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The purpose of this paper is to evaluate the importance of financial liquidity and solvency on US commercial and domestic banks’ cost efficiency while accounting for internal and external factors.

The Stochastic Frontier Analysis and Data Envelopment Analysis estimators are used to estimate the cost efficiency of 11,044  US commercial and domestic banks from 2005 to 2017. Using Tobit regression model, the importance of financial liquidity and solvency on cost efficiency is examined.

The results provide evidence that the financial liquidity and solvency negatively impact the cost efficiency of US commercial and domestic banks. Overall, US commercial and domestic banks were inefficient during the financial crisis in comparison to the tranquil period. The importance of financial solvency on the cost efficiency was not statistically significant, while the financial liquidity negatively collapsed because of contagion. Finally, the results provide evidence that the amount of total assets matters in the improvement of the cost efficiency.

This paper estimates and identifies the 2007-2009 financial crisis with liquidity, solvency or both financial factors.

The purpose of this study of M&A in relation to sustainability aims to create synergies that drive both financial performance and positive environmental and social outcomes, aligning business objectives with broader societal goals. The study can help in understanding the structure, collaborations and recent keywords and topics that are related to M&A in relation to sustainability.

This study applies comprehensive bibliometric analysis on performance analysis and science mapping of the scholarly literature related to M&A in relation to sustainability published between 2004 and 2024 in 196 publications on Web of science and Scopus by leveraging biblioshiny within R Studio and the VOS viewer software for enhanced visualization and interpretation of the bibliometric data.

This study helps to find out the co-occurrence analysis, bibliographical coupling and keyword analysis. Moreover, it scrutinizes patterns of collaboration among researchers, institutions and nations, shedding light on the global reach and influence of scholarly contributions in the field of M&A in relation to sustainability.

This study identified trends can help scholars, policymakers and business experts to study M&A through the lens of sustainability.

This bibliometric study of M&A in relation to sustainability can drive positive change by promoting responsible business practices, fostering innovation and creating value for both shareholders and society as a whole.

The paper concludes by finding emerging topics of the research and future directions, it gives a good input to extend the line on the graph. This study provides overview of 20 years of research on M&A in relation to sustainability and discusses its findings to identify the research gap.

This paper aims to analyse the weekday effect on the Nifty Shariah indices as per the Islamic calendar. The study is intended to know about the return and volatility of these indices during Ramadhan and non-Ramadhan days.

The study focuses on analysing the Nifty Shariah indices and Sensex daily returns collected from NSE India and BSE India, respectively, during the period of 1 August 2016 to 31 July 2022. Descriptive statistics are used to analyse the data, while the Ordinary Least Square method is used to determine the impact of weekdays on the Nifty Shariah indices. Additionally, the study applies the GARCH statistical model to examine the influence of Ramadhan on the returns and volatility of the Nifty Shariah indices.

All of the Nifty Shariah indices produced positive returns during the overall sample period. According to the study, Tuesday index returns outperform other weekdays. The GARCH model indicated that the coefficient values for the Nifty 50 Shariah and Nifty 500 Shariah indices were negative. Ramadhan has a strong negative effect on volatility, according to this study.

The outcomes of the research are beneficial for investors aiming to exploit daily or weekly price fluctuations, rather than pursuing extended investment periods. Furthermore, fund managers can employ these findings to shape trading strategies, and academics can examine the performance of Shariah indices in the Indian context. This enables devout investors to make significant financial choices, thus advancing ethical values in society and upholding standards of both public and private morality.

The world of investing has changed drastically. Investors are willing to invest the companies that give high priority to environmental, social and governance issues (ESG). This study delves into the performance of the BSE CARBONEX index in comparison to the BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas. It seeks to examine the impact of calendar anomalies, particularly focusing on the day-of-the-week effect, on these indices. To accomplish this, daily closing prices of the BSE CARBONEX, BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas were gathered from the BSE official website. The study period was divided into three segments: the full period, period I (2017–2020) and period II (2020–2022). The study's findings reveal that throughout the full period, period I and period II, BSE Energy exhibited the highest mean daily return compared to the other selected indices. There appears to be a discernible Tuesday effect on the daily average mean returns of BSE CARBONEX, BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas in both the full sample period and period II. Results from ordinary least squares (OLS) analysis by day indicate a notably high positive and statistically significant daily return on Tuesdays, particularly during the full sample period and period II. Furthermore, the GARCH (1,1) model suggests a significant Tuesday effect on the BSE Energy and BSE Oil & Gas indices.

In this chapter, we utilize the residual concept of productivity measures defined in the context of normal-gamma stochastic frontier production model with heterogeneity to differentiate productivity and inefficiency measures. In particular, three alternative two-way random effects panel estimators of normal-gamma stochastic frontier model are proposed using simulated maximum likelihood estimation techniques. For the three alternative panel estimators, we use a generalized least squares procedure involving the estimation of variance components in the first stage and estimated variance–covariance matrix to transform the data. Empirical estimates indicate difference in the parameter coefficients of gamma distribution, production function, and heterogeneity function variables between pooled and the two alternative panel estimators. The difference between pooled and panel model suggests the need to account for spatial, temporal, and within residual variations as in Swamy–Arora estimator, and within residual variation in Amemiya estimator with panel framework. Finally, results from this study indicate that short- and long-run variations in financial exposure (solvency, liquidity, and efficiency) play an important role in explaining the variance of inefficiency and productivity.

The consistency of stochastic frontier analysis (SFA) and data envelopment analysis (DEA) cost efficiency measures using a sample of 650 commercial and domestic banks in the United States is investigated based on cluster analysis while accounting for the yearly variation in banks.

Due to the importance of efficiency measures for policy and managerial decision-making, the cost efficiency measures of SFA and DEA estimators are examined according to four criteria: levels, rankings, stability over time and stability over clustering groups. In this paper, we present two clustering methods, Gap Statistic and Dindex, that involve SFA and DEA cost efficiency measures. The clustering approach creates homogeneous groups of banks offering a similar mix of efficiency levels. Hence, each evaluated bank knows the cluster to which it belongs. Furthermore, this paper provides nonparametric statistical tests of SFA and DEA cost efficiency measures estimated with and without a clustering approach.

The results suggest that the clustering approach plays a considerable role in the rankings of US banks. Furthermore, the average SFA and DEA cost efficiency measures over time of the homogeneous US banks are substantially higher than those of the heterogeneous US banks.

This research is the first to provide comparative efficiency measures needed for desirable policy conclusions of heterogeneous and homogeneous US banks.

This paper evaluates the importance of both liquidity and solvency risk factors on variations in efficiency measures of US commercial and domestic banks from 2005 to 2017.

The analysis is conducted using the true random effect stochastic cost model to examine the role of both liquidity and solvency risk factors. To this end, the author uses the exponential stochastic cost function and adds new variables, including bank size, crisis as an indicator of the financial crisis and the Dodd–Frank Act and Basel II Accord as regulatory dummies.

The results show that both liquidity and solvency risk factors positively affect the variance of cost inefficiency measures and thus have negative impact on the cost efficiency measures. In addition, banks increased the cost of financial intermediation during the financial crisis, whereas regulatory factors of Basel II Accord and Dodd–Frank Act play a crucial role in explaining the cost efficiency measures.

These results are the first to quantify the impact of both liquidity and solvency on the variations in cost efficiency measures.

Long-term forecasts about commodity market indicators play an important role in informing policy and investment decisions by governments and market participants. Our study examines whether the accuracy of the multi-step forecasts can be improved using deep learning methods.

We first formulate a supervised learning problem and set benchmarks for forecast accuracy using traditional econometric models. We then train a set of deep neural networks and measure their performance against the benchmark.

We find that while the United States Department of Agriculture (USDA) baseline projections perform better for shorter forecast horizons, the performance of the deep neural networks improves for longer horizons. The findings may inform future revisions of the forecasting process.

This study demonstrates an application of deep learning methods to multi-horizon forecasts of agri-cultural commodities, which is a departure from the current methods used in producing these types of forecasts.