Search

Financial institutions should prioritize the adoption of comprehensive Environmental, Social, and Corporate Governance (ESG) disclosure policies to improve their market reputation and decrease capital expenditures. The current study’s research objective is to investigate the impact of both inside and outside executives on the successive adoption of ESG strategies, based on the sustainable leadership theoretical framework and the bottom-up corporate governance theory. Data for the current study were obtained from the Refinitiv Eikon database and analyzed through using the entropy weight and TOPSIS techniques. The research suggests that including fully autonomous board members has the potential to improve the transparency of firms’ ESG criteria. This result was derived from an analysis of data pertaining to the behavior of CEOs and non-executives at the company level in Fiscal Year (FY) 2023. The verification of the soundness and dependability of this finding has been carried out by scrutinizing the problem of endogeneity and diverse techniques of data representation. Furthermore, our study has disproven the idea that having CEOs on the board of directors may significantly improve the ESG performance of financial institutions. Consequently, the research proposes that adopting a strict policy of board independence has the capacity to alleviate the environmental, social, and governance repercussions that arise from the control of internal executives, namely CEOs.

Background/Objectives: Failure to thrive (FTT) is mostly caused by insufficient consumption of nutrient-rich food, recurrent infections like diarrhea and intestinal worms, substandard caregiving practices, and limited availability of health and other vital services. Furthermore, there was a correlation between the educational level of mothers and the occurrence of FTT in children aged 6–12 months. Thus, the objective of the current research is twofold: (i) to investigate other factors related to FTT and (ii) to evaluate the impact of them on FTT in Sub-Saharan African countries and their urban areas. Methods: We used weight entropy and TOPSIS methods to approach the research question. In particular, the entropy-weight method is effective for precisely evaluating the relative significance of the selected criteria for TOPSIS computation. Thus, data were retrieved from the database of UNICEF for the year 2019 for nine Sub-Saharan countries, and based on the methods used, five criteria have been selected for consideration. Those of mothers in higher education were identified as having a higher weight, which means that this can affect positively the ability of mothers to mitigate the situation of FTT and protect their children. Results: The findings of the study highlight the factors of maternal education at a higher level and unhealthy habits as those with the greatest weight and impact on the FTT. Moreover, the results indicate that the association between maternal education, and especially higher education, and FTT is stronger in Ethiopia. Despite the limited amount of research on the specified relationship in Sub-Saharan countries, this study is among the initial ones to examine it. Conclusions: The current study can aid policymakers in devising appropriate policies and implementing effective measures to tackle FTT in Sub-Saharan Africa, like enhancing the number of mothers in these countries to be integrated into the educational system to help both themselves and their children mitigate or avoid the symptoms of FTT.

Family businesses have distinct characteristics that differentiate them from other firms. Researchers must meticulously analyze issues, with a specific focus on the interplay of family business dynamics, considering this factor. The main objective of this research was to provide insight into the adverse effects of family companies, particularly how the power dynamics inside these organizations might enable corruption or fraud and how corporate governance can help in mitigating these phenomena. Specifically, family businesses can be investigated by considering unique characteristics such as ownership and control, generational dynamics, and corporate governance. To address the study topic, a bibliometric analysis was conducted using the R statistical programming language and the bibliometric tools Biblioshiny and VOSviewer. Data were obtained from the Scopus database and examined in documents. The lack of unbiased external evaluation, the ineffectiveness of internal audits, disputes between different generations, the dominance of family members, and the narrow extent of governance all contribute to the exacerbation of tensions that promote corruption inside family firms. Moreover, the findings indicate that CEO duality correlates with the occurrence of corruption and fraudulent activities, such as manipulating profits. Furthermore, the findings suggest a correlation between the qualities of the board and instances of corruption and bribery inside family firms. These factors also increase the probability of financial statement fraud.

This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility patterns of Bitcoin. R/S analysis, SMA, and RSI calculations assess time series data obtained from our volatility estimators. Although Bitcoin is known for its high volatility and price instability, our analysis using R/S analysis and moving averages suggests the existence of underlying patterns. The estimated Hurst exponents for our volatility estimators indicate a level of persistence in these patterns, with some estimators displaying more persistence than others. This persistence underscores the potential of momentum-based trading strategies, reinforcing the expectation of additional price rises after declines and vice versa. However, significant volatility often interrupts this upward movement. The SMA analysis also demonstrates Bitcoin’s susceptibility to external market forces. These observations indicate that traders and investors should modify their risk management approaches in accordance with market circumstances, perhaps integrating a combination of momentum-based and mean-reversion tactics to reduce the risks linked to Bitcoin’s volatility. Furthermore, the existence of robust patterns, as demonstrated by our investigation, presents promising opportunities for investing in Bitcoin.

Over the past decade, microbiome research has significantly expanded in both scope and volume, leading to the development of new models and treatments targeting the gut–brain axis to mitigate the effects of various disorders. Related research suggests that interventions during the critical period from birth to three years old may yield the greatest benefits. Investigating the substantial link between the gut and brain during this crucial developmental phase raises fundamental issues about the role of microorganisms in human health and brain development. This underscores the importance of focusing on the prevention rather than the treatment of neurodevelopmental and neuropsychiatric disorders. The present review examines the gut microbiota from birth to age 3, with a particular focus on its potential relationship with neurodevelopment. This review emphasizes the immunological mechanisms underlying this relationship. Additionally, the study investigates the impact of the microbiome on cognitive development and neurobehavioral issues such as anxiety and autism. Importantly, it highlights the need to integrate mechanistic studies of animal models with epidemiological research across diverse cultures to better understand the role of a healthy microbiome in early life and the implications of dysbiosis. Furthermore, this review summarizes factors contributing to the transmission of gut microbiome-targeted therapies and their effects on neurodevelopment. Recent studies on environmental toxins known to impact neurodevelopment are also reviewed, exploring whether the microbiota may mitigate or modulate these effects.

This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimator’s dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling.