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The purpose of this paper is to determine cause and effect relationship between foreign direct investment (FDI) and economic growth (gross domestic product (GDP) taken as proxy) for Brazil, Russia, India, China and South Africa (BRICS nations) individually for the period 1992-2013. Also, the study tries to explore the reasons behind the linkage between FDI and GDP by estimating a linear regression model consisting of both macro-economic and institutional variables.

Johansen cointegration technique followed by vector error correction model (VECM) and standard Granger causality test are employed to investigate the causal linkage between FDI and GDP. To delve into the reasons behind this linkage, an ordinary least square (OLS) technique is also applied to test the linear regression model consisting of net FDI inflows as dependent variable and nine macro- economic and institutional variables. Residual diagnostics is also conducted using Breusch-Godfrey Lagrange Multiplier test for diagnosing the problem of serial correlation, Breusch-Pagan-Godfrey test for examining heteroskedasticity and Jarque Bera test for verifying the normality of residuals.

The Johansen cointegration result establishes a single cointegrating vector (long run relationship) between FDI and GDP for India, China and Brazil. After proving a cointegration, VECM results revealed that there exists unidirectional long run causality running from GDP to FDI in case of Brazil, India and China. Also, it is confirmed that there exists short run causality between FDI and GDP in China, i.e. the past lags of FDI jointly impact the value of GDP. However, for Russia and South Africa, where there is no cointegration in the long run, standard Granger causality test is conducted which reveals that in both the nations, FDI and GDP are independent of one another. The results of OLS technique reveal different country-specific factors causing this linkage between FDI inflows and economic growth.

Various researchers in the past have examined this issue of linkage between FDI and GDP in the context of various developing or developed nations. This reveals a gap in the existing literature pertaining to this causal linkage in the context of the BRICS. Thus, this study fills this gap by analyzing not just this causal nexus with the help of VECM and Granger causality techniques but also tries to explore further the reasons for such strong/weak/no link with the help of fitting a regression model which comprises of both macro-economic and institutional country-specific variables influencing this causation.

Bitcoin has emerged as a phenomenal asset earning abnormal profits. However, the factors with predictability power over its price are not widely studied. Therefore, this study aims to explore the factors that determine bitcoin prices. The analysis explores the determinants belonging to four categories – macro economic, financial, technical and fundamental factors.

The study employs random effects regression on the panel data of five countries. Then Granger causality test is applied on the time series of all the variables. Lastly, diagnostic tests are conducted to confirm the findings to be robust and reliable.

The findings suggest that oil price, bitcoin supply, trading volume and market capitalization significantly impact the price of bitcoin in the long run. In short run, bitcoin returns are only caused by oil price and market capitalization. Interestingly, bitcoin returns influence its attractiveness to investors, market capitalization, S&P 500 returns and trading volume, in the short run.

The technical analysis is found to be redundant in the short run. In the long run, technical as well as fundamental analysis are useful. The bitcoin is found to be a good diversification tool as it has no linkages with the stock markets and gold market. It is also an inflationary hedger owing its limited supply.

The studies on cryptocurrency market have not conducted the analysis across countries. This study captures the cross-sectional effects along with time effects. The study also includes 17 variables belonging to four categories.

The purpose of this work is to study the connections generated between stock market indices, representing firms whose practices focus on fighting climate change and several global risk factors in accordance with the sustainability objectives defined in the 2030 Agenda. An endogenous perspective is adopted, considering the spillovers generated within the low carbon stock market sector, as well as the latter’s exposure to exogenous shocks of an economic and financial nature.

This work uses a multivariate model of dynamic correlation (GARCH-corrected dynamic conditional correlation [cDCC]), which can accompany the correlations generated over time.

Considering five low carbon indices, representing various parts of the world, and four global macro-economic and financial variables, over a period of approximately eight years, it was possible to understand that the variables studied transmit between each other a statistically significant spillover. The period of the pandemic crisis shows a sharp increase in the information transmission process. It was also possible to conclude that some global variables are risk factors, performing the role of transmission channels for the spillover effects to low carbon indices, increasing the risk of contagion and reducing the possibilities of diversifying the investment portfolio.

Firstly, this work analyses the connection and spillover effects between low carbon indices. Secondly, considers an extended sample covering different market phases, particularly that of the pandemic crisis and the Ukrainian War, creating conditions to compare connection patterns between those indices. Thirdly, it studies the variable influence over time of global risk factors in the transmission of spillover between low carbon indices.

Research on job precarity and job instability have largely neglected the labor market trajectories in which these employment and non-employment situations are experienced. This study addresses the mechanisms of volatility and precarity in observed work histories of labor market entrants using the National Longitudinal Survey of Youth of 1997. Several ideal-typical post-education pathways are modeled for respondents entering the labor force between 1997 and 2010, with varying indicators and degrees of precarity. A series of predictive models indicate that women, racial-ethnic minorities, and lower social class labor market entrants are significantly more likely to be exposed to the most precarious early careers. Moreover, leaving the educational system with a completed associate’s, bachelor’s, or post-graduate degree is protective of experiencing the most unstable types of career pattern. While adjusting for these individual-level background and education variables, the findings also reveal a form of “scarring” as regional unemployment level is a significant macro-economic predictor of experiencing a more hostile and turbulent early career. These pathways lead to considerable earnings penalties 5 years after labor market entry.

