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Investors are using the stock market to invest their money for capital appreciation. Recent advancements in the information system have made stock market investments accessible to various investors. This study aims to examine the relationship between the stock price index and Gross Domestic Product (GDP) using empirical data, focusing on the short-term or long-term relationship, as well as the impact of available information on stock prices. The research is supported by theories such as the efficient market hypothesis, stock valuation theory, the balance sheet model, and Tobin's q theory. Secondary data from 1995-2022 were utilized, with quarterly data obtained from the Philippines Statistics Authority (PSA) and the Macrotrend. Net. Diagnostic tests were conducted to ensure there were no violations of normality, homoscedasticity, and serial correlation. Econometric tests were utilized to test the historical data, including the Augmented Dickey-Fuller (ADF) test for stationarity, the lag length test, the Johansen cointegration test, and the Granger causality test. The results indicate a positive long-term relationship between the stock price index and GDP. The Vector Error Correction Model (VECM) supports this finding, revealing that a 1.02% increase in the stock price index corresponds to a GDP increase. These findings support the balance sheet theory, indicating that wellperforming companies attract investment, leading to higher investment income and subsequent positive economic impacts. Additionally, the availability of information plays a role in stock prices, emphasizing the importance of information security for companies. Future research could explore the effects of specific stock classes on GDP, conduct comparative studies among different countries, or examine other economic indicators' influence on stock prices and the economy. Moreover, investigating the impact of information system advancements on stock prices and the overall economy presents an interesting avenue for further exploration. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. Furthermore, social media has garnered attention for its predictive capabilities in various fields, including financial markets and the economy. In this study, we exploit the predictive power of sentiment from Twitter and Reddit, alongside Google Trends indexes, to forecast log returns for 10 cryptocurrencies, namely Bitcoin, Ethereum, Tether, Binance Coin, Litecoin, Enjin Coin, Horizen, Namecoin, Peercoin and Feathercoin. We evaluate the performance of LASSO Vector Autoregression using daily data from January 2018 to January 2022. In a 30-day recursive forecast, we achieve a mean directional accuracy (MDA) rate of over 50%. Moreover, we observe a significant increase in forecast accuracy in terms of MDA when using sentiment and attention variables as predictors, but only for less capitalized cryptocurrencies. This improvement is not reflected in the RMSE. We also conduct a Granger causality test using post-double LASSO selection for high-dimensional VAR models. Our results suggest that social media sentiment does not Granger-cause cryptocurrencies returns. [ABSTRACT FROM AUTHOR]

Value relevance can be defined as the ability of accounting variables to explain or predict financial outcomes. This study intends to examine the value relevance of accounting information, specifically the impact of accounting information on the stock prices of non-financial public companies listed on the Bulgarian Stock Exchange. The examined period is 01.01.2016 -- 31.12.2021. We apply the panel unit root test, descriptive statistics, the panel regression models, the correlation matrix, and the Granger causality test. The results collected demonstrate that the earnings per share, the dividend per share, and the book value of equity per share calculated for the analyzed companies have an impact on the stock price of these enterprises. A moderate positive correlation between stock price and other accounting variables is revealed. [ABSTRACT FROM AUTHOR]

The primary determinants of business cycle synchronization among OECD countries and their interactions are examined using panel Granger causality tests, single-equation panel regressions, simultaneous equations model error component three-stage least squares (EC3SLS), and cross-sectional 3SLS econometric techniques. The sample spans the years 1990 through 2021. The results demonstrate the direct and indirect effects of the key macroeconomic variables on the synchronization of the business cycles of the sample economies, as well as the complementary and competing nature of these variables. The magnitude and significance of these macroeconomic factors in relation to the business cycle synchronization before and after the global financial crisis of 2009 are also examined in this study. Furthermore, the analysis supports the ex-post argument for entering the euro currency union. The study article contributes by providing direction for future empirical research on the sources and propagation mechanisms of international business cycle transmission. [ABSTRACT FROM AUTHOR]

In recent years, with the enhancement of pollution control and ecological environmental protection, the quality of the ecological environment in China has continued to improve, but the problem of haze pollution with PM2.5 characteristics should not be underestimated. Daily PM2.5 data of 11 prefecture-level cities in Jiangxi Province from January 1 to December 31 in 2022 was selected, and the spatial spillover effects of haze pollution from a network perspective. The main findings are as follows: (1) The spatial correlation network of haze pollution for the 11 prefecture-level cities in Jiangxi Province is constructed using the Granger causality test. (2) The network characteristics were obtained using social network analysis. Partial result Nanchang emerges as the most significant media city in this network. (3) The network can divided into four distinct sectors, namely a sector representing net benefits, a sector indicating net overflow, a bilateral overflow sector and a broker sector. These four sectors have distinct geographical distributional characteristics. (4) The spatial correlation network of haze pollution in the 11 prefecture-level cities was found to be correlated with the level of urbanization difference by Quadratic assignment procedure (QAP) regression analysis, but the single urban population proportions did not pass the significance level test of 10%. Based on the analysis above, this paper considers that it is an important strategy for the Jiangxi Province haze control to establish a regional collaborative prevention and control mechanism, implement the differentiated regional governance strategy, and achieve the high-quality development of the whole province. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of this paper is to analyse the influence of capital efficiency on the economic growth of Bangladesh using the Harrod-Domar (H-D) model. Design/methodology/approach: We use annual data from 1980 to 2019 for this paper. Three steps are taken in the data analysis. First, to check the existence of a unit root, we use the augmented Dickey-Fuller (ADF) test and to determine co-integration among the variables, we use the Johansen-Juselius co-integration test. Next, for long-run estimation, we use the dynamic ordinary least square (DOLS) estimator. The sensitivity of the long-run estimations is further checked by the fully modified OLS (FMOLS) and autoregressive distributed lag (ARDL) estimators. Lastly, we use the Granger causality test to determine the long-run causality among the variables. Findings: The long-run co-integration test validates the co-integrating relationship among the variables. DOLS estimations reveal that the economic growth of Bangladesh is negatively associated with the incremental capital output ratio (ICOR), validating the notion that capital efficiency matters for achieving higher economic growth. On average, an increase in ICOR by a unit tends to reduce economic growth in the long term by 0.75 percent. Our results also reveal no significant relationship between savings and economic growth when the model is extended. Finally, causality results indicate unidirectional causality between ICOR and economic growth. Practical implications - Based on the results obtained, we argue that the enhancement of capital productivity could bring efficiency because ICOR is an inverse of capital productivity. Since Bangladesh's capital productivity is considerably low compared with other neighbouring countries, it is suggested that firms should gradually move towards technological advancement and enhance economies of scale, etc. in the long run. Moreover, policies in favour of continuous skill development programmes could be highly effective in increasing capital productivity given that capital follows a vintage structure. Originality/value: This is the first paper to analyse the economic growth pattern of Bangladesh using the traditional H-D model by incorporating variables such as savings and ICOR and also by relaxing the assumption of time-invariant (i.e. fixed) data of the variables. Moreover, this paper extends the traditional H-D empirical model by introducing key indicators and time breaks for Bangladesh's economy through a stepwise regression process. [ABSTRACT FROM AUTHOR]

This study aims to examine the relationship between interest rates and inflation in Pakistan, specifically considering the Fisher effect and the non-Fisher effect. The ongoing debate regarding whether raising interest rates contributes to higher inflation rather than effectively controlling it has motivated our research. To investigate this, we utilize time-varying vector auto-regressive and Granger causality tests. Our findings reveal the significance of the neo-Fisher effect, particularly from 2015 onwards, compared to the traditional Fisher effect in Pakistan. [ABSTRACT FROM AUTHOR]

West Africa has been grappling with various security challenges prompting governments' intervention via military expenditure both at the country and regional level. Given the sporadic surge in military expenditure in the region and the potential effect such expenditure may exert on investment which is a sin qua non to the development process of any economy or region, this study utilizes the augmented mean group (AMG) approach and the Granger non-causality test in investigating the impact and causal relationship between investment and military expenditure in the Economic Community of West African States (ECOWAS) between 1980 and 2020. With country-specific cross-dependence and heterogeneity adequately accounted for, the study found that military expenditure has a dampening impact on investment at both the panel and country levels; unemployment adversely impacts investment; whereas economic growth stimulates investment (catalytic effect) in the ECOWAS, although at differing levels of significance. The study also established a long-run relationship among the variables; with only economic growth Granger causing investment. The study thus recommends that country-specific and regional-based military policies be established to glean economic growth through viable investment. [ABSTRACT FROM AUTHOR]

This study analyses the interrelation between technological innovation (TI) and trade friction (TF) by applying the bootstrap rollingwindow full- and subsample Granger causality test from China in a sample from January 2002 to December 2021. Results show that the influence of TI on TF is twofold. On the one hand, TI is a push for TF. This finding is consistent with the ‘income effect’, which postulates that TI leads to more TF by affecting the income of other countries. On the other hand, TI has a negative influence on TF. This result confirms the ‘substitute effect’, implying that TI can benefit consumers by providing more high-quality and cheaper products. In turn, TF can hinder TI by reducing exporters’ profits. Based on these findings, governments should coordinate their efforts toward innovation and trade policies. At the same time, firms should master core technology and develop their high-performance products to avoid the risk of TF. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between political instability, economic growth, and Greece's overall stock market index (ASE) returns from 1970 to 2018. The aim is to measure and quantify the impact of political instability on economic growth and the overall stock market returns. The analysis includes six different variables as proxies that quantify political instability. A new Socio-Political Instability index is constructed from these variables by employing the principal component analysis. The empirical part employs GARCH, GARCH-M, Granger causality tests and OLS regressions. The paper provides new insightful results and a new measure of political instability for Greece. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between insurance development, population, economic growth, and health expenditures for a panel of 31 OECD countries over the period 1995–2021. A dynamic panel data analysis based on cointegration, FMOLS, DOLS, VECM, and Granger causality tests is employed to suggest for the existence of a long-run relationship among variables. The long-run results show that insurance development, national income, and population exhibit positive impacts on health expenditures. The results reveal that insurance factor has larger income effects than substitution effects on health expenditures. Regarding the short-run causal relationship between the variables, the empirical results suggest that economic growth strengthens health expenditure growth, while insurance growth reduces it. In the short-run, insurance development has a crowding-out effect since it produces larger substitution effects than income effects. The results provide political implications that governments need to concern the short-run crowding-out effect of private insurance sections on health expenditures when making fiscal policies on public health expenditures. [ABSTRACT FROM AUTHOR]

This paper investigates the predictive relationships among climate policy uncertainty (CPU), oil prices, and renewable energy (RE) stock market returns, particularly highlighting the challenges posed by the varying data frequencies of these variables. The study utilizes a comprehensive dataset comprising monthly CPU, daily oil prices, and RE stock returns, sourced globally. By applying a mixed-frequency causality test (MFCT), the analysis reveals significant predictability across different time horizons, particularly highlighting the strong influence of oil prices on RE stock returns over short-term horizons, while CPU demonstrates a more pronounced effect over medium to long-term horizons. In contrast, the application of the classical Granger causality test on low-frequency (monthly) data indicates an insignificant relationship between CPU and RE stocks, suggesting that traditional models may overlook important predictive dynamics. The analysis was conducted using Matlab code, and the findings provide valuable insights for policymakers in designing effective climate policies and for investors in optimizing portfolio strategies and hedging against risks. [ABSTRACT FROM AUTHOR]

Timely and accurate traffic flow prediction is crucial for stabilizing road conditions, reducing environmental pollution, and mitigating economic losses. While current graph convolution methods have achieved certain results, they do not fully leverage the true advantages of graph convolution. There is still room for improvement in simultaneously addressing multi-graph convolution, optimizing graphs, and simulating road conditions. Based on this, this paper proposes MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction. This method overcomes the aforementioned issues by dividing the process into different stages and achieves promising prediction results. In the first stage, we construct a latent similarity adjacency matrix and address the randomness interference features in similarity features through two optimizations using the proposed ConvGRU Attention Layer (CGAL module) and the Causal Similarity Capture Module (CSC module), which includes Granger causality tests. In the second stage, we mine the potential correlation between roads using the Correlation Completion Module (CC module) to create a global correlation adjacency matrix as a complement for potential correlations. In the third stage, we utilize the proposed Auto-LRU autoencoder to pre-train various weather features, encoding them into the model's prediction process to enhance its ability to simulate the real world and improve interpretability. Finally, in the fourth stage, we fuse these features and use a Bidirectional Gated Recurrent Unit (BiGRU) to model time dependencies, outputting the prediction results through a linear layer. Our model demonstrates a performance improvement of 29.33%, 27.03%, and 23.07% on three real-world datasets (PEMSD8, LOSLOOP, and SZAREA) compared to advanced baseline methods, and various ablation experiments validate the effectiveness of each stage and module. [ABSTRACT FROM AUTHOR]

Given the critical role of the clean energy market in the global economy and environmental sustainability, this paper investigates the impact of the U.S. Business Conditions Index (ADS) on the risk of segmented clean energy markets across different time scales and market conditions, as well as its spillover mechanisms. By using wavelet coherence and wavelet quantile analysis, we examine how the Aruoba–Diebold–Scotti (ADS) Business Conditions Index affects the risk levels of segmented clean energy indices under varying market conditions. To further understand this impact mechanism, we also employ the quantile Granger causality test to analyze the spillover effects of ADS on the clean energy market. The results show that the ADS index significantly influences the risk levels of segmented clean energy markets, with notable differences across various time scales and market conditions. The contributions of this study include: (1) segmenting the measurement of clean energy market risk into the Solar Index (SOLAR), Renewable Energy Index (RE), Biomass Index (BIO), Wind Energy Index (WIND), and Clean Energy Index (WILDER); (2) providing new evidence on the impact of the ADS Business Conditions Index on segmented clean energy market risk; and (3) offering new perspectives for different clean energy market participants to better navigate complex business environments and develop effective risk management strategies. [ABSTRACT FROM AUTHOR]

China has taken sustainable development strategies seriously in recent years, aiming at reducing energy consumption and environmental pollutants emissions. This research empirically evaluates the sustainable innovation efficiency (SIE) in Chinafrom the perspective of energy and environmental constraints. Furthermore, the relationship between SIE and economic growth is tested through Granger causality test. The results indicate that SIE in China varies obviously in different regions. Granger causality runs only from economic growth to SIE and not the other way round. Economic growth is causative factor of sustainable innovation, indicating that China's sustainable innovation has not yet achieved coordinated development with the economy. Our findings also provide useful decision-supporting insights for Chinese policymakers to promote coordinated development of regional sustainable innovation and economic growth. [ABSTRACT FROM AUTHOR]

This study examines the relationship between Poland's stock prices and three key macroeconomic factors: GDP growth, inflation, and interest rates. Using econometric tools like Granger causality tests, Vector Autoregression (VAR), and Impulse Response Functions (IRF), I assessed how these factors influence the Warsaw Stock Exchange (WIG Index) using quarterly data (2005-2024). The results showed a significant negative connection between interest rates, inflation, and stock prices: a 1% rise in interest rates leads to a 0.85% drop in stock prices in the following quarter. Inflation also negatively affects stock prices, with a 1% increase leading to a 0.40% decrease. Conversely, GDP growth positively impacts stock prices, where a 1% rise in GDP results in a 0.6% increase. The Granger causality test underscores the importance of monetary policy, showing that interest rate changes may predict stock market movements. Inflation is important; however, it is less predictive than GDP growth, which has a moderate positive impact on stock prices. These findings highlight the sensitivity of Poland's stock market to macroeconomic changes, offering valuable insights for analysts, investors, and policymakers. Future research could include additional factors like exchange rates or international trade to deepen the analysis. [ABSTRACT FROM AUTHOR]

Accurate prediction of the future evolution of observational time series is a paramount challenge in current data‐driven research. While existing techniques struggle to learn useful representations from the temporal correlations, the high dimensionality in spatial domain is always considered as obstacle, leading to the curse of dimensionality and excessive resource consumption. This work designs a novel structure‐aware reservoir computing aiming at enhancing the predictability of coupled time series, by incorporating their historical dynamics as well as structural information. Paralleled reservoir computers with redesigned mixing inputs based on spatial relationships are implemented to cope with the multiple time series, whose core idea originates from the principle of the celebrated Granger causality. Representative numerical simulations and comparisons demonstrate the superior performance of the approach over the traditional ones. This work provides valuable insights into deeply mining both temporal and spatial information to enhance the representation learning of data in various machine learning techniques. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sustainable development is a global imperative; however, it is unattainable without a transformation of the production sector. Therefore, the study examines the impact of green strategies [Industrial innovation, Eco‐tech imports‐IMPT, and Green finances‐GFN] and policy interventions [Environmental policy‐EPY and Environmental taxes‐ETX] on production‐based emissions (PBE), industrial value addition (IVA), and sustainable development goals (SDG). The study covers the years 1990–2022 and focuses on 25 European nations. In addition, the pooled mean grouped (PMG)‐ARDL and mean group (MG) techniques were used to assess the short‐ and long‐term parameters. The country‐specific relationships are evaluated using the Bootstrap Granger Causality test. The results affirm that green strategies and policy interventions mitigated PBE and increased the IVA and SDG in Europe. However, globalization and energy use increase the PBE and IVA while impeding progress toward the SDGs. The mitigation technologies fueled with green energy (MPRE) came out as the ultimate solution, which requires green technologies and sustainable practices across all sectors. Therefore, it is suggested that governments implement eco‐friendly policies that boost GFN, reduce pollution, increase green output, and meet the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

An economy's primary objective is to foster high economic growth, ultimately enhancing the well-being of its citizens. Recent economic strategies have increasingly emphasized trade-oriented policies to achieve this goal. This study delves into the nexus between Saudi seaborne trade (measured in tons) and gross domestic product (GDP), aiming to determine any causal relationship between these economic variables. Using datasets from the Saudi Central Bank and the General Authority of Statistics, this research employs rigorous unit root, cointegration, and causality tests to find the intricate interplay between trade and economic growth in Saudi Arabia. The empirical analysis highlights a robust long-term relationship between Saudi GDP and seaborne trade, suggesting cointegration. Specifically, the findings reveal a significant impact of economic growth on seaborne trade, as indicated by a trade elasticity of 1.08, which implies that changes in GDP yield a substantial shift in trade. For instance, a 1% increase in GDP corresponds to an approximate 1.08% increase in trade volume. The error correction mechanism also illustrates the dynamic adjustment process: deviations of Saudi seaborne trade and economic growth from equilibrium are corrected by 61% and 43%, in the subsequent year. Furthermore, Granger causality tests unveil bidirectional causality between GDP and trade, elucidating that economic growth changes precede trade volume alterations and vice versa. Thus, fostering international trade not only stimulates income but also bolsters the domestic economy of Saudi Arabia. [ABSTRACT FROM AUTHOR]

Qatar has experienced a remarkable surge in all four components of gross domestic product (GDP)—consumption, government spending, investment, and net exports as well as significant growth in population over the past thirty years. Despite a potential link between increasing production and consumption and a corresponding rise in CO2 emissions in Qatar, there exists a significant gap in the literature addressing this linkage. Using sectoral data, this article investigates the long-term trends of GDP growth and expenditure and their nexus with CO2 emissions in Qatar. Least square regression analysis (multiple regression approach) and Granger Causality tests have been used to examine the long-term association between GDP-expenditure and its components and CO2 emission patterns. Additionally, the study discusses the environmental implications and the necessity for effective natural resource management in climate policy. Our analysis reveals consistent average annual increases in Qatar over the past three decades: 4.8% in total expenditure, 5.4% in GDP, 4% in government spending, 4.7% in household consumption, and 1.77% in CO2 emissions. During this period, production- and consumption-based emissions surged by 700%, while consumption-based emissions rose by 500%. The energy sector alone accounts for 86% of Qatar's total CO2 emissions. Significant Granger causality was found between CO2 emissions and various economic growth and GDP components, suggesting a potential inverted U-shaped Kuznets Curve, indicating a possible decline in CO2 emissions in future. However, achieving this decline in CO2 emissions in accordance with UN SDG Goal 12 requires additional policy measures focussed on carbon capture and storage, circular economy practices, carbon pricing, and complementary climate policies. Specific policy actions include establishing CO2 observatories, promoting clean energy initiatives, and engaging the private sector. [ABSTRACT FROM AUTHOR]

The difference in the results from previous studies on the impact of inflation on poverty, coupled with an increase in the money supply from direct investment, inspired this research. This study aimed to ascertain how poverty, inflation, and foreign investment affect consumption spending in Indonesia based on 1997-2021 time series data. This study used Vector Autoregression estimation, utilizing data sources from the World Bank. The data will be processed with econometric models. From the study’s findings, it may be inferred that overall consumption affects inflation, which can be seen from the Granger causality test that shows that this variable has a one-way causal relationship. The results of the same test also show that FDI has an effect on total consumption and conversely consumption has an effect on FDI. However, the poverty variable does not affect total consumption and vice versa, because according to the causality test, the variable obtains an insignificant probability value. However, the VECM results explain that poverty has an effect on total consumption, and that the effect of consumption on inflation is due to the larger t-statistic value and has a positive relationship. This implies that the rate of inflation will increase the lower the level of consumption. However, the Impulse Reason test shows that poverty has a negative trend, as does the FDI variable. [ABSTRACT FROM AUTHOR]

This paper investigates inflation dynamics in emerging markets. We apply a multi-country model (GVAR) and a time-varying Granger causality test (TVGC) to examine the drivers of inflation in Brazil, China, India, Indonesia, South Africa, and Türkiye over the period 1980Q1-2022Q4. The aim is to ascertain whether these drivers are locally or globally determined and vary over time. The GVAR model results indicate that local and global factors drive domestic inflation, collectively contributing to inflationary pressures in emerging countries. The TVGC results, on the other hand, provide strong evidence of the causality from local and global factors to domestic inflation. Local factors appear to have a stronger influence than global ones, suggesting that local dynamics play a more significant role in explaining inflation in these countries. Consequently, globalization and economic/trade integration, seen as tools to suppress inflation, can turn into factors that cause inflation in the face of a supply shock such as the one caused by the pandemic. [ABSTRACT FROM AUTHOR]

This research investigates how the USA's load capacity factor (LCF) has been impacted by trade openness, financial development, stock market capitalization, and industrialization over the period 1990–2022. This study also tests the "Load Capacity Curve (LCC)" hypothesis. Various unit root tests were conducted to determine the stationarity of the dataset, revealing that the variables are free from unit root problems and exhibit mixed orders of integration. The "Autoregressive Distributed Lag (ARDL)" bounds test confirmed co-integration among the variables. The results from the ARDL model validated the existence of the LCC hypothesis in the USA. The findings also demonstrated that industrialization is positively correlated with LCF, whereas financial development, stock market capitalization, and trade openness are negatively correlated with LCF. To ensure the robustness of the ARDL estimations, the study also employed several regressions, all of which confirmed the validity of the ARDL results. Additionally, pairwise Granger causality tests revealed unidirectional causal relationships between GDP and LCF, stock market capitalization and LCF, and industrialization and LCF, while no causal relationships were found between financial development and LCF and trade openness and LCF. The findings advocate that the USA should advance eco-friendly industrial practices to ensure environmental sustainability, alongside regulated financial markets and institutions that mandate the utilization of green investments. [ABSTRACT FROM AUTHOR]

Climate data collected through Internet of Things (IoT) devices often contain high‐dimensional, nonlinear, and auto‐correlated characteristics, and general causality analysis methods obtain quantitative causality analysis results between variables based on conditional independence tests or Granger causality, and so forth. However, it is difficult to capture dynamic properties between variables of temporal distribution, which can obtain information that cannot be obtained by the mean detection method. Therefore, this paper proposed a new causality analysis method based on Peter‐Clark (PC) algorithm and modified local Granger causality (MLGC) analysis method, called PC‐MLGC, to reveal the causal relationships between variables and explore the dynamic properties on temporal distribution. First, the PC algorithm is applied to compute the relevant variables of each variable. Then, the results obtained in the previous stage are fed into the modified local Granger causality analysis model to explore causalities between variables. Finally, combined with the quantitative causality analysis results, the dynamic characteristic curves between variables can be obtained, and the accuracy of the causal relationship between variables can be further verified. The effectiveness of the proposed method is further demonstrated by comparing it with standard Granger causality analysis and a two‐stage causal network learning method on one benchmark dataset and two real‐world datasets. [ABSTRACT FROM AUTHOR]

During the past several decades, the world has been putting strong emphasis on the role of environmental technology innovation which can reduce environmental degradation and promote sustainable development. This research sheds light on the impact of green technology or eco innovation on sustainable development for China. Additionally, the effects of other variables such as governance, information and communication technology, trade uncertainty and urban population are also analysed. The study employs Fully modified ordinary least squares (FMOLS) and Canonical Cointegration Regression (CCR) as the long run tests and Breitung and Candelon Spectral Granger-Causality as the time varying granger causality test. The results suggest that green innovation promotes sustainable development in the long run. The roles of governance quality and urbanization are also found to be positive while the role of ICT is found to be insignificant yet negative. The role of trade uncertainty is positive yet insignificant. Based on these results, the study calls for the private and public organizations and industries to invest in eco innovation. Different incentives from the government can also encourage private industries to invest in green innovation technologies. [ABSTRACT FROM AUTHOR]

Recent advancements in cryptography and digital assets have triggered a profound transformation, causing a reorganization of the financial sector. The growing popularity of cryptocurrencies as investment instruments has raised concerns regarding the influencer mentions on social media impact on their price dynamics. This study assesses the discourse surrounding major cryptocurrencies and tokens within the decentralized finance (DeFi) and masternode sectors, examining its influence on prices and trading volumes during the period from 2019 to 2021. The study verifies the alignment between social media communications concerning these assets and their intended objectives. The analysis utilizes KHCoder software to explore topics with the highest public engagement, revealing that DeFi users frequently exhibit herd-like behavior by actively pursuing high-return farming trends, often at the expense of unique project attributes. In contrast, discourse surrounding masternode tokens more closely adheres to project objectives. The study employs panel models and Granger causality tests to investigate the relationship between cryptocurrency market performance, interactions on social media, and tweet sentiment. The findings substantiate a causal and temporal connection between micro-discourses on Twitter and asset prices, as well as trading volumes. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Accounting & Finance Reviews / Muhasebe ve Finans İncelemeleri Dergisi is the property of Muhasebe & Finans Incelemeleri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We examine the effect of options trading on an optioned firm's investment decisions. We find that active options trading improves an optioned firm's investment efficiency, and that this effect holds under several alternative empirical specifications and identification strategies, including fixed‐effects models, different matching methods, an instrumental variable approach, a Granger causality test and a quasi‐natural experiment based on the listing decisions of the options exchanges. This relation is mediated by two factors, namely, information asymmetry and uncertainty, consistent with the notion that options trading improves investment efficiency by providing information that facilitates external monitoring and managerial learning. The results of cross‐sectional analyses indicate that the effect of options trading on investment efficiency increases with firms' tendency to overinvest or underinvest, and with managers' risk‐taking and learning incentives. We also demonstrate that the effect of options markets on investment efficiency is distinct from the effect of stock markets. Overall, our findings suggest that options trading plays a nonnegligible role in improving an optioned firm's investment efficiency. [ABSTRACT FROM AUTHOR]

Domestic and Foreign institutional investors are professional investors who aim to profit from price movements in the stock market. These investors not only profit from their investments but also drive the stock market of any country. The study examines the association between Sensex Nifty 50’s movements and investing patterns of FIIs, DIIS and the causal relationship between stock market returns and institutional investments (FIIS and DIIs). The study also analyses the impact of FIIs and DIIs in driving the Indian stock market. The study employed correlation analysis, vector autoregression model, Block exogeneity Wald-test and the Granger causality test for the objectives. Our results indicate the gradual fall in the FIIs-DIIs ratio in the Indian stock market, making domestic institutional investors a prominent counterforce to FIIs and establishing it as an equal driver. DIIs act as positive feedback traders, while FIIs act as neutral to negative traders. The causality test demonstrates that market return has a significant impact on both the players, but neither FIIs nor DIIs have a significant impact on market movement [ABSTRACT FROM AUTHOR]

This paper proposes a panel Granger causality approach for heterogeneous panels with cross-sectional dependence. We define a panel VAR model with unobserved common factors and apply the PANIC procedure to obtain the de-factored data. We then estimate the lag augmented (LA)-VAR model for each cross section and construct the panel statistics based on the meta-analytic approach that combines the p-values of the individual statistics. The Monte Carlo simulations indicate that the combination tests show good size and power properties and appear suitable for the panels where cross sections may have different unit root or co-integration properties. We finally re-investigate Granger causality between export and economic growth in OECD countries. The results shed light on the importance of accounting for cross-sectional dependence within a factor model framework in determining direction of Granger causality for country-specific analysis. The results further reveal that export and economic growth do not cause each other in the majority of the European Union countries. [ABSTRACT FROM AUTHOR]

This study examines the causality relationship between portfolio investments and credit default swaps (CDS) in Türkiye. Analysing the dynamics between portfolio investments and CDS premiums, two important variables for financial markets is critical to understanding how risk perception and investment decisions are affected. While portfolio investments are generally considered an indicator of the confidence of foreign investors in the country's economy, CDS premiums are an important risk measure that reflects the country's debt risk and the risk perception of market participants. In this context, examining the relationships between the two variables contributes to the understanding of the effects of investor behavior and risk perception on macroeconomic indicators in financial markets. In the study, the Granger causality test was applied using data from the period 2014Q1-2024Q1. The results obtained show that CDS premiums have a significant and unidirectional causal effect on portfolio investments. Increases in CDS premiums increase investors' risk perception and lead to a decrease in portfolio investments. On the other hand, no causal effect of portfolio investments on CDS premiums was found. These findings emphasize the importance of risk management in terms of portfolio investments in Türkiye and reveal that CDS premiums play a role in investor decisions.

Incompatible with the pioneering role of the transportation sector in greenhouse gas (GHG) emissions, this research mainly deals with GHG emissions from the transport sector. In this context, the study looks at the four leading European Union (EU) countries (i.e., Germany, France, Italy, and Spain), investigates emissions from their sub‐types of the transport sector (i.e., aviation, rail, and road), uses environmental transport taxes (ETAX) as the main explanatory variable in the bivariate modeling, and considers biofuel consumption (BIO) as a control factor in the multivariate modeling. The study uses monthly data between 2004 and 2022, bivariate and multivariate quantile‐on‐quantile regression as well, and Granger causality‐in‐quantiles as the main approaches. The results demonstrate that (i) ETAX is completely inefficient on aviation and road transport GHG emissions; (ii) ETAX curbs rail transport GHG emissions in France at only middle quantiles; (iii) with the moderating impact of BIO, ETAX becomes effective in declining aviation, rail, and road transport GHG emissions (iv) BIO has a certainly reversing impact on the nexus between ETAX and transport sector GHG emissions. Overall, the study highlights that ETAX has a stand‐alone ineffective impact in curbing transport sector GHG emissions, but BIO has a certain moderating contribution in this manner. Thus, the study strongly recommends taking further actions, such as more use of BIO as in the case of this study, to support ETAX practices in combating GHG emissions for the EU countries. [ABSTRACT FROM AUTHOR]

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the term "behavioural fatigue" became the centre of policy debates in Great Britain. These debates involved deciding when to go into lockdown and whether behavioural interventions could be effective. Behavioural interventions can only succeed where people's Capabilities, Opportunities, and Motivations to perform target behaviours are supported. Our retrospective data analyses examine the relationships between people's Capabilities, Opportunities, Motivations, and Behaviours, i.e., adherence to lockdown guidelines. Our cross-sectional analyses include 17,962 unique participants in Great Britain who completed a survey over the initial 30 days of the first lockdown (April 2020). We examine trends in responses to each scale and then the relationships between the scales using Granger's causality test with tests for stationarity and cointegration. A mixture of increasing and decreasing trends was identified for Capabilities and Opportunities. Decreasing trends were identified for Motivation and Behaviour. Granger's causality tests found that Capability forecasts Opportunity and Behaviour and that Motivation forecasts Opportunity. The discussion reiterates that to realise and maintain Behaviour changes, policies surrounding people's Capabilities, Opportunities, and Motivations must continue to support target behaviours. [ABSTRACT FROM AUTHOR]

The Indian Central Himalaya Region (ICHR), the northern topographic front of the Indian summer monsoon (ISM), is an ideal location to study topography-climate interactions because two weather systems—the ISM and mid-latitude westerlies (MLW)—create distinct eco-climatic regimes from tundra (north) to tropical (south). The region's paleoclimatic studies show considerable climatic variations since the late Pleistocene. We evaluated 29 paleoclimatic records from the region and synthesized the results semi-quantitatively using the weighted palaeoclimate index (WApCI) to better understand the important climatic events and their driving mechanisms. According to the WApCI, the region has at least six enhanced monsoonal periods and eight drier spells during the past 34 ka. The cold-dry climatic events, such as the last glacial maxima (LGM), Younger Dryas (YD), 8.2 ka, and 4.2 ka, are associated with northern-hemisphere (NH) climate dynamics and propagated via MLWs. While, the warmer phases are dictated by the insolation-driven ISM dynamics. The WApCI's reconstructed rainfall anomaly aligns with paleoclimatic-model experiments for dynamically generated Paleoclimate Modeling Intercomparison Projects (PMIP3/PMIP4) rainfall for chosen time-slices (last-millenium, historical, mid-Holocene, and LGM). Finally, the Granger causality test determines the temporal relationship between the climatic drivers/forcing indices and primary meteorological parameters. The results showed that summer and post-monsoon precipitation is primarily influenced by total solar irradiation, winter precipitation is driven by a complex mix of variables, and pre-monsoon precipitation is driven by the Arctic oscillation. Based on the facts, we hypothesize that past climate variability demonstrates a complex interplay of local and hemisphere teleconnections in ICHR's climate dynamics. [ABSTRACT FROM AUTHOR]

