Global Stock Research Articles

Soil microbiome interventions for carbon sequestration and climate mitigation.

Mitigating climate change in soil ecosystems involves complex plant and microbial processes regulating carbon pools and flows. Here, we advocate for the use of soil microbiome interventions to help increase soil carbon stocks and curb greenhouse gas emissions from managed soils. Direct interventions include the introduction of microbial strains, consortia, phage, and soil transplants, whereas indirect interventions include managing soil conditions or additives to modulate community composition or its activities. Approaches to increase soil carbon stocks using microbially catalyzed processes include increasing carbon inputs from plants, promoting soil organic matter (SOM) formation, and reducing SOM turnover and production of diverse greenhouse gases. Marginal or degraded soils may provide the greatest opportunities for enhancing global soil carbon stocks. Among the many knowledge gaps in this field, crucial gaps include the processes influencing the transformation of plant-derived soil carbon inputs into SOM and the identity of the microbes and microbial activities impacting this transformation. As a critical step forward, we encourage broadening the current widespread screening of potentially beneficial soil microorganisms to encompass functions relevant to stimulating soil carbon stocks. Moreover, in developing these interventions, we must consider the potential ecological ramifications and uncertainties, such as incurred by the widespread introduction of homogenous inoculants and consortia, and the need for site-specificity given the extreme variation among soil habitats. Incentivization and implementation at large spatial scales could effectively harness increases in soil carbon stocks, helping to mitigate the impacts of climate change.

The Spillover Effects of Market Sentiments on Global Stock Market Volatility: A Multi-Country GJR-GARCH-MIDAS Approach

In behavioral economics, it has widely been documented that there might be a close relationship between overall market sentiment and economic performance, such as GDP per capita. In this paper, we investigate the effects of market sentiment on stock market volatility, which has widely been recognized as an important factor for economic sustainability. In particular, we aim to identify the existence of spillover effects of market sentiments on global stock market volatility. As a first attempt, we chose ten countries from major economic regions over the world (including America, Asia, Europe, and Oceania), and analyzed their interdependence and interconnectedness using a GJR-GARCH-MIDAS model. The results highlight that an individual country’s stock market volatility is significantly influenced not only by its own market sentiment (proxied by the consumer confidence index) but also by the overall market sentiments of other countries across the world. The results also highlight significant country-by-country heterogeneity in the time lags of the global spillover effects, which indicates substantial heterogeneity in the behavioral dynamics of individual countries.

Precision Least Squares: Estimation and Inference in High-Dimensions

The least squares estimator can be cast as depending only on the precision matrix of the data, similar to the weights of a global minimum variance portfolio. We give conditions under which any plug-in precision matrix estimator produces an unbiased and consistent least squares estimator for stationary time series regressions, in both low- and high-dimensional settings. Such conditions define a class of “Precision Least Squares” (PrLS) estimators, which are shown to be approximately Gaussian, efficient, and to provide automatic family-wise error control in large samples. For estimating high-dimensional sparse regression models, we propose a LASSO Cholesky estimator of the plug-in precision matrix. We show its consistency and how to properly bias correct it, thereby obtaining a LASSO Cholesky-based PrLS (LC-PrLS) estimator. LC-PrLS performs well in finite samples and better than state-of-the-art high-dimensional estimators. We employ LC-PrLS to investigate the dynamic network of predictive connections among a large set of global bank stock returns. We find that crisis years correspond to a collapse of predictive linkages.

Bayesian-based multi-class damage identification on prestressed reinforced concrete bridges

Model-driven structural system identification techniques are effective for comprehensive damage assessment but relying on a single model for structural identification (St-Id) can lead to unreliable results due to the ill-posed nature of the inverse calibration problem. To address this issue, this work explores the potential of multi-class digital twins, derived from sets of competing model classes, each one characterised by a distinct failure mechanism. To accelerate the calibration process, Kriging metamodels are employed, facilitating the fusion of diverse data types, such as modal displacements, frequencies, and static rotations. The Bayesian Information Criterion (BIC) is utilised to identify the most suitable model class. This research specifically targets the damage assessment of prestressed reinforced concrete girder bridges, which represent a significant portion of the global bridge stock. To this end, a numerical model of a representative span is developed, followed by a parametric analysis to evaluate the proposed approach’s effectiveness. Various damage scenarios including stiffness reduction and tendon prestress losses are considered, demonstrating the method’s capability for quasi-real-time multi-class damage identification. The study emphasizes the combined use of modal features and static rotations within a Bayesian framework, underscoring the importance of data fusion in achieving effective damage identification for these structures.

Anthromes and forest carbon responses to global change

Societal Impact StatementForest ecosystems absorb and store about 25% of global carbon dioxide emissions annually and are increasingly shaped by human land use and management. Climate change interacts with land use and forest dynamics to influence observed carbon stocks and the strength of the land carbon sink. We show that climate change effects on modeled forest land carbon stocks are strongest in tropical wildlands that have limited human influence. Global forest carbon stocks and carbon sink strength may decline as climate change and anthropogenic influences intensify, with wildland tropical forests, especially in Amazonia, likely being especially vulnerable.Summary Human effects on ecosystems date back thousands of years, and anthropogenic biomes—anthromes—broadly incorporate the effects of human population density and land use on ecosystems. Forests are integral to the global carbon cycle, containing large biomass carbon stocks, yet their responses to land use and climate change are uncertain but critical to informing climate change mitigation strategies, ecosystem management, and Earth system modeling. Using an anthromes perspective and the site locations from the Global Forest Carbon (ForC) Database, we compare intensively used, cultured, and wildland forest lands in tropical and extratropical regions. We summarize recent past (1900‐present) patterns of land use intensification, and we use a feedback analysis of Earth system models from the Coupled Model Intercomparison Project Phase 6 to estimate the sensitivity of forest carbon stocks to CO2 and temperature change for different anthromes among regions. Modeled global forest carbon stock responses are positive for CO2 increase but neutral to negative for temperature increase. Across anthromes (intensively used, cultured, and wildland forest areas), modeled forest carbon stock responses of temperate and boreal forests are less variable than those of tropical forests. Tropical wildland forest areas appear especially sensitive to CO2 and temperature change, with the negative temperature response highlighting the potential vulnerability of the globally significant carbon stock in tropical forests. The net effect of anthropogenic activities—including land‐use intensification and environmental change and their interactions with natural forest dynamics—will shape future forest carbon stock changes. These interactive effects will likely be strongest in tropical wildlands.

Warren Buffett and Berkshire Hathaway's Recent Investment Strategy Changes and the Reasons for the Corresponding Risk Measures

With the development and changes of the world economy, the size of the global securities market is gradually increasing, and the investment methods available for citizens to choose from are becoming more diverse. Stocks, as one of the most important investment methods, have also received increasing attention from investors. After experiencing a slow economic recovery, the distribution of global stock and securities markets is also constantly expanding. In recent years, the global ETF market has crossed new milestones, with a scale exceeding $11 trillion for the first time, showing an unstoppable development trend, reflecting the recovery and growth of the global securities market after experiencing the financial crisis. In addition, global stock indices have generally risen. Especially for assets that are lagging behind in 2023, it shows an upward trend in the global securities market after entering 2024. Due to the continuous changes and maturity of the economic landscape and securities market, it has brought huge social dividends and increased investor willingness. As an outstanding representative in the fields of stock and securities investment, Buffett's investment ideas and strategies have been highly valued by the general public. Therefore, studying typical cases of Buffett's investments has a guiding significance. This article will analyze Buffett's investment preferences and performance from 2023 to the first half of 2024, starting from the current international background and combining Buffett's value investment philosophy. It will provide reference suggestions for investment industry practitioners and regulatory agencies.

