This paper explores how digital technology reduces trade costs using bilateral trade data from RCEP member countries and a panel fixed effects model. The findings show that digital technology significantly lowers trade costs, a conclusion that holds even after accounting for lag effects and various robustness checks. The impact of digital technology on trade costs follows an inverted U-shape: the effect is most significant in the current period, especially with a one-period lag, and diminishes over time. Larger economies and higher export levels strengthen this negative impact due to their reliance on exports and continuous improvements in domestic digital technology. The study recommends investing in digital infrastructure, formulating reasonable internet access policies, supporting digital skills development, and enhancing digital connectivity to bridge the digital divide, thereby promoting trade facilitation and efficiency.

Based on the empirical data of 6110 listed export companies in China from 2010 to 2019, this paper studies the impact of China's economic policy uncertainty (EPU) index on the profitability of listed companies from the specific perspective of export companies, and analyzes the moderating effects of different factors on this impact. The results show that China's EPU index has a significant negative impact on the profitability of listed export companies, and the increase in R&D investment and wage returns will aggravate this negative impact. In addition, listed export enterprises with different enterprise characteristics have different responses to EPU. State-owned enterprises and enterprises located in coastal areas are relatively less negatively affected by EPU.

This study compares return and volatility performance of exchange-traded funds (ETFs) with high-ESG (Environment, Social, and Governance) rating vs. low-ESG rating. The paper also examines time-series data predictability by identifying their positive dependence and volatility asymmetry properties, and examines the performance of two combinations of short-memory models i.e., autoregressive moving average and exponential generalized autoregressive conditional heteroskedasticity (ARMA-EGARCH); autoregressive moving average and asymmetric power autoregressive conditional heteroskedasticity (ARMA-APARCH) and two long-memory models, autoregressive moving average and fractionally integrated exponential generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH); and autoregressive fractionally-integrated moving average and asymmetric power autoregressive conditional heteroskedasticity (ARFIMA-APARCH). The study found that low-ESG rating ETFs on average have slightly significant higher returns and also lower volatility compared to their high-ESG rating counterparts. Evidence of asymmetric volatility properties are also present on both high-ESG and low-ESG rating ETFs returns. The study also observed that for both high-ESG and low-ESG rating ETFs denote a stationarity, but non-invertible process in their returns. Results can provide fresh understanding in the topic of leverage effects and volatility that can open future research channels to academicians.

Liquidity and its associated issues are one of dominant strands in the market microstructure. In this study, microblogging-based behavioral perspective on economic unrest is linked to the market liquidity. The concept of liquidity is examined in terms of price dispersion relative to the quantity traded. The analysis contains the quantification of multiple linear regression, Gaussian distribution technique, and vector error correction methodology. In the economic stability period, the investor’s mood, either in positive manner or pessimistic context, had an influential role on the price impact volume-based liquidity. Meantime, the probability was higher for occurrence of price impact volume-based liquidity in response to the sentiment indicators. In the economic unrest environments, the positive bias investor’s mood was not vigorous enough to influence the dispersion of asset’s prices and trading quantity. Most importantly, the negative bias investor’s emotion was linked to increase the dispersion of asset’s prices relative to the quantity traded. Investors with a lower amount of trading quantity had declined the liquidity in the market. Additionally, there was a higher probability for occurrence of illiquidity in the pessimistic market periods. However, changes in past sentiment series were not associated with changes in pervious liquidity series, either in short run or long run. The findings may be potentially applicable to manage the behavioral perspective of liquidity risk.

This study investigates the impact of COVID-19 on bank borrowers across 22 Arab economies from 2017 to 2022, employing the two-step system GMM estimator. The findings reveal a significant negative effect of COVID-19 on the number of bank borrowers, highlighting a sharp decline in borrowing activity due to the pandemic’s economic disruptions. Notably, the decline was more pronounced in non-GCC countries, where economic structures and pandemic responses differed. Robustness tests, including subsample analysis and alternative econometric models (pooled OLS, fixed effects, random effects, and 2SLS), consistently confirm these results. The study emphasizes the need for targeted policy interventions to stabilize financial systems, reduce borrowing costs, and control exchange rate fluctuations to maintain credit access during crises. The findings provide crucial insights for policymakers aiming to support economic recovery and strengthen the resilience of the banking sector in the face of future external shocks.

