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This paper aims to investigate the correlation between banking sector non-performing loans (NPLs) and the level of sustainable development.

Pearson correlation test statistic was used to assess the correlation between bank NPLs and sustainable development.

There is a significant positive correlation between banking sector NPLs and the level of sustainable development measured by the sustainable development index (SDI). The significant positive correlation is evident in European countries and in countries in the region of the Americas. There is a significant negative correlation between banking sector NPLs and achieving SDG3 and SDG7 in African countries and European countries. There is also a significant negative correlation between NPLs and achieving SDG10 in European countries. There is a significant positive correlation between banking sector NPLs and achieving SDG4 and SDG7 in the region of the Americas. There is also a significant positive correlation between NPLs and achieving SDG10 in African countries and in countries in the region of the Americas.

The present study is unique and different from other studies because it used a unique SDI to capture the level of sustainable development. The analysis is also unique because it covers several regions, which have not been covered in previous studies.

The purpose of the study is to investigate the correlation between credit supply to government and credit supply to the private sector to determine whether there is a crowding-out or crowding-in effect of credit supply to government on credit supply to the private sector.

The study used data from 43 countries during the 1980–2019 period. The study employed the Pearson correlation methodology to analyze the data.

There is a significant positive correlation between credit supply to government and credit supply to the private sector. There is also a significant positive relationship between credit supply to government and credit supply to the private sector, implying a crowding-in effect of government borrowing on private sector borrowing. The positive correlation between credit supply to government and credit supply to the private sector by banks is stronger and highly significant in the period before the Great Recession, while the positive correlation is weaker and less significant during the Great Recession, and the correlation further weakens after the Great Recession. The regional analyses show that the positive correlation between credit supply to government and credit supply to the private sector by banks is stronger and highly significant in the African region than in the Asian region and the region of the Americas.

There is no evidence on the correlation between credit supply to government and credit supply to the private sector during the Great Recession.

This paper examines the association between corporate governance and financial inclusion in terms of correlation. This paper examines whether countries that have a strong corporate governance environment also experience better financial inclusion outcomes.

The indicators of financial inclusion are automated teller machines (ATMs) per 100,000 adults, bank accounts per 1,000 adults and bank branches per 100,000 adults, while the indicators of corporate governance are extent of corporate transparency index, the extent of director liability index, the extent of disclosure index, the extent of ownership and control index, the extent of shareholder rights index, minority investors protection index and ease of shareholder suits index. The association was analyzed using Pearson correlation analysis and granger causality test.

Strong corporate governance is significantly associated or correlated with better financial inclusion outcomes. The regional analyses show that corporate governance has a significant positive association with financial inclusion in Asian countries and in Middle East countries. However, a positive and negative association was observed between some indicators of corporate governance and financial inclusion in European countries, North American countries, South American countries, African countries and in Middle East and North Africa (MENA) countries, implying that strong corporate governance has a positive and negative association with financial inclusion depending on the indicators of corporate governance and financial inclusion used. There is also evidence of uni-directional granger causality between corporate governance and financial inclusion.

Little is known about the association between corporate governance and financial inclusion. This paper is the first to examine this association.

The link between supply chain agility (SCA) and performance has been tested in previous research with different samples and results. The present paper quantitatively analyses and summarises the impact of SCA on performance found in previous empirical papers and determines the influence of several identified moderators.

Using a meta-analysis approach based on a systematic literature review, a total of 63 empirical papers comprising a sample of 14,469 firms were meta-analysed to consider substantive (type of performance and SCA operationalisation) and extrinsic (economic region and industry) moderators.

Results confirm a significantly large, positive correlation between SCA and performance. None of the analysed moderators has enabled the identification of any significant differences between the SCA and performance correlations by subgroup. However, high heterogeneity in total variance, both in the full sample and the subgroups by moderator, demands further rigorously reported empirical research on this topic with clearly conceptualised variables and frameworks and the use of validated scales.

Several research gaps and best practice recommendations have been indicated to improve future empirical research on this topic.

Practitioners in different economic regions and industries will find consistent evidence of improvements in performance through SCA.

No meta-analysis has been found in previous research to estimate the value of the correlation between SCA and performance and the influence of moderating variables.

