Global Stock Research Articles

Combining Sentinel-2 and diverse environmental data largely improved aboveground biomass estimation in China's boreal forests.

Accurately mapping aboveground biomass (AGB) in China's boreal forests is crucial for assessing global carbon stock and formulating forest management strategies but remains challenging as the environmental heterogeneity complicates AGB estimation. Here, we investigated the relative gains of integrating Sentinel-2 and environmental data, as well as synthetic aperture radar (SAR) images to map AGB in China's boreal forests. We used two machine learning algorithms, random forest and gradient boosting regression (GBR), and four dataset combinations to develop the AGB models, then evaluated the AGB map by carrying on uncertainty analysis and comparing it with existing AGB products. Results showed that the GBR model based on Sentinel-2 and environmental data presented the best AGB estimation capability (R2: 0.75, RMSE: 23.60 Mg/ha), while further adding SAR images had negative effects on the model improvement. The Tasseled Cap Distance, short-wave infrared from Sentinel-2, Black dragon fire disturbance, Elevation, and Geographic locations were found to be significant contributors to AGB prediction. Our AGB estimates exhibited moderate to low uncertainty and outperformed other existing AGB maps in China's boreal forests based on independent validation assessment. The AGB distribution presented a noticeable south-north gradient difference, ranging from 3.23 to 346.37 Mg/ha. This study provides new insight into AGB estimation through the integration of Sentinel-2 imagery and multiple environmental data and offers a basis for sustainable management in China's boreal forests.

An analysis of the effects of aging society on global stock markets

PurposeThe primary purpose of this study is to explore the effects of demographic transition toward aging populations on the performance of stock market indices across various economic developments. The research aims to provide valuable insights into the life-cycle hypothesis on savings patterns, investment behavior and the potential reverberations on global financial markets.Design/methodology/approachThe study adopts a comprehensive global perspective, scrutinizing the effects of aging populations on stock market indices across developed, developing and transitional economies through the panel data analysis. Using annual data spanning the period from 1991 to 2020, encompassing a sample of 10 countries from each economic development level, the study employs the panel autoregressive distributed lag (ARDL) model with fixed effect estimation.FindingsThe findings unveil a statistically significant positive impact of the elderly population proportion on global stock market indices. However, the magnitude and contours of this impact exhibit considerable heterogeneity across different country groups. Specifically, the study finds that while the aging population significantly influences stock market performance in developed nations, its effect is overshadowed by other economic factors, such as consumer price indices and interest rates, in developing countries and economies in transition.Originality/valueThe originality and value of this study lie in its comprehensive global perspective, which encompasses a diverse array of economies at varying developmental stages. The research contributes to an understanding of the effects of demographic transitions on stock market performance on a global scale. The insights derived from this study hold significant implications for policymakers, financial institutions and investors seeking to navigate the challenges and opportunities posed by aging societies in an increasingly interconnected global economy. Additionally, the findings highlight the need for specific strategies and policies that account for the unique economic characteristics and developmental stages of different nations.

Transforming Personal Finance Coaching through Artificial Intelligence

Artificial intelligence (AI) has bloomed in recent years and is gradually becoming an irreplaceable asset in finance,among other sectors. Personal finance is a subset of finance, which too is being revolutionized due to changing timesand technological advancements, much like AI. Security and proper financial guidance have never been moreimportant with such significant change. In this study, we use FinBERT, a modern large language model specializedin the financial domain, for our AI-powered personal finance coach. However, FinBERT, although a cut above therest, still has room for growth, so we aim to improve its flaws and enhance its efficiency. We established thatFinBERT succeeded in detecting sentiments in explicit sentiments, but was not usually successful in doing socorrectly for implicit sentiments. FinBERT, despite its limitations, has a high accuracy and is the best model to usein our study. This model can also be utilised to provide accurate results regarding the overall trend (positive ornegative) of the global stock market. Our results demonstrate that integrating AI in personal finances is feasible andcan successfully aid individuals in making decisions regarding their finances.

Interconnected Stock Markets: Analysing Cointegration and Correlation among Global Stock Indices

This study examines the long-term and short-term relationships among five major global stock indices—NSE NIFTY (India), S&P 500 (USA), FTSE 100 (UK), Hang Seng (Hong Kong), and Nikkei 225 (Japan)—over the period from 2008 to 2023. Using advanced econometric techniques such as Johansen cointegration tests, Engle-Granger residual cointegration test and the Vector Error Correction Model (VECM), the research explores the degree of interconnectedness and cointegration among these indices. The results indicate strong positive correlations between NSE NIFTY, S&P 500, and Nikkei 225, suggesting a growing synchronization of these markets due to global economic integration. Johansen's test reveals the presence of multiple long-term equilibrium relationships, affirming that despite short-term deviations, these markets tend to move together in response to global macroeconomic factors. The analysis further highlights that the S&P 500 and FTSE 100 indices exhibit significant error correction mechanisms, underscoring their leadership role in global market dynamics. In contrast, NSE NIFTY shows a weaker adjustment toward long-term equilibrium, indicating its reactive nature to changes in other global indices, particularly the S&P 500. The study finds that while Hang Seng and Nikkei 225 are connected globally, their short-term linkages appear weaker, likely influenced by regional factors. Overall, the results emphasize the critical influence of the U.S. and Japanese markets on NSE NIFTY, with both long-term cointegration and short-term market dynamics shaping the Indian stock market’s movements. This research offers key insights for investors seeking to understand global stock market interdependencies and manage portfolio risk.

Carbon-centric dynamics of Earth’s marine phytoplankton

Marine phytoplankton are fundamental to Earth's ecology and biogeochemistry. Our understanding of the large-scale dynamics of phytoplankton biomass has greatly benefited from, and is largely based on, satellite ocean color observations from which chlorophyll-a (Chla), a commonly used proxy for carbon biomass, can be estimated. However, ocean color satellites only measure a small portion of the surface ocean, meaning that subsurface phytoplankton biomass is not directly monitored. Chla is also an imperfect proxy for carbon biomass because cellular physiology drives large variations in their ratio. The global network of Biogeochemical (BGC)-Argo floats now makes it possible to complement satellite observations by addressing both these issues at once. In our study, we use ~100,000 water-column profiles from BGC-Argo to describe Earth's phytoplankton carbon biomass and its spatiotemporal variability. We estimate the global stock of open ocean phytoplankton biomass at ~314 Tg C, half of which is present at depths not accessible through satellite detection. We also compare the seasonal cycles of carbon biomass stocks and surface Chla visible from space and find that surface Chla does not accurately identify the timing of the peak annual biomass in two-thirds of the ocean. Our study is a demonstration of global-scale, depth-resolved monitoring of Earth's phytoplankton, which will be crucial for understanding future climate-related changes and the effects of geoengineering interventions if implemented.

Assessment of Biomass, Carbon Stock and Sequestered CO2 in Teak (Tectona grandis L.f.) Plantation at Pt. Ravishankar Shukla University Campus, Raipur, Chhattisgarh, India

Carbon sequestration through tree plantation has significant role to mitigate the increasing level of CO2. Accurate measurement of carbon in teak plantations is required for estimating their contribution to global carbon stocks. The purpose of present work was to assess total biomass, carbon stock and sequestered CO2 in an 18 years old teak (Tectona grandis L. f.) plantation using allometric equations. Estimated total biomass, carbon stock and sequestered CO2 were 79.31, 37.28 and 136.66 t ha-1, respectively. Teak plantations in Chhattisgarh serve an important role in carbon sequestration, which contributes to climate change mitigation. It is a fast-growing species with robust, high-carbon wood, accumulates a substantial quantity of carbon over time. Expanding and sustaining these plants reduces atmospheric CO2 levels while also benefiting the region’s economy through timber production and forest-based livelihoods.