The purpose of this paper is to inspect whether bank‐specific and macro‐economic determinants influence Islamic banks' profitability in the selected countries of different regions.

In order to achieve the study objective and to answer the question, the balanced panel data regression model has been used. Bank level data is used and this study examines the alternative measures ROA and ROE as a bank‐specific function and macro‐economic determinants.

The study results signify that banks with larger assets size and with efficient management lead to greater return on assets.

The paper shows that management efficiency regarding operating expenses positively and significantly affects the banks' profitability.

The aim of this study is to empirically examine firm-specific factors that influence the financing decisions of companies listed on BSE-500 index. Firm-specific variables such as profitability, company size, growth potential, liquidity, non-debt tax shields, age and tangibility were evaluated in this study.

This empirical research is performed using longitudinal data of 366 companies listed on the BSE 500 index during 2006–2020. Pooled ordinary least square method is employed to classify primary determinants of capital structure.

The results show that profitability, liquidity and non-debt tax shield are negatively associated whereas, company size, growth potential, age and tangibility are positively associated with the capital structure. The authors’ observations are aligned with either the trade-off hypothesis or the principle of the pecking order.

This study helps to better understand how firm-specific factors play a vital part in deciding the capital structure of businesses and makes a significant contribution to the literature. Thus, the present study examines the drivers of the capital structure among sample Indian companies, which allow firm managers and regulators to recognise relevant variables that optimise performance. This study is limited to Indian companies and only firm-specific variables were considered.

The current research focuses on the impact of firm-specific variables upon the financing decisions of Indian companies. In the background of developed countries, numerous studies in this field have been carried out. In the Indian context, however, there are not many researches in this area. However, the existing studies use one or two ordinary least square (OLS) models, resulting in a lack of thorough research and robust results. To address this gap in the analysis, the current study used four models and used a 15-year time frame, as well as a bigger sample size, which was not used in earlier investigations.

The purpose of this paper is to show how dynamic regression models based on equilibrium correction principles can be used to form auditor expectations of account balances as part of the analytic review.

The design and method are empirical, using the automated econometric software of PcGets and annual data of the Toyota Company over the period 1950‐2004 to generate forecasts of sales and earnings.

Automated equilibrium correction models (AECMs) are shown to possess stable parameters and provide reliable one year ahead forecasts of sales based on macro‐economic data. AECMs are then used to generate indicative earnings forecasts conditional upon sales as an expectation generating tool for directing auditors' attention to possible sources of error in financial statements.

Analysis is illustrative of a general method and does not provide exhaustive treatment of the full range of potential application of AECMs.

Until recently, econometric problems have made the use of dynamic regression models in auditing difficult for non‐specialists to implement. Developments in automated software packages such as PcGets now make the use of such procedures by audit practitioners possible.

Relatively little is known about dynamic regression models in the accounting and auditing literature. This paper introduces the basic concepts underpinning AECMs and demonstrates their potential to contribute to the analytic review toolkit of the auditor.

The purpose of this paper is to develop a better understanding of the debt structuring of project finance (PF) loans and the main drivers affecting the maturity of bank loans in infrastructure deals. When banks grant loans to a project, they have two decision variables: the interest margin or the spread and the maturity of the loan. Although several studies analyze the drivers of the spread, few studies in the literature look at the maturity of bank loans. As infrastructure projects are typically highly leveraged, the structuring of bank lending is an important parameter in the financial viability of the project.

The paper develops a regression analysis of the loan’s maturity on four categories: characteristics of the project, political risk of the country where the project is executed, the macro-economic setting and the regulatory framework. By using a new data set of InfraDeals containing data on bank loans of more than 1,800 infrastructure projects worldwide from 1997 to 2016, this paper reveals new insights on the debt structuring of banks for PF loans.

The results indicate that the maturity of bank loans granted to infrastructure deals is predominantly driven by political risk and regulation, rather than the structuring of the project. This implicates that the region where the deal is closed weighs more heavily than the specificities of the project itself.

The results have important policy implications. The paper allows to develop a better understanding on how political risk and new regulation, like Basel III, might affect the PF market. The paper is the first one finding empirical evidence of the impact of Basel III regulation on PF lending. By delving deeper into the political risk variable, the authors formulate several recommendations to mitigate political risk.

Introduction – The banking sector is one of the most important building blocks of the financial system. A failure in the banking sector can cause serious problems in a country’s economy. In order for countries to achieve economic growth and development goals, the banking sector, which affects all sectors significantly, needs to be strong. Countries with a robust and reliable banking system have a high credit rating. As a result of this high credit rating, the interest of foreign capital in the country increases. Thus, the credit volume of banks expands and loans are provided at a more appropriate rate for investments. In this respect, the performance and profitability of banks are important. The CAMELS performance model is a valuation system used to determine the general status of banks. The CAMELS model consists of six components. According to this, C represents capital adequacy; A, asset quality; M, management adequacy; E, earnings; L, liquidity; and S, sensitivity to market risks.

Purpose – The purpose of this study is to demonstrate the effect of the CAMLS variables on the variable E.

Methodology – In the implementation part of the study, the data of 11 banks in the BIST Bank Index between 2004 and 2018 were used. In the analysis part of the study, a panel data analysis method was used.

Findings – The capital adequacy (C), management adequacy (M) and liquidity (L) variables were effective on profitability. This study revealed the importance of the capital, management and liquidity variables, which are internal factors, in increasing the profitability of banks.