This study aims to conduct an empirical investigation to assess the hedge, diversifier and safe-haven properties of different environmental, social and governance (ESG) assets (i.e. green bonds and ESG equity index) vis-à-vis conventional investments (namely, equity index, gold and commodities).

The authors examine the sample period 2007–2021 using the bivariate cross-quantilogram (CQG) analysis and a dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity (GARCH) experiment with several extensions.

The evidence shows that the analyzed ESG investments exhibit mainly diversifying features depending on the asset class taken as a reference, with some potential hedging/safe-haven qualities (for the green bond) in peculiar timespans. Therefore, the results suggest that investors might consider sustainable investing as a new measure of risk reduction, which has interesting implications for both portfolio allocation and policy design.

To the best of the authors’ knowledge, this study is the first that empirically investigates at once the dependence between different ESG investments (i.e. equity and green bond) with different conventional investments such as gold, equity and commodity market indices over a large sample period (2007–2021). Well-suited methodologies like the bivariate CQG and the DCC multivariate GARCH are used to capture the spillover effect and the hedging/diversifying nature, even in temporary contexts. Finally, a global perspective is used.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

This research provides some evidence by the vine copula approach, suggesting the significant and symmetric causal relation between subsections of Baltic Exchange and hence concluding that investing in different indexes, which is currently a risk diversification system, is not a correct risk reduction strategy.

The daily observations of Baltic Capesize Index (BCI), Baltic Handysize Index (BHSI), Baltic Dirty Tanker Index (BDTI) and Baltic LNG Tanker Index (BLNG) over an eight-year period have been used. After collecting data, calculating the return and estimating the marginal distribution of return rates for each of the indexes applying asymmetric power generalized autoregressive conditional heteroskedasticity and autoregressive moving average (APGARCH-ARMA), and with the assumption of skew student's t-distribution, the dependence of Baltic indexes was modeled based on Vine-R structures.

A positive and symmetrical correlation was observed between the study groups. High and low tail dependence is observed between all four indexes. In other words, the sector business groups associated with each of these indexes react similarly to the extreme events of other groups. The BHSI has a pivotal role in examining the dependency structure of Baltic Exchange indexes. That is, in addition to the direct dependence of Baltic groups, the dependence of each group on the BHSI can transmit accidents and shocks to other groups.

Since the Baltic Exchange indexes are tradable, these findings have implications for portfolio design and hedging strategies for investors in shipping markets.

Vine copula structures proves the causal relationship between different Baltic Exchange indexes, which are derived from different types of markets.

This research mainly intends to ascertain the stimulus of investor investment tendencies on the amount of capital investment in the share market.

Utilizing a sample of 477 individual investors who actively trade on the Bangladesh capital market, this empirical study was conducted. The objective of this examination is to ascertain the investment trading behavior of retail investors in the Bangladesh capital market using multiple regression, hypothesis testing and correlation analysis.

The coefficients of market categories, preferred share price ranges and investment source reveal negative predictor correlations; all predictors are statistically significant, with the exception of investment source. Positive predictive correlations exist between investor category, financial literacy degree, investment duration, emotional tolerance level, risk consideration, investment monitoring activities, internal sentiment and correct investment selection. Except for risk consideration and investment monitoring activities, all components have statistically significant predictions. The quantity of capital invested in the stock market is heavily influenced by the investment duration, preferred share price ranges, investor type, emotional toleration level and decision-making accuracy level.

This investigation was conducted exclusively with Bangladeshi individual stockholders. Therefore, the existing study can be extended to institutional investors and conceivably to other divisions. It is possible to conduct this similar study internationally. And the query can enlarge with more sample size and use a more sophisticated econometric model. Despite that the outcomes of this study help the regulatory authorities to arrange more informative seminars and consciousness programs.

The conclusions have practical implications since they empower investors to modify their portfolios based on elements including share price ranges, investment horizons and emotional stability. To improve chances of success and reach financial objectives, they stress the significance of bettering financial understanding, active monitoring and risk analysis. Results can also be enhanced by distributing ownership over a number of market sectors and price points. The results highlight the value of patience and giving potential returns enough time.

This study on the trading behavior of investors in Bangladesh is unique and based on field study, and the findings of this study will deliver information to the stakeholders of the capital market regarding the investors’ trading behavior belonging to different categories, financial literacy level, investment duration, emotional tolerance level and internal feeling.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).