Challenging Institutional Path Dependence Through Field Configuring Events: Exploring the Collective Institutional Entrepreneurship of the Sustainable Stock Exchanges Initiative

ABSTRACTResearch Question/IssueHow can a global social movement, mobilized through a multi‐stakeholder initiative, promote the uptake of novel governance practices in distinct national institutional environments?Research Findings/InsightsUsing data from all 114 of the world's stock exchanges, covering over 99% of global stock market capitalization as of 2018, I find that exchanges in common‐law countries were more likely to become early partners of the Sustainable Stock Exchanges Initiative (SSEI) after its launch in 2012. However, exchanges based in civil‐law countries experienced a higher likelihood (i.e., risk) of adoption, as measured by the hazard ratio, particularly during a series of sustained field‐configuring events (FCEs) promoted by the SSEI. Over time, this increased hazard ratio indicated a rising likelihood of adoption for civil‐law‐based exchanges, eventually surpassing that of their common‐law counterparts.Theoretical/Academic ImplicationsWhile institutional environments' path dependency can facilitate or hinder the adoption of governance practices, collective institutional entrepreneurship (CIE) can moderate this path dependency and reveal new information regarding the structural fit between promoted governance practices and their institutional environments.Practitioner/Policy ImplicationsThe insights provided by this study can help practitioners understand why some practices face resistance in specific institutional environments, while others are more readily accepted. These insights also help practitioners communicate the elements of governance practices that implicitly align with the institutional environments in which they are seeking adoption.

Integrating material flow analysis and supply chain resilience analysis to study silicon carbide

AbstractSilicon carbide (SiC) is a niche nonmetallic material that is essential in many industrial processes. Here, we integrate material flow analysis and supply chain resilience analysis to understand global SiC stocks and flows and to assess its supply chain. We use industry interviews to fill data gaps and collect information on the SiC system to overcome data scarcity. We find that globally around 1000 kt of SiC is produced each year. The biggest use of SiC is the abrasives industry (40%), followed by metallurgy (28%), refractories (20%), technical ceramics (0.7%), other uses (0.7%), and semiconductors (0.01%). As an energy‐intensive material, the SiC supply chain is under pressure, increasing the relevance of resilience considerations. Besides typical supply chain risks such as low diversity of supply and geopolitical trade restrictions, SiC particularly faces risks due to its energy‐intensive production process and associated emissions. In the SiC semiconductor supply chain, losses of nearly 75% are a particular issue. Due to high demand in the SiC market, stockpiles are negligible, and substitution is difficult in most sectors. We find that in the case of SiC, sustainability measures such as use reduction, recycling, or decreasing energy use or emissions would also positively contribute to supply chain resilience. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

The global distribution and drivers of wood density and their impact on forest carbon stocks.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

Impact of cellulose enrichment on castor oil polyurethane sheets: A path to greener materials

AbstractDue to the growing concern over the depletion of the global crude oil stock and environmental issues, the replacement of petroleum‐based polymers with environmentally benign alternatives derived from renewable resource has attracted great attention. In the current study, four different polyurethane (PU) sheets were synthesized from unmodified castor oil (CO) and hexamethylene diisocyanate (HDI), incorporating cellulose nanofibers (CNF) as a filler. The mixture consisting molar ratio of CO, HDI, and CNF in a ratio of 1:5:0.8 yielded the best results. This formulation achieved a tensile strength of 22.55 MPa and an elongation at break of 55.9%. Additionally, the resulting PU sheets exhibited excellent thermal properties, with a glass transition temperature (Tg) and melting temperature (Tm) that were notably enhanced, further highlighting the effectiveness of this specific mixture in producing high‐performance, bio‐based PUs. Fourier‐transform infrared spectroscopy (FTIR), thermal, and mechanical characterizations indicated that both reinforced and unreinforced PU exhibited properties within acceptable ranges. Given the bio‐based origins of CO and cellulose, the synthesized polymers are biodegradable and suitable for biomaterial applications. The study confirms that the bio‐based origins of CO and cellulose render synthesized polymers biodegradable, suitable for various biomaterial applications.