The Magnet Effect of Price Limits: An Agent-Based Approach

ABSTRACT In this paper, we study the magnet effect within the context of circuit breaker events that transpired in global stock markets during March 2020. Our analysis encompasses nine such events across six countries. Employing a time-distanced quadratic function to model trading activities leading up to limit hits, we observe a substantial acceleration in return movement and the convergence of traders’ behavior preceding limit hits. Furthermore, most circuit breaker events manifest a distinct reduction in trading volume and amplified market stability during this crucial phase. To gain deeper insights into the magnet effect, we employ an agent-based model of the stock market that incorporates price limits. Our model replicates the stylized facts associated with limit hit events and the magnet effect. Importantly, our simulation results closely align with empirical observations. We also emphasize the importance of market environment changes and shifts in traders’ behavior in influencing limit hit events and the magnet effect.

Important role of Fe oxides in global soil carbon stabilization and stocks

Iron (Fe) oxides can interact with soil organic carbon (SOC) to form Fe-bound organic carbon (OC-Fe), which strongly promotes SOC protection, mitigating global climate change. However, the global patterns and factors controlling OC-Fe are unclear. Here, we conducted a meta-analysis of 3,395 globally distributed soil profiles to reveal the role of Fe-Al oxides in global soil carbon stabilization and stocks. The global OC-Fe stock in topsoil is 233 PgC, accounting for 33 ± 15% of the total SOC stock. A substantial OC-Fe deficit (difference between OC-Fe and OC-Femax) was observed at the equator and at mid-latitudes. Our findings suggest that mineral factors should be incorporated into soil carbon models to improve model predictions. Although there are uncertainties in current OC-Fe extraction method, the global distribution of OC-Fe and OC-Femax constitutes a vital resource for future research targeting carbon cycling issues and offers innovative strategies for global soil carbon sequestration initiatives.

Hybrid metaheuristic algorithm methods and econometric models in prediction of dogecoin price

Purpose This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between crypto-price and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision. Findings The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models. Originality/value The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies’ prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

Soil carbon in the world’s tidal marshes

Tidal marshes are threatened coastal ecosystems known for their capacity to store large amounts of carbon in their water-logged soils. Accurate quantification and mapping of global tidal marshes soil organic carbon (SOC) stocks is of considerable value to conservation efforts. Here, we used training data from 3710 unique locations, landscape-level environmental drivers and a global tidal marsh extent map to produce a global, spatially explicit map of SOC storage in tidal marshes at 30 m resolution. Here we show the total global SOC stock to 1 m to be 1.44 Pg C, with a third of this value stored in the United States of America. On average, SOC in tidal marshes’ 0–30 and 30–100 cm soil layers are estimated at 83.1 Mg C ha−1 (average predicted error 44.8 Mg C ha−1) and 185.3 Mg C ha−1 (average predicted error 105.7 Mg C ha−1), respectively.

Impacts of Donald Trump's tweets on volatilities in the European stock markets

This study investigates whether political news from Donald J. Trump affects European stock markets. We use realized volatility and the HAR-RV model to examine the effect of Trump's tweets on the stock market volatility in Germany, France, and the UK during his last presidential term. Results indicate that Trump's tweets positively impact volatilities in European stock markets, with these impacts increasing as attention to his tweets rises. The effect on the UK stock market is relatively weaker. The empirical results indicate that Trump's tweets influence global stock markets beyond the US.

Interdependence Between Energy Commodities and Global Stock Indices: A Volatility Transmission Approach

This study aims to investigate the volatility transmission between energy commodities and global stock indices. Two primary energy commodities, Brent crude oil and Natural gas, are considered using monthly data from January 2001 to June 2023. Dynamic conditional correlation–GARCH method has been employed to examine the interdependence between two selected energy commodities and stock indices of India, the United States and Japan. The results show a high degree of interdependence between Brent crude oil and the global stock market indices of India, the United States and Japan compared to natural gas. The study also shows that long-run shocks persistent by the crude oil price variability have a more substantial influence on the global stock indices of India, the United States and Japan than short-run stocks. Swings in commodity prices and their effect on the stock market volatility indicate that policymakers and regulators should learn about financial system risks and vulnerability to external shocks.

Global Stock Markets Volatility Correlation Structure and Implication of Portfolio Based on Complex Network Theory

Global Stock Markets Volatility Correlation Structure and Implication of Portfolio Based on Complex Network Theory

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

An Integrated Approach with Permutation Entropy Measure and Conventional Tests for Study on Stock Market Efficiency

Countries globally strive to create efficient stock markets to stimulate economic growth and encourage more investors. This paper deals with testing the weak form of the Efficient Market Hypothesis (EMH) using daily data across 57 global stock markets spanning from 2013 to 2023. Moreover, the study focuses on employing the combination of the Permutation Entropy measure to compute the weak form efficiency degree and several conventional tests to obtain a sufficient evaluation of the market efficiency in the sample. The traditional test results provide inconsistent conclusions on the weak form efficiency as the data series are non-normal distributed but follow a random walk pattern. However, combined with the high Permutation entropy scores estimated for the sample, it is concluded that all 57 markets are weak-form efficient. Furthermore, despite some exceptions, the developed markets tend to display the highest degrees of efficiency, followed by emerging markets, while frontier markets show the weakest efficiency.

Investing in relative market positions in interconnected financial markets: A strategy for international portfolio diversification

International investors face challenges in properly diversifying their portfolios risk due to the increasingly interconnected nature of financial markets. Using the Tail-Event Driven Network (TENET) model, this study proposes two new indices, relative dominance and relative importance, to measure the relative market position of a stock market in the global stock market network. This study further proposes a dominance-importance-oriented (DIO) strategy to diversify portfolio risk in interconnected stock markets using data from the stock markets of 35 economies. The empirical findings show that stock markets in developed countries generally have higher relative dominance and lower relative importance, whereas those in developing and emerging countries have the opposite pattern. Moreover, financial liberalization is an important determinant of stock markets' relative positions in the global market network, although its impact varies with the economic development of their domicile countries. Furthermore, the DIO strategy offers a feasible and effective method for international portfolio diversification by jointly considering relative dominance and importance, verifying that portfolio diversification in terms of relative market position is beneficial.

How Should Investors’ Long-Term Returns Be Measured?

We assess measures of long-horizon investment outcomes and clarify underlying trading strategy interpretations. We focus attention on a measure we call the “sustainable return,” defined as the rate of periodic withdrawal for consumption consistent with the preservation of real capital. We use this notion to highlight the role of return sequence risk, which is distinct from risk in the overall level of returns. We illustrate this and several other long-horizon measures in a global stock sample, emphasizing limitations of the arithmetic and geometric means of short-interval returns and the necessity in many contexts to consider the reinvestment of interim cash flows.

The Effects of COVID-19 on the Returns and Volatilities of Global Stock Indexes

At the beginning of 2020, COVID-19 broke out around the world, and its impact on the global economy exceeded expectations. Countries declared states of emergency one after another. The outbreak not only triggered a crisis of supply, demand, investment, and supply chain disruptions but also transmitted to the financial sector. Global risk aversion is rising rapidly, and global stock markets continue to plummet, threatening to trigger further financial crises. To investigate the impact of COVID-19 on the financial sector and to prevent and mitigate financial risks, this paper examines the impact of COVID-19 on global stock markets from a global perspective. Firstly, selected data of seven global stock indices using WRDS and S&P Capital IQ databases. After processing these data, ARMA-GARCH modeling was carried out using the data before the outbreak and the data for the whole sample period respectively. Then, the residuals of the model were subjected to L-B and ARCH-LM tests to determine the fit of the model for different stock indices and different periods. Next, determine if the return and volatility of the equity index have changed during COVID-19 based on the goodness of fit. The results of the study show that: (1) The outbreak of COVID-19 has had a serious impact on the yields and volatility of major global equity indices. As can be seen from the descriptive statistics of the data, particularly around March 2020, each of the sample equity indices experienced high volatility. (2) By fitting the post-outbreak data using the pre-outbreak fitted model, it was found that the fit was poor. This suggests that the outbreak of the new crown epidemic had a significant impact on the process of generating stock data. (3) The results of this paper show that the data for the China SSE Composite Index after the outbreak still fit the pre-outbreak fitted model well. This also indicates that the Chinese mainland stock market has shown greater stability than other markets when all major world stock indices were significantly hit by COVID-19.

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.