PurposeThis study attempts to assess the role that institutional quality (IQ) plays in influencing inflows and outflows of Foreign Direct Investment (FDI) for BRICS nations as burgeoning FDI is flowing into and out of these countries. Moreover, this paper explores the impact of individual governance indicators separately on the FDI flows.Design/methodology/approachThe study analyses this nexus for these emerging economies for the period 1996–2019 using autoregressive distributed lag technique.FindingsThe study indicates a significant and positive coefficient for IQ in India and South Africa, suggesting that improving IQ would enhance the IFDI. However, for outward FDI (OFDI)–IQ linkage, the results show a negatively significant impact of IQ on OFDI for Brazil and Russia. Additionally, the authors observe control of corruption as a significant institutional component for attracting inward FDI for Brazil, India and South Africa, whereas it is an insignificant factor for Russia and China. Further, the authors notably find that upgrading the governance indicators will decrease the level of OFDI for Brazil, Russia, China and South Africa. On the contrary, findings suggest that improving the IQ will foster the OFDI for India.Originality/valueThis study uses time-series analysis instead of cross-country analysis (used extensively in literature), avoiding heterogeneity. Further, this study explores the IFDI–IQ link for BRICS nations, which are captivating a significant chunk of IFDI, and still not given much attention in the extant literature. Moreover, the authors identify the impact of IQ on the OFDI, neglected by the existing studies.

Does institutional quality matter for foreign direct investment flows? Empirical evidence from BRICS economies

Abstract This study explores the complex relationships involving ecological footprints, energy use, carbon emissions, governance efficiency, economic prosperity, and financial stability in South Asian nations spanning the period from 2000 to 2022. Employing various methodologies such as cross-sectional dependence tests, co-integration analysis, and first- and second-generation unit-root tests, we use a panel Autoregressive Distributed Lag model, feasible generalized least squares, and Panel Corrected Standard Errors to ensure the robustness of our findings. We find noteworthy positive correlations between several variables, including heightened ecological consciousness, effective governance structures, increased GDP per capita, and amplified CO 2 emissions. These relationships suggest potential pathways to strengthen the financial stability of the entire region; they also highlight the latent potential of embracing ecologically sustainable practices to fortify economic resilience. Our results also underscore the pivotal role of appropriate governance structures and higher income levels in bolstering financial stability in South Asian countries. Interestingly, we also find negative coefficients associated with the use of renewable energy, suggesting that escalating the adoption of renewable energy could create financial instability. This finding stresses the importance of diversification in energy strategies, cautioning policymakers to carefully consider the financial ramifications of potentially costly imports of renewable energy sources while seeking to reduce carbon emissions, emphasizing the need to strike a balance between ambitious sustainability goals and the pursuit of sustained economic robustness in the region. In considering the implications of these findings, it is crucial to consider each country’s broader socioeconomic context. Our results offer valuable insights for policymakers in developing renewable energy strategies. 

The implications of the ecological footprint and renewable energy usage on the financial stability of South Asian countries

The impacts of forest resources, green investment, healthcare, and education on environmental pollution: - China Carbon neutrality program

Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accuracy. The first step is to filter the original series of stock market prices into two new series, consisting of a nonlinear trend series in the long run and a stochastic component of a series, using the Hodrick-Prescott filter. Next, all possible filtered combination models are considered to get the forecasts of each filtered series with linear and nonlinear time series forecasting models. Then, the forecast results of each filtered series are combined to extract the final forecasts. The proposed filtering-combination technique is applied to Pakistan's daily stock market price index data from January 2, 2013 to February 17, 2023. To assess the proposed forecasting methodology's performance in terms of model consistency, efficiency and accuracy, we analyze models in different data set ratios and calculate four mean errors, correlation coefficients and directional mean accuracy. Last, the authors recommend testing the proposed filtering-combination approach for additional complicated financial time series data in the future to achieve highly accurate, efficient and consistent forecasts.

Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange

The field of sustainability accounting aims to integrate environmental, social, and governance factors into financial reporting. With the growing importance of sustainability practices, emerging technologies have the potential to revolutionize reporting methods. However, there is a lack of research on the factors influencing the adoption of blockchain and cloud-based sustainability accounting in China. This study employs a mixed-methods approach to examine the key drivers and barriers to technology adoption for sustainability reporting among Chinese businesses. Through a systematic literature review, gaps in knowledge were identified. Primary data was collected through an online survey of firms, followed by in-depth case studies. The findings of the study reveal a positive relationship between company size and reporting behaviors. However, size alone is not sufficient to predict outcomes accurately. The industry type also has significant but small effects, although its impact on reporting behaviors varies. The relationship between profitability and reporting behaviors is intricate and contingent, requiring contextual examination. The adoption of blockchain technology is positively associated with capabilities, resources, skills, and regulatory factors. On the other hand, cloud computing adoption is linked to resources, management support, and risk exposures. However, the specific impacts of industry on adoption remain inconclusive. This study aims to offer empirical validation of relationships, shedding light on the intricate nature of interactions that necessitate nuanced conceptualizations incorporating contextual moderators. The findings underscore the importance of providing customized support and adaptable guidance to accommodate the evolving practices in sustainability accounting. Moreover, the assimilation of technology and organizational changes highlights the need for multifaceted stakeholder cooperation to drive responsible innovation and address the challenges posed by digital transformations in this field.

Drivers and influencers of blockchain and cloud-based business sustainability accounting in China: Enhancing practices and promoting adoption

Explain systemic risk of commodity futures market by dynamic network

A study on the moderating role of national absorptive capacity between institutional quality and FDI inflow: evidence from developing countries

Does energy conversion contribute to economic development in emerging and growth leading economies (EAGLE’s): evidence from panel ARDL approach

Abstract In light of the increasing investor interest in cryptocurrencies (CR) as alternative financial assets in financial markets, we sought to examine the connection between economic policy uncertainty (EPU) and cryptocurrencies. To do so, monthly data for Bitcoin (BTC), Ethereum (ETH), and Tether (THT) from January 2021 to April 2023 were employed. We utilized quantile regression and Granger causality analysis to investigate the relationship between EPU and cryptocurrencies. The initial results of this study suggest that EPU has little effect on the cryptocurrency market in the short-term. To enhance the strength and validity of these findings, we performed separate evaluations tailored to the unique contexts of the United States and China. The results revealed that the effects of EPU were adverse and statistically insignificant for China, while the situation differed slightly for the United States. Given that the United States has the most developed economy, its policies have a significant influence globally. As a result, cryptocurrencies have the potential to serve as efficient hedging tools. Furthermore, we incorporated nonlinear autoregressive distributed lag (NARDL) analysis to assess the asymmetric impact of EPU on cryptocurrencies by adopting both short-term and long-term perspectives. The outcomes demonstrated that both Bitcoin and Ethereum can serve as hedging tools in the short-term, although this utility diminishes in the long-term. Conversely, Tether displayed a positive association with EPU in the long-term. The findings of this study hold significance for policy-makers, offering valuable insights related to structuring efficient policies. The recommendations include fostering a rational framework for active participation from various stakeholders, including investors, governmental bodies, central banks, stock exchanges, and financial institutions. This collaborative effort aims to mitigate irrational fluctuations and enhance the acceptability of cryptocurrencies. In essence, this research underscores the potential of cryptocurrencies as a secure hedge against short-term EPU. However, we caution against assuming that any single cryptocurrency can consistently serve as a dependable investment haven. 

Is cryptocurrency a hedging tool during economic policy uncertainty? An empirical investigation

The primary use of futures is hedging risk. Traders in the spot market can hedge certain risks through the futures market. With the development of the futures market, the arbitrage transactions around futures have attracted increasingly attention. The aim of this paper is to establish an innovative and unique pairs trading framework, and use it to test the effectiveness of China's futures market. The framework for pairs trading is based on cointegration test, Kalman filter and Hurst index filtering. We use the data of 47 commodities with relatively good liquidity in the Chinese commodity futures market. We apply the representative index of China's commodity futures market, "Wenhua Commodity Index" as the benchmark, to evaluate the performance of the strategy and compare it with the benchmark model. This study found that, according to the pairs trading framework, after considering transaction costs, the cumulative return in the sample reached 81%, the cumulative return out of the sample was 21%. It is worth noting that the out-of-sample maximum drawdown achieved excellent results of no more than 1%. In the same period, trading the Wenhua Commodity Index with a "buy and hold" strategy achieved a gain of 31%, but with maximum drawdown reached 15%. The values of our paper are, first it proves that Chinese commodity futures market is not a weak-form efficient market, because technical analysis based on machine learning could obtain excess returns. Second, this research combines the theory and practice of statistical arbitrage, which also provides guiding significance for investment practice. 

An innovative high-frequency statistical arbitrage in Chinese futures market

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