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The purpose of this paper is to fill the momentous gap by explicitly investigating the asymmetric effects of monetary policy (MP) on non-bank financial intermediation (NBFI) assets.

The authors utilized panel data from 29 countries for the period of 2012–2020 and used the quantile regression estimation. In addition to simultaneous quantile regression (SQR), the authors also employ quantile regression with clustered data (Parente and Silva, 2016) and the generalized quantile regression (GQR) method (Powell, 2020).

The empirical results show a significant heterogeneous impact of MP. While there is a positive relationship between MP and NBFI assets (“waterbed effect”) at lower quantiles of NBFI assets, at middle and higher quantiles, MP has a negative impact on NBFI assets (“search for yield” effect). The authors further find that negative impact strengthens as the quantile levels of NBFI assets rise from mid to high. Findings also reveal that “procyclicality” (except higher quantile) and “institutional demand” hypotheses hold. However, regarding “regulatory arbitrage,” mixed results are observed indicating the impact of Basel III requirements.

Previous empirical studies have concentrated on either the Dynamic Stochastic General Equilibrium (DSGE) framework or conditional mean regression approaches and delivered mixed findings of the MP effects on NBFI. The current paper takes a step toward dealing with this issue by deploying quantile regression methodology, which shows the impact of MP on NBFI at different conditional distributions (quantiles) of NBFI assets instead of just NBFI's conditional mean distribution.

This paper aims to examine whether family business groups’ (FBG) having the same network auditor among their affiliates mitigates earnings manipulation (EM).

This paper used unbalanced panel data from the years 2010–2019. The sample of the study is composed of 327 nonfinancial listed Pakistan Stock Exchange firms, consisting of 187 FBG-affiliated firms and 140 nonaffiliated firms. The ordinary least square and generalized least square regressions have been used to check the hypothesized relationship. Furthermore, the propensity score matching technique is used to ascertain comparable companies’ features and to control the potential endogeneity problem. Finally, the results are robust to various measures of EM and FBG’ proxies.

The findings of the study show that the same network auditor is reducing EM in FBG affiliates. In addition, the BIG4 same network auditors are also instrumental in constraining EM as compared to non-BIG4 audit firms. Overall, the results of this study depict that the same network auditor in FBG’s affiliated firms significantly influences EM. These results are robust with respect to generalized least squares and the endogeneity problem.

This research study has two important implications for the interested parties. First, although the authors find in this research study that the same network auditor is negatively associated with EM in the FBG-affiliated firms, however, FBG-affiliated firms might use opportunistically the real activity manipulation. Second, regulators highlight the change in audit partner/firm rotation, though the study findings indicate that regulators and practitioners may consider the benefits associated with the same network auditors for FBG.

This research study adds a new investigation to previous literature by examining the role of the same network auditors in the EM of the FBG’ affiliates. To the best of the author’s knowledge, this is the first study to bring new knowledge by investigating the role played by the same network auditors along with the BIG4 same network audit firms in constraining EM in FBG.

This study aims to examine the effect of gender equality on financial stability and financial inclusion for 14 developing countries using yearly data from 2005 to 2021.

The two-stage least squares regression estimation and the generalized linear model regression estimation were used to investigate the effect of gender equality on financial stability and financial inclusion.

Gender equality has a significant positive effect on financial stability and financial inclusion in developing countries. Gender equality has a significant positive effect on financial stability and financial inclusion in African countries. Gender equality has a significant positive effect on financial stability but not on financial inclusion in non-African countries.

Little attention has been paid to the role of gender equality in promoting financial stability and financial inclusion. The authors address this issue in this study.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

The authors seek to advance the understanding of small- and medium-sized enterprise (SME) internationalization at the regional level and examine the role of home market institutions in this process.

The authors analyze hypotheses with data from SMEs in five country markets and from the Global Entrepreneurship Monitor. A cluster analysis establishes the regional diversification patterns (based on regional diversification scope, speed and rhythm) and a multinomial regression tests the effect of home market institutions on their adoption.

The results offer a refined picture of SME regional diversification by revealing three patterns: intra-regionally focused firms, late inter-region diversifiers and early inter-region diversifiers. They also suggest that the adoption of these patterns is determined by SMEs' home market institutions.

