Factors influencing green bond yield: Evidence from Asia and Latin American countries

Despite numerous studies in the domain of green bonds, a paucity of literature concentrates on emerging countries’ green bonds. To fill this void, this study aims to examine the factors influencing green bond yield in the Asian and Latin American contexts. The data are compiled from the Bloomberg and Fred databases between 2017 and 2022. The panel regression with the Generalized Least Square method was employed. The results reveal that Asian green bonds provide higher yields with less risk to their investors than Latin American green bonds. The regression results of Asian green bonds show negligible effects of all factors, except coupon rate with a positive effect (β = 0.844), indicating its remarkable influence on green bond yield. However, the findings of Latin American green bonds uncover that coupon (β = 0.780), maturity (β = 0.025), and bond rate (β = 2.472) surpass the green bonds yield due to their positive effects, whereas issue size (β = –1.215) causes a reduction in the green bonds yield with their negative effect. Further, Environmental, Social, and Governance disclosure shows a positive (β = 1.611) effect, indicating better yield for investors due to their potential power to vanish greenwashing in these markets. Moreover, interest rate and GDP exert significant positive (β = 0.141) and negative (β = –0.030) effects on green bond yield, respectively. This observation implies that higher lending rates increase bond yield, whereas GDP-led growth provides lower yield due to better economic prospects and high investor demand for the bonds. AcknowledgmentThe authors are grateful to Manipal Academy of Higher Education (MAHE), Manipal, for providing financial assistance in the form of a “JRF Contingency Grant” for this research article

Impact of personality traits on investment decision-making: Mediating role of investor sentiment in India

The behavior of investors and their investment decision-making process in the financial markets are guided by psychological (sentiments) and personal characteristics (personality traits). Research in recent years has shown the connection between investor sentiment and personality traits and investment decisions. Though academic works in the field of behavioral finance are growing, studies on personality traits and investment decision-making with investor sentiment as a mediator are sparse. To this end, the paper aims to analyze the effects of Indian retail investors’ Big-five personality traits (Neuroticism, Extraversion, Openness to experience, Agreeableness, and Conscientiousness) on their short-term and long-term investment decision-making with the mediating effect of investor sentiment. The study employs the Partial Least Square-Structural Equation Model to test the framed hypotheses. The findings of the study reveal that Neuroticism has a significant positive effect (β=0.352, p<0.05) on investor sentiment. It further shows that Extraversion has a significant positive effect (β=0.186, p<0.05) on long-term decision-making. On the contrary, the consciousness trait has a significant negative effect (β=-0.335, p<0.05) on short-term investment decision-making. Furthermore, the Openness trait demonstrates a significant effect on both short-term and long-term investment decision-making (β=0.357, p<0.05; β=0.007, p<0.05). However, the findings reveal no significant intervening effect of investor sentiment between personality traits and investment decision-making. Thus, the study strongly exerted the impact of investors’ personality traits on their investment decision-making due to the high influence of personal characteristics over sentiment effects

Do bond attributes affect green bond yield? Evidence from Indian green bonds

Over the years, green finance tools have gained considerable attention with the increased concern to achieve sustainability in the economy. Green bonds are one such new innovative green finance tool embodied with bonds and green attributes. However, research on the Indian green bond is relatively modest. Thus, this study aims to analyze the impact of bond attributes on green bond yield. The study retrieves green bond data from the Bloomberg and Climate Bonds Initiative databases from 2015 to 2022. To test the framed hypotheses, the study employs a panel regression technique with a random effect model. The findings of the study show a significant positive effect of bond ratings (β = 2.80926, p < 0.05) on green bond yield based on the argument that good-rated bonds serve as collateral in the security market. On the contrary, the result also reveals a significant negative effect of bond maturity (β = –0.327296, p < 0.05) and bond label (β = –3.16480, p < 0.05) on green bond yield. The results based on the observation suggest that when the certified bond is issued, this signals the greenness of the bond in the market and attracts high demand, whereas the long maturity ensures the green project construction for a longer period, resulting in a lower bond value. Thus, empirical findings reveal that bond attributes are the major factors in influencing bond yield. The obtained results serve as a prerequisite for potential issuers, investors, and policymakers to further popularize the green bond in the country

Is the Nexus Between Capital Structure and Firm Performance Asymmetric? An Emerging Market Perspective

The nature of the relationship between leverage and firm performance has been a subject of investigation in extant literature. We re-examine the nature of the association by using a sample of 78 non-financial firms listed in the Nifty 100 index during the 2013-2023 period by applying the quantile regression technique and comparing the result with the linear regression approach (system GMM technique). Our empirical analysis demonstrates that leverage negatively impacts the performance of firms. Further, results show that the association is non-homogeneous among firms of different quantiles: leverage withers the performance of highly profitable firms (upper quantile) than low profitable firms (lower quantile). The identified concave relationship highlights the prominence of optimal capital structure and the role of finance managers in designing a sound financial policy that matches firm characteristics and borrowing requirements. The findings of our study draw insightful implications for managers and policymakers while contributing to the ongoing leverage and firm performance debate reported in previous studies

Does the Ind AS moderate the relationship between capital structure and firm performance?

In line with the wide implementation of IFRS around the globe, the significant shift in the Indian accounting system appertained to the Ind AS is expected to have a substantial impact on the firm-level information environment. Nevertheless, the question of whether the adoption of such standards moderates the relationship between leverage and firm performance remains unanswered. In this backdrop, we aim to close this research gap employing 3120 firm-year observations from 401 Indian non-financial firms for a period from 2013 to 2022. Notably, we found that the leverage among Indian firms discourages profitability. Further, the adoption of Ind AS negatively moderates the leverage and firm performance association. The findings suggest that the enhanced transparency and the firm's reporting quality dissuade risk-averse investors from investing in highly levered companies. As a result, investors avoid risky investments, and firms must strive to foster their trust and motivation. The conclusion of the present research draws significant implications for management and policymakers while also contributing to the ongoing debate on capital structure and firm performance

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Bibliometric portrait of the theory of community-based enterprise: evolution and future directions

AbstractThe focus of this study is to present an overview of the literature related to the Theory of Community-Based Enterprise (TCBE) using bibliometric analysis. We analysed 477 articles published in 201 journals in the Scopus database till 2021. Initially, prominent features based on key bibliometric indicators of 477 articles are presented under performance analysis, like prominent studies, journals, authors, and keywords. Then in network analysis, bibliographic coupling, and co-occurrence analysis were conducted on the articles. The bibliometric analysis enables mapping of the theory’s evolution, provides a comprehensive overview of TCBE literature, and leads to identifying the dynamics of the field and cluster-based themes and their relationships. The study resulted in three significant findings—the first is that limited studies have assessed the assumptions and features of the theory as valid in different cultural scenarios. Second, the theory is used in various concepts but prominently as a sub-topic under social entrepreneurship; there exists an opportunity to study community initiatives as a focus. The third is a spectrum of words used to represent the idea of community-based enterprises that hampers building uniformity of the concept

Overview of Corporate Governance Research in India: A Bibliometric Analysis

AbstractThough the Corporate Governance concept has gained paramount interest due to the spate of corporate scandals, there is a void in the literature in terms of a summary overview of corporate governance in the Indian context. The present study aims to provide a state-of-the-art summary of corporate governance in India. To do so, the study employs a bibliometric analysis with a systematic literature review approach with extensive use of Bibliometric R Packages and VOSViewer software. To this end, the study reviews a total of 344 articles published in the Scopus database between 2004 and 2022. Akin to this, the review performs performance analysis, science mapping, and network analysis. The findings show an increasing trend in publications since 2004 till date with an annual growth rate of 23.99%. The network analysis results delineate earnings management, gender diversity, ownership structure, board structure, board size, corporate governance, ownership, and firm performance as major research themes in this field. This study is the primary attempt to show the growth and evolution of CG research in India. Thus, the review contributes to the existing literature on CG at the country level and provides scope for further research. Also, the study findings help policymakers, academicians, and regulators to strengthen corporate governance practices in the country

Land Use/Land Cover Segmentation of Satellite Imagery to Estimate the Utilization of Earth’s Surface

Land Use/Land Cover (LULC) mapping plays a major role in land management applications such as to generate community map, proper urban planning, and disaster risk management. Proposed algorithm efficiently segments different land use/land cover classes such as buildings, trees, bare land, and water body. RMS value based multi-thresholding technique is used to segment various land use/land cover classes and consequently using the binning technique to accurately estimate the utilization of earth’s surface. The proposed algorithm is tested on two different data sets of Bengaluru city, India. The percentage utilization of surface objects for grid 7 image of dataset I is found to be 96.68% building, 1.05% vegetation, and 0.22% barren land, the area covered in grid 7 of dataset I is identified as overutilized land. Percentage utilization of surface objects for grid 8 of dataset II is found to be 68.95% building

A Texture based Image Retrieval for Different Stages of Alzheimer’s Disease

In the last few years, using digital images have become significant across most of the sectors including healthcare and medical labs. To analyze and interpret the large collection of images having a complex disease pattern requires the knowledge of medical experts. So, the image retrieval technique plays an important role to assist the doctors to carefully examine an image of a new patient by comparing with most similar images existing in the database and also to take a correct decision during diagnosis. So, we carried out our studies by collecting the images of Magnetic Resonance Imaging (MRI) from the Open Access Series of Imaging Studies (OASIS) database. Later, we have categorized the collected MRI images into three different groups based on the size of a ventricular region of the brain and then employed second and higher order statistical methods to extract the textural features from each image. Thus