWARA) is proposed in this study that has an increasing impact on the methodological originality. The findings indicate that increasing profitability has the greatest weight (0.095). Similarly, market share is found as the second most critical factor (0.092) for merger decisions in the banking industry. A profitable bank can attract more investors and with the help of this situation it can be much easier to raise capital and access funding from capital markets. These issues can be used to finance projects that align with SDGs, such as renewable energy, affordable housing, or clean water initiatives. In addition to this situation, profitability can also have a positive impact on innovation and technological advancement. With sufficient resources, a bank can invest in research and development, technological infrastructure, and innovative products and services. Owing to these investments, sustainable development can be promoted.

This article presents a comprehensive overview of the relationship between economic growth, financial development, and carbon emissions by utilizing bibliometric analysis and scientific mapping. The research utilized the Scopus database, covering the period from 1989 to 2023. Through network analysis, the study identified five primary clusters, distinguished by the geographical and technological extent of the topic. The main objective of this review is to provide a comprehensive understanding of the future research avenues in the nexus between economic growth and carbon emissions. The article discusses the authors, journals, regions, and organizations that have had a significant impact on the growth-emission nexus, along with a summary of the most frequently cited works in this field. Furthermore, a meta-analysis was conducted to determine the aggregate impact of each research initiative. Based on the analysis and identified gaps, the study proposes future research paths.

Maintaining a stable exchange rate is a challenging task for the world, especially for developing economies. This study examines the impact of asymmetric exchange rates on trade flows in selected Asian countries and finds that the effects of increased exchange rate volatility on exports and imports differ among Pakistan, Malaysia, Japan, and Korea. The quarterly data from the period 1980 to 2018 is collected from the International Financial Statistics (IFS) database maintained by the International Monetary Fund (IMF). We employ both linear and non-linear Autoregressive Distributed Lag (ARDL) models for estimation. The non-linear models yielded more significant findings, while the linear models did not indicate any significant effects of exchange rate volatility on trade flows. The results of the study suggest that in the case of Pakistan, both the linear and non-linear models indicate that increased exchange rate volatility adversely affects exports and imports, while decreased volatility enhances both. This implies that stabilizing the exchange rate would be beneficial for Pakistan's trade. In contrast, the linear model applied to Malaysia shows no long-run effects of exchange rate volatility on exports. However, the result suggests that decreased volatility stimulates Malaysia's exports. Therefore, in the case of Malaysia, stabilizing the exchange rate could contribute to boosting exports. We also found that increased exchange rate volatility boosts exports of Japan. On the other hand, decreased volatility hurts exports of Japan. As for the long-run effects of exchange rate volatility on imports, we found that increased volatility boosts imports of Korea. The study provides various policy implications regarding the impact of exchange rate volatility on trade flows in developing economies. The study highlights the importance of country-specific considerations in understanding the impact of exchange rate volatility on trade flows, and has important policy implications for promoting trade and economic growth in these nations. It emphasizes the need to model exchange rate volatility separately for developed and developing countries and to continue research and analysis to identify ways to mitigate its negative effects on the economy.

Examining urbanisation and tourism from the perspective of global Sustainable Development Goals is essential for achievinga balance between environmental protection and economic growth in the world's most polluted nations. Moreover, most polluted countries pay more attention to the nature of foreign direct investment (FDI) inflows to achieve a sustainableenvironment. This study intends to explore the impacts of FDI, tourism, urbanization, and economic growth on carbon dioxide emissions using panel data for the top ten most polluted nations for the period from 2000 to 2019. To guide empirical testing,the panel unit root tests LLC and IPS are used. The outcomes of LLC and IPS advise FM-OLS application on how to accomplish the goals. The findings provide proof of how FDI and other factors affect carbon emissions (CE). Particularly, renewable energy consumption (REC) has a detrimental but minor effect on CE. For the panel of developing nations, FDI had a favourable and significant effect on CE along with economic growth, tourism, and urbanization. The expansion of cities isalso harming nature and ecological footprints. These findings are alarming as all factors cause CE under consideration thatleads to the deterioration of the environment. Therefore, more environmental rules should be put into place to reduce CE, draw in clean FDI, and encourage quality-oriented investment in selected nations. Second, it is important to ensure the deployment of green technology and the upgrading of urbanized structures. The government can take several actions against the use of polluting goods and vehicles in urban areas, and any polluting industries should also be outlawed in such residential areas.

This paper explores the relationship between foreign direct investment (FDI) settlement, the contribution of clean energy and CO2 emission in United Arab Emirates (UAE). Using the time-series estimation methods, this study opts cointegration test to find the long-run association between FDI and CO2. Furthermore, energy source of UAE is based on fossil fuel, we employed fixed-effect regression model to determine the effect of FDI after controlling several other factors that causes CO2 emission to find the magnitude. After obtaining the data from 1971-2019, we find that FDI plays a significant role in CO2 emission in the economy. Another key contribution of this paper is regarding clean energy. The results confirm that clean energy helps to reduce the CO2 emissions. The findings of this research endorses the initiative of the UAE launched ‘Energy Strategy 2050’. This implies that UAE is moving in a right direction to curb the CO2 emission by increasing the contribution of clean energy in the total energy mix and would achieve this target through an effective implementation of the ‘Energy Strategy 2050’.

This paper evaluates the performance of eight tests with null hypothesis of cointegration on basis of probabilities of type I and II errors using Monte Carlo simulations. This study uses a variety of 132 different data generations covering three cases of deterministic part and four sample sizes. The three cases of deterministic part considered are: absence of both intercept and linear time trend, presence of only the intercept and presence of both the intercept and linear time trend. It is found that all of tests have either larger or smaller probabilities of type I error and concluded that tests face either problems of over rejection or under rejection, when asymptotic critical values are used. It is also concluded that use of simulated critical values leads to controlled probability of type I error. So, the use of asymptotic critical values may be avoided, and the use of simulated critical values is highly recommended. It is found and concluded that the simple LM test based on KPSS statistic performs better than rest for all specifications of deterministic part and sample sizes.

This paper evaluates the performance of eight tests with null hypothesis of cointegration on basis of probabilities of type I and II errors using Monte Carlo simulations. This study uses a variety of 132 different data generations covering three cases of deterministic part and four sample sizes. The three cases of deterministic part considered are: absence of both intercept and linear time trend, presence of only the intercept and presence of both the intercept and linear time trend. It is found that all of tests have either larger or smaller probabilities of type I error and concluded that tests face either problems of over rejection or under rejection, when asymptotic critical values are used. It is also concluded that use of simulated critical values leads to controlled probability of type I error. So, the use of asymptotic critical values may be avoided, and the use of simulated critical values is highly recommended. It is found and concluded that the simple LM test based on KPSS statistic performs better than rest for all specifications of deterministic part and sample sizes.

This article provides a detailed introduction to China's launching of a digital currency. We conduct a comparative analysis concerning whether digital currency is a more stable and reliable currency than cryptocurrency and investigate whether a digital renminbi (or yuan) could replace the US dollar as a medium of exchange in international transactions. China has gained a first-mover advantage by rolling out a central bank digital currency (CBDC). But the outcome will depend on the US response as well as the future evolution of the US and Chinese economies. Most other articles on this topic focus on domestic use of the Chinese CBDC. But this study is unique in analyzing the prospects of a digital renminbi as a replacement for the US dollar in international commerce.

In this paper, we attempt to explore the extent to which the hard won development gains over the last several years could be reversed due to the unfolding COVID-19 global pandemic, how we can reboot the global response to accelerate the SDGs in times of uncertainties, and most importantly how to turn the recovery into an opportunity to build back better and more resilient economies. To do so, we examine the case of blockchain as one of the emerging innovative work-streams in development practices that could lead the way forward and pave the path for new developmental narratives as we all navigate the uncharted territories of the new digital age. This paper provides useful insights about the underlying dynamics underpinning the adoption of blockchain backed-solutions for sustainable development, and it showcases some of the promising use-cases being developed through trial-and-error experiments by its early adopters. The paper offers a deep dive into a burgeoning development practice in search of disrupting business-as-usual to solve increasingly complex development challenges by mainstreaming innovations such as blockchain-enabled solutions to rethink the ways in which development solutions are being delivered across the SDG spectrum. This work points to the significant potential of blockchain technology as a game changer in solving some of the most pressing issues hindering the global recovery post Covid-19 to transition towards greener and more inclusive economies. Nevertheless, we also stress that the hype-cycle behind the “let’s blockchain it” trend does not mean that blockchain-backed solutions are necessarily superior to other alternatives which might be less costly and less technical in nature. Development practitioners prototyping and implementing blockchain-based solutions for sustainable development can utilize these insights and discussions to make informed decisions in their journey to harness the disruptive potential of blockchain alone or in tandem with other emerging technologies in the new world of business as unusual.

The main aim of this article is to examine the inter-relationships among the top cryptocurrencies on the crypto stock market in the presence and absence of the COVID-19 pandemic. The nine chosen cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Eos, BitcoinCash, Binance, Stellar, and Tron and their daily closing price data are captured from coinmarketcap over the period from 13 September 2017 to 21 September 2020. All of the cryptocurrencies are integrated of order 1 i.e., I(1). There is strong evidence of a long-run relationship between Bitcoin and altcoins irrespective of whether it is pre-pandemic or pandemic period. It has also been found that these cryptocurrencies’ prices and their inter-relationship are resilient to the pandemic. It is recommended that when the investors create investment plans and strategies they may highly consider Bitcoin and altcoins jointly as they give sustainability and resilience in the long run against the geopolitical risks and even in the tough time of the COVID-19 pandemic.

This paper examines the efficiency and its relation to profitability in Turkish banking sector by employing Panel Stochastic Frontier Approach. In the post crises period, extensive structural changes have taken place and a great number of new developments have occurred, affecting the efficiency of banking sector. This is the first study that employs panel stochastic frontier approach for banking efficiency in Turkey. In this research, both cost and profit efficiency measures are estimated for the panel data consisting of 32 banks between 2002–2007. Results suggest that there is cost efficiency gain and convergence in the efficiency levels of banks. As another interesting result, foreign banks are less efficient and state banks are more efficient. This paper also analyzes the relation between efficiency and profitability and finds no robust relation between them. However, the bank size matters more for profitability. Santrauka Autoriai nagrinėja Turkijos bankų veiklą, t. y. jų pelningumą bei efektyvumą pokriziniu laikotarpiu. Šis laikotarpis buvo pasirinktas todėl, kad atsirado daug įvairių struktūrinių pokyčių, kurie turėjo įtakos bankininkystės sektoriaus efektyvumui. Tyrimui buvo pasirinkti 32 Turkijoje veikiantys bankai (jų veiklos rodikliai prieš ekonominę krizę ir po jos). Rezultatai rodo, kad Turkijoje veikiančių užsienio komercinių bankų veikla yra mažiau efektyvesnė nei valstybinių. Taip pat autoriai analizuoja bankų veiklos efektyvumo ir pelningumo santykį, tačiau, kaip rodo gauti rezultatai, stipraus ryšio tarp jų nėra. Reikšminiai žodžiai: efektyvumas, pelningumas, komerciniai bankai, nacionaliniai bankai, Turkija