The authors develop a nuanced understanding of SME internationalization by building upon and expanding the regionalization rationale in the internationalization patterns literature. Additionally, the authors address the acknowledged, yet rarely investigated, country-level determinants of internationalization patterns.

The building sector is vital to a nation’s economy, as it has a major influence on economic activity and growth, job creations and the advancement of infrastructure. Intricate challenges that are inherent in crises such as the COVID-19 outbreak lead to material scarcities, project delays, labor shortages, escalated expenses, funding challenges, regulatory obstacles and dwindling investment funds, all of which culminate in costs that are in excess of those budgeted. While numerous studies have explored the ramifications of COVID-19 on project budgets, there is little, if any, data available on forecasting the magnitude of this impact.

This investigation seeks to bridge this knowledge deficiency by devising a predictive tool grounded in an ordinal logistic regression method. An online survey was designed and disseminated to gauge the views of construction field experts about the diverse contributors to excessive costs during the viral outbreak, and a predictive tool, crafted from the survey participants’ feedback.

Findings showed that smaller-scale enterprises and contractor-centric establishments faced greater adversities than medium-to-large ones and consultancy-or-owner-type entities.

The insights from this research shed light on the amplified risk of higher project costs amid health crises or analogous events, underlining the imperative need for fortified risk management approaches to bolster project outcomes. By factoring in demographics, this research offers policymakers a refined lens through which to customize interventions and promote balanced and enduring advancement in the construction industry.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

Corporate social responsibility (CSR) promotes society, reduces risk, and encourages ethical business practices. Due to its relevance, we study how CSR influences firms' sustainable development. We analyze data from 427 New York Stock Exchange (NYSE)-listed firms from 2008 to 2022. The Refinitiv environmental and social score is used to measure CSR, whereas for firms' sustainable development we rely on corporate sustainable growth rate (SGR) and market-based metrics. The analysis employs various econometric techniques, including ordinary least square, fixed effect regression, two-stage least square, generalized method of moment, and simultaneous quantile regression. The results indicate that CSR has a positive and significant effect on firms' sustainable development across all models. This relationship supports the notion that socially responsible business can contribute to long-term financial sustainability in line with “stakeholder theory”, indicating that companies should accommodate the concerns of various stakeholders, including society and the environment, to achieve sustainable development. We evaluate how the conditional distributions of SGR and firms’ value are affected by CSR, categorizing them into high, moderate, and low regimes. The quantile regression estimates indicate that the effect of CSR is more pronounced at upper quantiles, followed by moderate and low regimes. These findings underscore the importance of considering CSR in assessing the SGR and enterprises market value. We also confirm that our results are robust under range of different econometrics' methods. Finally, we enlighten current literature, and our research has useful policy implications for management and investors.

The primary goal of this paper is to investigate the relationship between stock market liquidity and firm dividend policy within a market implementing the tax imputation system. The main aim is to understand how the tax imputation system influences the relationship between firm dividend policy and stock market liquidity within a cross-sectional framework.

This paper investigates the relationship between stock market liquidity and the dividend payout policy under the full tax imputation system in the Australian market. This study uses the Generalized Least Squares regressions with firm- and year-fixed effects.

In contrast to the negative relationship between the liquidity of common shares and the firms' dividends documented in countries with the double tax system, the study reveals that in Australia, the dividend payout ratios are positively associated with liquidity after controlling for various explanatory variables with both the contemporaneous and lagged time periods. Such a finding is robust to the use of alternative liquidity proxies and to the sub-period tests and remains during the COVID-19 pandemic period.

The insights derived from this study have significant implications for various stakeholders within the economy. The findings provide regulators with valuable insights to conduct a more holistic assessment of how the tax system impacts the economy, especially concerning the dividend choices of firms. Within the context of a full tax imputation system, investors can make investment decisions without factoring in the taxation impact. Simultaneously, firms can be relieved of concerns about losing investors who prioritize liquidity, particularly when a high dividend payout might not align optimally with their financial strategy.

This study contributes to the literature by extending the literature on the tax clientele effects on dividend policy, providing evidence that the tax imputation system can moderate the impact of liquidity on dividend policy. This study examines the impact of the dividend tax imputation system on the substitution effect between dividends and liquidity.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.