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This research study examines the impact of social and governance initiatives on financial performance of global banks. The study is significant in the context of massive changes in regulations, government policy, social attitudes and market development attributed to banking sector.

The source of data for this study was ESG database of Thomson Reuters. The study was based on 472 global banks. The research paper uses two-stage least square model and the study covered the five-year period 2015–2019.

Banks with high intensity of social and governance-related activities have positive market-based valuation effects. Adequately capitalized banks tend to invest more in social initiatives. Banks' governance initiatives directed toward the use of anti-takeover defensive mechanisms are skeptically perceived by markets. Riskier banks tend to have less investments in social initiatives.

The findings are relevant in the context of expectations from policymakers, consumers and investors with respect to the role which banks ought to play in funding the development of a sustainable economy. The research finding that strong governance and social initiatives by banks are value-enhancing measures is a clear evidence of the significance of ESG initiatives as value-creating mechanisms as perceived by markets.

This study addresses the gap in the research, which examines the role of governance and social initiatives on value creation in the banking sector firms. The study examines the impact of different elements of governance and social initiatives on financial performance of banks.

Recent studies of the impact of financial liberalization in emerging markets have not examined the dynamic impact of the liberalization process on equity returns despite the important implications on ongoing reform policies. We analyze six Asian equity markets using a dynamic adjustment model with three independent variables: market capitalization value, pricebook value ratio, and price‐earnings ratio. We use panel data for the period 1991‐2000 and the LSDVR (least square dummy variable regression) approach to identify the timing effects of liberalization. The stability of the model is also tested. The results indicate, in most cases, the significance of all three variables and the timing effects. Evidence of significant structural changes is also supported.

This study aims to investigate the role of corporate governance practices in restraining earnings management in Indian commercial banks.

Estimation of earnings management is based on discretionary loan loss provision and discretionary realised security gains and losses using Beatty et al. (2002) model. The effect of corporate governance on earnings management is examined by performing two-way least square dummy variable regression. Data for a period of five years (2016–2020) is collected from the Centre for Monitoring Indian Economy ProwessIQ database, Reserve Bank of India website, annual report of banks, National Stock Exchange and bank’s website.

Regression results exhibit that number of board committees, size and independence of audit committee and joint audit are significantly effective in curbing earnings management. Other board-related variables (size, independence, meetings and diligence) and audit committee variables (meetings and diligence) are not effective in restraining earnings management in Indian banks.

The findings may prove to be helpful to regulators, board of directors and investors. It shows the weak area of corporate governance in India that is lack of autonomy to independent directors, which needs regulators attention and it also suggests that the number of independent auditors should be adequate for audit purposes. The board of directors must ensure the formulation of an adequate number of committees, which perform their own super specialised functions. This study brings an alarm to investors not to rely on reported earnings alone as they may be manipulated.

This paper substantiates the scant literature on the role of corporate governance practices in restraining earnings management in banks of emerging markets and to the best of the authors’ knowledge impact of joint audits on earnings management is previously unexplored in Indian banks, which are examined in this study.

This paper examines the role of institution in the combating crime in India. This study also assesses institutions for controlling property crime in India in the post-reform era.

Crime and socio-economic data are taken from National Crime Record Bureau and the Reserve Bank of India, respectively. Twenty major Indian states are selected for the study purpose for the period of 1994–2019. Fixed effect panel data technique is used for analysis purpose.

Property crime rate declines with economic growth, while it increases with financial development. Findings of fiscal policy instruments are different. Own tax is positively associated with property crime in India, while non-tax fiscal instruments such as fine, penalty, and so on, are inversely related to it. Property crime rate is inversely related to institutional factors like charge sheet and conviction rate.

Further research is needed for other crimes in India. State-level data are used here for analysis purpose; however, spatial or cluster analysis techniques might provide more insights for combating crimes in India.

This study suggests that economic growth and fiscal instrument along with institutional development are essential to control property crime in India.

Government should take steps to improve the law-and-order system to control property crime across states.

Impact of non-tax fiscal instrument reduces property crime while that of own tax is increases it in India. These findings are unique and added certain insight in the study. Institutional roles are captured its performances like charge sheet and convict rate, which are significantly reduce property crime in Indian states. Least square dummy variable model is applied to capture individual state effects.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0063

The main purpose of this study is to examine the relationship between credit risk and financial performance in non-financial firms.