Matrix GARCH model: Inference and application*

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARCH specification for the trace of conditional row or column covariance matrix, which allows for the model identification. Moreover, we introduce a quasi-maximum likelihood estimator (QMLE) for model estimation and develop a portmanteau test for model diagnostic checking. Simulation studies are conducted to assess the finite-sample performance of the QMLE and portmanteau test. To handle large dimensional matrix time series, we also propose a matrix factor GARCH model, and establish its theoretical properties. Finally, we demonstrate the superiority of the matrix GARCH and matrix factor GARCH models over existing multivariate GARCH-type models in volatility forecasting and portfolio allocations using three applications on credit default swap prices, global stock sector indices, and future prices.

Climate Change Effects in Stock Markets

Climate change significantly impacts global stock markets through both short-term extreme weather events and long-term climate shifts. This study examines the relationship between CO2 emissions and stock returns across 19 countries from 2001 to 2020 using regression analysis. The findings also reveal a positive correlation between CO2 emissions and stock returns, indicating that investors anticipate higher returns to offset climate-related risks. This suggests that higher CO2 emissions drive investor expectations for increased returns to compensate for potential economic losses. The study highlights the importance of balanced emission reduction strategies and promoting green finance to support sustainable economic growth. Additionally, it underscores the need to integrate climate risks into financial decision-making, offering valuable insights into the financial implications of climate change and guiding policymakers and investors in addressing these challenges. By providing empirical evidence on the link between CO2 emissions and stock returns, this research enriches existing knowledge and emphasizes the financial risks and opportunities presented by climate change. The study's results align with the carbon risk premium theory, which posits that investors demand higher returns from companies with higher carbon emissions. This study advocates for policies that balance economic growth with environmental sustainability, promoting green finance initiatives to facilitate the transition to a low-carbon economy and mitigate the adverse effects of climate change on financial markets.

Modeling fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles in 1970–2020

Abstract. Vehicles are among the most important contributors to global anthropogenic CO2 emissions. However, the lack of fuel-, vehicle-type-, and age-specific information about global on-road CO2 emissions in existing datasets, which are available only at the sector level, makes these datasets insufficient for supporting the establishment of emission mitigation strategies. Thus, a fleet turnover model is developed in this study, and CO2 emissions from global on-road vehicles from 1970 to 2020 are estimated for each country. Here, we analyze the evolution of the global vehicle stock over 50 years, identify the dominant emission contributors by vehicle and fuel type, and further characterize the age distribution of on-road CO2 emissions. We find that trucks accounted for less than 5 % of global vehicle ownership but represented more than 20 % of on-road CO2 emissions in 2020. The contribution of diesel vehicles to global on-road CO2 emissions doubled during the 1970–2020 period, driven by the shift in the fuel-type distribution of vehicle ownership. The proportion of CO2 emissions from vehicles in developing countries such as China and India in terms of global emissions from newly registered vehicles significantly increased after 2000, but global CO2 emissions from vehicles that had survived more than 15 years in 2020 still originated mainly from developed countries such as the United States and countries in the European Union. The data are publicly available at https://doi.org/10.6084/m9.figshare.24548008 (Yan et al., 2024).

COMPARISON OF VECTOR AUTOREGRESSIVE (VAR) AND VECTOR ERROR CORRECTION MODELS (VECM) FOR THE COMPOSITE STOCK PRICE INDEX (JCI) IN INDONESIA

The capital market has a major role for economic development which is able to encourage capital formation and sustain economic growth. Financial liberalisation between countries in the world will make capital markets in each country interconnected. If there is a large movement in the global stock index, it will also have an impact on the JCI in Indonesia. In addition to global indices, macroeconomics is also seen as capable of influencing capital markets. Inflation and interest rates are macroeconomic components that can be used. The purpose of this research is to be able to see the relationship between one variable and another, namely the Composite Stock Price Index (JCI), Dow Jones Industrial Average (DJIA), inflation rate, interest rate. The VAR/VECM analysis approach was used in this study. Data was taken from 2017 to 2023. The data used for modelling is 84 observations. Based on the results, Inflation and DJIA have a long-term influence on the increase of JCI, while interest rates have no long term influence on JCI in Indonesia. With the model projection for the next 10 years. The projection results state that the variability of JCI in Indonesia is most dominantly explained by the JCI variable itself with a proportion of 95.21619%, while inflation contributes 0.193202%, interest rates 0.249170%, and DJIA index 4.341438%. The results of this analysis can be used as a consideration of strategies in increasing the JCI in Indonesia to maintain the health and stability of the economy.

The Impact of the COVID-19 Pandemic on Trading in the Stock Market: A Legal Perspective

The COVID-19 pandemic significantly impacted global stock markets, leading to unprecedented volatility and legal challenges. This study explores how the pandemic altered trading behavior and examines the associated legal ramifications. In March 2020, stock markets experienced a sharp decline, prompting the U.S. Federal Reserve and other regulatory bodies to intervene. While markets rebounded, volatility persisted, introducing heightened risks for investors. Non- professional investors became more active, complicating regulatory oversight. From a legal perspective, the pandemic raised concerns about stock price manipulation, insider trading, and market abuse. Regulators, like the U.S. Securities and Exchange Commission (SEC), responded by implementing temporary measures to maintain market integrity. These legal frameworks, designed for traditional crises, struggled to address the unique challenges posed by COVID-19. Furthermore, the pandemic revealed gaps in existing laws, prompting calls for updated regulations to manage market disruptions during non-financial crises. The study underscores the need for ongoing legal adaptation to safeguard markets and prevent exploitation during global emergencies. As the pandemic evolved, so did the legal landscape, with cases emerging that highlighted both opportunistic trading and regulatory inadequacies. This research contributes to understanding the intersection of market volatility and legal frameworks in times of crisis.

Global Stock Markets during Covid-19: Did Rationality Prevail?

This study assesses the validity of the Efficient Markets Hypothesis (EMH) during the Covid-19 period by evaluating whether various stock markets around the world accurately predicted economic performance in their respective countries. The underlying premise is that stock prices should discount future company cash flows, and the projection of these cash flows can be proxied by investors' ability to forecast macroeconomic conditions. We assess stock market performance from January 2020 to February 2022 in 16 countries, equally split between developed and emerging markets. While results varied, the study suggests that stock markets generally predicted economic activity during Covid-19. The modeling test indicated that stock markets significantly predicted economic activity in 11 out of 16 countries, particularly in developed markets. Furthermore, incorporating the stock market as a variable, improved economic forecasts in all 16 countries. Unlike other studies that mainly focused on the characteristics of share price movements, this research judges the rationality of stock markets against an external criterion. Ultimately, it suggests that EMH was mostly vindicated during the Covid-19 period.

Global Estimates of Particulate Organic Carbon Concentration From the Surface Ocean to the Base of the Mesopelagic

AbstractThe gravitational settling of organic particles from the surface to the deep ocean is an important export pathway and one of the largest components of the ocean carbon pump. The strength and efficiency of the gravitational pump are often measured using metrics reliant on reference depths and empirical formulations that parameterize the relationship between depth and the flux or concentration of particulate organic carbon (POC). Here, BGC‐Argo profiles were used to identify the isolume where POC concentration, [POC], starts to decline, revealing attenuation trends below this isolume that are remarkably consistent across the global ocean. We developed a simple empirical approach that uses observations from the first optical depth to predict [POC] from the surface ocean to the base of the mesopelagic (1,000 m), allowing assessments of spatial and temporal variability in gravitational pump efficiencies. We find that rates of [POC] attenuation are high in areas of high biomass and low in areas of low biomass, supporting the view that bloom events sometimes result in a relatively weak deep biological pump that is characterized by low transfer efficiency to the base of the mesopelagic. Our isolume‐based attenuation model was applied to satellite data to yield the first remote sensing‐based estimate of integrated global POC stock of 3.02 Pg C over the top 1,000 m, with an uncertainty of 0.69 Pg C. Of this total stock, approximately 1.02 Pg was located above the reference isolume where [POC] begins to attenuate.