Using the bootstrap rolling-window subsample Granger causality test from China, this study analyses the influence of technological progress (TP) on trade conflict (TC). The results show that TP can both promote and prevent TC. In 2012 and 2018, TP led to more trade conflicts between China and its trading partners. This result proves the "trade-loss effect", suggesting that TP in one country promotes TC by threatening other countries' income. However, TP had a negative influence on TC in 2021 and 2022. This finding is consistent with the "welfare effect", implying that TP can prevent TC by providing more high-quality and cheaper products for worldwide consumers. This study suggests that the government should adopt appropriate trade policies when encouraging TP to promote bilateral trade. Furthermore, firms should develop their own high-quality irreplaceable products through technological innovation to address TC risk. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic, multi-scale relationship between sentiment related to the COVID-19 pandemic and extreme returns in crude oil. The recently developed COVID-19 indices are employed to gauge pandemic sentiment. Utilizing daily data spanning from January 2020 to December 2021, Granger's linear and nonlinear causality tests reveal that indices nonlinearly influence extreme fluctuations in West Texas Intermediate and Brent crude oil prices. Interestingly, a reciprocal causation is also identified: extreme crude oil returns significantly affect the indices. Furthermore, the wavelet transform coherence analysis sheds light on the indices' ability to predict extreme crude oil price volatility across specific time-frequency domains, displaying diverse distributions and lead-lag patterns among the sub-indices. Our study underscores the efficacy of indices in anticipating extreme fluctuations in crude oil values during the COVID-19 pandemic, carrying important implications for investors, scholars, and policymakers. [ABSTRACT FROM AUTHOR]

Under the background of increasing economic policy uncertainty (EPU), this paper explores the time-varying impact of EPU on technical innovation (TI) by using the Granger causality test of the bootstrap rolling-window of full and sub-sample. We find the positive impact from EPU to TI indicates that a low EPU will stimulate TI. On the contrary, the positive effect of TI on EPU shows that the rapid development of technology has led to frequent changes in relevant policies, which will cause the rise of EPU. [ABSTRACT FROM AUTHOR]

This paper uses a two-regime Markov-switching GARCH model to illustrate that the state-switching of returns in the EU carbon market and its associated energy and stock markets is related to the occurrence of major events. Using the European economic policy uncertainty (EEPU) index as the proxy for the turbulence in the economic policies caused by major events, the Granger causality test shows that EEPU is associated with the three markets. To quantitatively analyze the impact of EEPU on the three markets, the GJRGARCH(1,1)-MIDAS model is constructed to solve the mixed frequency problem. The results indicate that EEPU has a negative effect on the long-term volatility of carbon and energy markets, but a positive effect on the stock market. The forecasting performance of the mixed frequency model considering EEPU is better than that of traditional GARCH models. Based on the decomposition of volatility in the mixed frequency model, the spillover index model is applied to analyze the spillover effects of the "carbon-energy-finance" system at three levels: total volatility, short-term volatility, and long-term volatility. The results indicate that at the level of long-term volatility affected by EEPU, the spillover effect of the system fluctuates more frequently and with greater intensity. The energy and stock markets play different roles at different levels of volatility, but the carbon market is always a risk receiver. The pairwise net spillover effects of the energy and stock markets on the carbon market are time-varying and related to the occurrence of major events. [ABSTRACT FROM AUTHOR]

Volatility reflects the degree of variation in a time series, and a measurement of the stock performance in the energy sector can help one understand the pattern of fluctuations within this industry, as well as the factors that influence it. One of these factors could be the COVID-19 pandemic, which led to extreme volatility within the stock market in several economic sectors. It is essential to understand this regime of volatility so that robust financial strategies can be adopted to handle it. This study used stock data from the Yahoo! Finance API and data from the energy-price database from the US Energy Information Administration to conduct a comparative analysis of the volatility in the energy sector in Brazil and in the United States, as well as of the energy prices in California. The volatility in these time series were modeled using GARCH. The stock volatility regimes, both before and after COVID-19, were identified with a Markov switching model; the spillover index between the energy markets in the USA and in Brazil was evaluated with the Diebold–Yilmaz index; and the causality between the energy stock price and the energy prices was measured with the Granger causality test. The findings of this study show that (i) the volatility regime introduced by COVID-19 is still prevalent in Brazil and in the USA, (ii) the changes in the energy market in the US affect the Brazilian market significantly more than the reverse, and (iii) there is a causality relationship between the energy stock markets and the energy prices in California. These results may assist in the achievement of effective regulation and economic planning, while also supporting better market interventions. Also, acknowledging the persistent COVID-19-induced volatility can help with developing strategies for future crisis resilience. [ABSTRACT FROM AUTHOR]

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups. [ABSTRACT FROM AUTHOR]

This study examined the effects of environmental sustainability and carbon emissions on post-harvest losses. Data used were collected from several databases such as World Bank Development Indicators (WDI), Food and Agriculture Organization Statistics (FAOSTAT), World Bank Doing Business and country policy and institutional assessment (CPIA) over the period of 2004 to 2022. The study applied the Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS) and Granger causality tests for the data analysis. The results showed that environmental sustainability reduces post-harvest losses, while carbon emissions increase post-harvest losses. The results suggest that a focus on environmental sustainability practices can help reduce post-harvest losses, while higher carbon emissions are associated with increased post-harvest losses. This underscores the importance of adopting environmentally friendly practices to enhance food security and reduce waste in the agricultural supply chain. [ABSTRACT FROM AUTHOR]

Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading volumes in Thailand through the VAR(2) model. Trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures are considered over the sample period from 31 October 2022 to 12 January 2024. The empirical results provide no evidence that the trading volume of EUR/USD futures is dependent on the past trading volumes of USD/JPY futures and USD/THB futures. The Granger causality test results show the existence of bidirectional causality between the trading volumes of USD/JPY futures and USD/THB futures. The results of the impulse response function are consistent with the sign results of the VAR(2) model, showing that the USD/JPY futures trading volume has a negative impact on the USD/THB futures trading volume, and vice versa. The analysis of variance decomposition shows that the variability of the USD/JPY futures trading volume and USD/THB futures trading volume, apart from its own shock, is explained by other FX futures trading volume shocks. Therefore, traders should pay more attention to new FX futures trading activity due to its negative impact on the USD/THB futures trading volume and its contribution to the variance in the USD/THB futures trading volume. Understanding the futures trading volume relationship also helps Thailand Futures Exchange develop new products and services that can foster market liquidity and stability. [ABSTRACT FROM AUTHOR]

Synopsis The research problem This study investigates whether the debt structure of a firm — that is, how a firm organizes its debt contracts — depends on the extent to which fair value measurement is applied to the balance sheet. Motivation Most public firms have a nontrivial percentage of balance sheet items measured at fair value under the mixed attribute accounting, and there is a lack of understanding of its effect on the reporting entity's debt structure. This study focuses on two aspects of the debt structure that have both practical and theoretical relevance, namely the priority structure and the financial flexibility. A priority structure involves the simultaneous use of different priorities of debt and is common among risky borrowers. Survey results show that financial flexibility is of first-order importance regarding the debt policy of firms. Here, financial flexibility refers to a firm's ability to pursue new investment opportunities via debt issuance. The test hypotheses The first hypothesis states that a borrower with a higher percentage of balance sheet items measured at fair value is less likely to have a priority structure. The second hypothesis states that a borrower with a higher portion of balance sheet items measured at fair value likely has a debt structure that is more financially flexible. The third hypothesis states that the fair value of liabilities and the fair value of assets are equally relevant to debt structure decisions. Target population This study focuses on nonfinancial firms in North America that are subject to the mixed attribute accounting, which measures a certain percentage of assets and liabilities at fair value. Adopted methodology The main tests use multivariate analysis that includes both logistic regressions and OLS regressions. Additional tests include cross-sectional analyses and the Granger causality test. Analysis Using a sample constructed from Compustat North America and Capital IQ (6,220 firms and 36,487 firm-year observations), the main tests regress dependent variables, namely the priority structure measures and financial flexibility, on the exposure to fair value measurement. Findings Results show that fair value measurement reduces information asymmetry and has favorable effects on the debt structure. A high exposure to fair value measurement reduces the need for a priority structure. A high exposure to fair value measurement also enhances financial flexibility. Additional analyses reveal that the favorable effect mainly comes from assets measured at fair value. The effect becomes weaker with respect to liabilities measured at fair value. [ABSTRACT FROM AUTHOR]

South Korea is considered one of the leading countries in innovation and research and development (R&D), in particular in renewable energy R&D efforts, but it is also one of the more polluting countries. So, understanding the interplay between these variables is of great interest. The main objective of this article is to examine the relationship between carbon dioxide (CO2) emissions, economic growth (GDP), renewable energy consumption (RE), foreign direct investment (FDI), home patents (HP), and foreign patents (FP) in South Korea using data ranging from 1980 to 2018. For this purpose, the autoregressive distributed lag (ARDL) approach and Granger causality tests were employed. Estimates of long-run elasticity point to a positive relationship between GDP and CO2 emissions. FP and RE have a negative influence on CO2 emissions but they have a favorable effect on GDP. FDI and HP have positive impacts on CO2 emissions. Granger causality outcomes demonstrate that CO2 emissions, GDP, RE, and FP have long-term bidirectional causal relationships. In the short-run, there are unidirectional causalities running from FP and FDI to RE and from FDI and HP to GDP. Besides, there is a bidirectional causality between GDP and RE. South Korea should continue to promote renewable energies and facilitate the use of foreign patents, particularly those relating to renewable energies. This will lead to a reduction in its carbon emissions while benefiting its economic growth. [ABSTRACT FROM AUTHOR]

This study examined South Africa's economic growth rate from 1980 to 2022 through an econometric analysis of fiscal and monetary policies. The study sought to investigate the relationships between the economy's growth rate and various fiscal and monetary policy variables, taking into account different economic approaches such as Keynesian, monetarist, and Wagner's perspectives. The methodology used consisted of data preparation, multiple unit root tests, Autoregressive Distributed Lag (ARDL) cointegration analysis, diagnostic tests, and pairwise Granger causality analysis. The empirical analysis found a long-term cointegration among the economic growth rate, government debt, expenditure, and revenue in fiscal policy, though government debt and expenditure were not statistically significant. Contrary to economic theory, increased government revenue had a negative correlation with economic growth. There was no long-term relationship found between the economic growth rate and monetary policy variables such as the official exchange rate, inflation rate, real interest rates, and M3 money supply. Pairwise Granger causality tests revealed a one-way relationship between government spending and economic growth, providing support to the Keynesian approach to fiscal policy. This study also discovered evidence that economic growth Granger-causes inflation, implying that economic growth may have predictive power for inflation, consistent with the demand-pull inflation hypothesis. However, no direct predictive relationships were found between the selected monetary policy variables and economic growth, supporting the long-run theory of monetary neutrality. This study suggests evaluating spending, managing inflation, implementing reforms, closing infrastructure gaps, encouraging investment, and ensuring fiscal sustainability. [ABSTRACT FROM AUTHOR]