The performance of the equity Australian Exchange Traded Funds (ETFs) over the five-year period 2019-2023 is examined in this study. The empirical results show that, on average, the return of the examined ETFs has been positive over the study period. In addition, the ETFs has beaten, in raw return terms, the S&P/ASX 200 Index, which is a proxy for the entire stock market in Australia, in three out of five years during the study period. However, these ETFs have not achieved any statistically significant alpha against the market benchmark. Moreover, the results of a cross-sectional regression model show that performance is negatively related to the age of ETFs but positively to their size. Finally, when it comes to persistence, the empirical findings indicate that in most of the cases performance reverts from one year to another.

This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.

This paper examines the impact of COVID-19 on Small and medium-sized enterprises (SMEs) deposits across 13 African nations from 2016 to 2022, using a seven-year dataset and Ordinary Least Squares estimator. Notably, it uncovers a significant positive link between the pandemic and SMEs deposits, indicating heightened reserves amidst economic uncertainty, potentially driven by risk mitigation or government support. Associations between gross domestic product (GDP), inflation, unemployment, foreign direct investment (FDI), exchange rates, and SMEs deposits were evident, showcasing higher GDP aligning with increased deposits, while inflation and unemployment linked to reduced ones. FDI showed a positive influence, and exchange rate fluctuations notably affected SMEs deposits, especially for those in international trade. These findings emphasize policy reconsideration for crisis strategies supporting SMEs reserves during uncertainty and interventions addressing inflation, unemployment, and exchange rate risks. Future research exploring regional nuances and global comparisons could further enrich policymaking for resilient SMEs amid evolving economic landscapes.

The study investigates the impact of COVID-19 on African exchange rates, employing a panel dataset over the period 2016-2022 from 13 African economies and employing pooled OLS estimator. The findings reveal a noteworthy positive correlation between COVID-19 levels and exchange rates within the region. However, this association necessitates deeper exploration due to its multifaceted implications, including economic disruptions, government interventions, global market interconnections, regional stability perceptions, and investor behavior. Economic indicators, such as GDP, inflation rate, real interest rate, and unemployment rate, significantly influence exchange rates. While GDP positively impacts exchange rates through economic growth, investor confidence, and trade strength, controlled inflation fosters investor confidence and export competitiveness, positively affecting exchange rates. Moreover, a pronounced positive impact of the real interest rate indicates economic stability, attracting investment and contributing to currency appreciation. Conversely, high unemployment negatively affects exchange rates, signaling economic weaknesses and eroding investor confidence. These findings emphasize nuanced policy responses for managing health crisis-induced economic disruptions and suggest future research directions focusing on specific country-level analyses and multifaceted factors influencing currency valuations within African economies.

The rise of privately issued digital currencies, which primarily serve as alternative investment assets poses a challenge to the traditional financial instruments traded in the financial market. This study examines the dynamic relationship between the major privately issued digital currency (Bitcoin) and two financial market securities in Nigeria. The paper employed Vector Autoregressive (VAR) model and presents three relevant findings. First, the impulse response function indicates the absence of a significant response of the Nigerian financial market to shocks emanating from the Bitcoin market, implying lower connectedness between the two markets. Secondly, the outcome of the variance decomposition reveals a lower contribution of Bitcoin to changes in stock prices and treasury bills, however, stock prices and treasury bills contributed higher impact to each other compared to the contribution of Bitcoin. Thirdly, a weak bi-directional causality between the Bitcoin and treasury bills was observed and a uni-directional causality running from treasury bills and stocks, implying the existence of portfolio rebalancing from the fixed income to the equities market. Despite the weak connection between digital currency and the financial market, the paper recommends that the Central Bank of Nigeria and the Securities and Exchange Commission should maintain monitoring the development of crypto exchanges and continue reviewing the existing policy restricting cryptocurrency transactions through banks to avoid its unsavoury effects.