Proximity to inlet channel drives spatial variation in sediment carbon across a lagoonal seagrass meadow

Seagrass meadows can be sinks for organic carbon, but estimates of global organic carbon stocks are complicated by substantial spatial variability in organic carbon burial observed within meadows. To improve estimates of organic carbon burial in seagrass meadows, it is necessary to understand the causes of the spatial heterogeneity. This study investigated relationships between spatial patterns in sediment organic carbon storage and accretion rates, hydrodynamics, and proximity to sources of organic carbon in a current-dominated Zostera marina Linnaeus meadow in Menemsha Pond, Massachusetts, USA. Sediment and velocity measurements were conducted at six stations along a 150-m transect across the meadow oriented perpendicular to the pond's unvegetated inlet channel. The meadow's edge near the channel had higher organic carbon than the channel as well as the highest organic carbon within the meadow. With increasing distance from the meadow's edge, all of the following decreased: sediment organic and total carbon, sediment accretion rates, peak tidal velocity, sediment trap mass deposition rate, and the relative contribution of non-seagrass sources to sediment organic carbon. Lower tidal velocities farther from the inlet channel reduced sediment resuspension, consistent with lower sediment trap mass deposition, which should enhance organic carbon content and organic carbon accretion rates. However, the opposite trend of decreasing organic carbon content (>50 % across the transect) and decreasing accretion rates with distance from channel was observed. This suggested that the local hydrodynamic intensity was not controlling organic carbon accretion, which was instead constrained by supply limitation and controlled by the lagoon-scale flow circulation.

Connectedness and systemic risk between FinTech and traditional financial stocks: Implications for portfolio diversification

The rapid growth in FinTech is attracting significant investor attention. There are concerns that FinTech stocks may be a source of systemic risk and this has implications for the stability of the financial sector. This study uses a TVP-VAR model to estimate the dynamic connectedness between global FinTech stocks and the stocks of large US banks, regional US banks, US insurance, and global private equity. The findings reveal that FinTech and insurance stocks are net receivers of shocks while large US banks are a major source of shocks. Thus, FinTech stocks are not a source of systemic risk but can be affected by financial contagion and systemic risk originating from large US banks. The highest connectedness was observed at the onset of the COVID-19 lockdowns illustrating the dramatic impact that a health pandemic can have on systemic risk. Systemic risk also increased during the 2023 US bank panic. Connectedness has implications for constructing investment portfolios. The equally weighted, risk parity, and risk parity connectedness portfolios have similar portfolio summary statistics, but lower risk adjusted returns than the maximum Sharpe ratio portfolio which better captures the low correlation between FinTech stocks and traditional financial stocks. These results are robust across various portfolio rebalancing periods (daily, weekly, monthly).

Impact of crude oil price innovations on global stock market volatility: Evidence across time and space

This study investigates the impact of crude oil price innovations on global stock market volatility through a ripple-spreading network model, incorporating four dimensions of crude oil price changes: realized volatility, implied volatility, variance risk premium, and realized skewness volatility. Additionally, we assess the effects of three types of crude oil shocks—oil-specific, aggregate demand, and oil supply shocks. The results indicate that while all four dimensions exhibit similar temporal diffusion patterns, their spatial impacts differ. Global stock markets demonstrate heightened sensitivity to implied volatility and variance risk premium, followed by realized volatility and, lastly, realized skewness volatility. Moreover, we find that realized volatility spreads through multiple transmission pathways, albeit at a slower pace compared to implied volatility and the variance risk premium. Among the crude oil shocks, oil-specific shock induces the most rapid volatility transmission across global markets, with aggregate demand shock following and oil supply shock exerting the smallest influence.