In order to test the relationship between Altman Z-score model as a credit risk proxy and the Return on Asset and Equity as indicator for financial performance with control variables leverage, liquidity and firm size. Least Square Dummy Variable regression analysis is opted. This research's sample included 69 non-financial companies from the Pakistan Stock Exchange KSE-100 Index between 2012 and 2017.

This study establishes the findings that Altman Z-score, leverage and firm size significantly impact the financial performance of the KSE-100 non-financial firms. However, liquidity is found to be insignificant in this study. Altman Z-score and firm size have shown a positive relationship to the financial performance, whereas leverage is inversely related.

This study brings in a new and useful insight into the literature on the relationship between credit risk and financial performance. The results of this study provide investors, businesses and managers related to non-financial firms in the KSE-100 index with significant insight about credit risk's impact on performance.

The evidence of the credit risk and financial performance on samples of non-financial firms has not been studied; mainly it has been limited to the banking sector. This study helps in the evaluation of Altman Z-score's performance in the non-financial firms in KSE-100 index as well.

The objective of the paper is to investigate the relationship between financial risk and the value of the company. In this context, the study is to revisit the trade-off theory of capital structure in the Indian context.

After applying outlier, the study considered 389 nonfinancial companies from BSE500 from 2001 to 2018 collected from the Capitaline database. The statistical package E-views 10 has been utilized for analysis. To understand the nature of the data the descriptive analysis, correlation analysis, normality, unit root, multi-collinearity and Heteroskedasticity were conducted. The Panel Estimated Generalised Least Square with cross-section weight was found suitable for analysis due to the existence of cross-correlated residuals. Further, the study has classified the levels of financial risk to determine the relationship of different levels of financial risk with corporate value.

It was found that the financial risk and corporate value had a significant negative relation during the period of study. On class interval-wise financial risk analysis, it was found that the debt-equity (DE) of around 1:1 may be considered optimal. Below that threshold limit, the DE affects value positively above which the ratio affects the value negatively.

The paper makes an attempt to determine the optimal financial risk at the corporate level in the Indian context.

This paper aims to examine the housing market in Greece after the Global Financial Crisis focussing on regional analysis and urban markets in Athens and Thessaloniki.

The paper uses a data set of over 70,750 property values from 2007 until 2014 incorporating characteristics variables upon which hedonic models are estimated. These form the bases for calculating value indices for mix adjusted houses/apartments by year and region. The indices are used in a panel model in which regional and economic variables are included as independent variables. Using advances in dynamic panel data modelling, a bias-corrected least squares dummy variable corrected (LSDVC) model is applied.

Results indicate the importance of macroeconomic variables in terms of the role of disposable income and significantly different regional effects. Examining the major urban markets, results indicate significant differences in the response of house values to exogenous demand side influences, consistent with the finding of significant regional differences in the LSDVC.

While data on valuations are used that may contain smoothing, the data set covers a large sample of residential properties. As regional economic differences are significant and persistent, housing markets will also behave differently, and hence national policies, unless targeted, will have regionally differentiated effects.

Regional heterogeneity needs to be considered in model estimation.

Policymakers should consider regional differences to improve policy effectiveness.

This is the first paper to use a large sample of residential properties in Greece and apply the LSDVC model to overcome estimation biases.

The primary objective of the paper is to demonstrate the importance of borrower‐specific characteristics as well as local situation factors in determining the demand prospect as well as the risk of credit loss on residential housing loan repayment behavior in India.

Using 13,487 housing loan accounts (sanctioned from 1993‐2007) data from Banks and Housing Finance Cos (HFCs) in India, this paper attempts to find out the crucial factors that drive demand for housing and its correlation with borrower characteristics using a panel regression method. Next, using logistic regression, housing loan defaults and the major causative factors of the same are examined.

In analyzing the housing demand pattern, some special characteristics of the Indian residential housing market (demographic and social features) and the housing loan facility structure (loan process, loan margin, loan rate, collateral structure etc.), that have contributed to the safety and soundness of the Indian housing market have been deciphered. The empirical results suggest that borrower defaults on housing loan payments is mainly driven by change in the market value of the property vis‐à‐vis the loan amount and EMI to income ratio. A 10 percent decrease in the market value of the property vis‐à‐vis the loan amount raises the odds of default by 1.55 percent. Similarly, a 10 percent increase in EMI to income ratio raises the delinquency chance by 4.50 percent. However, one cannot ignore borrower characteristics like marital status, employment situation, regional locations, city locations, age profile and house preference which otherwise may inhibit the lender to properly assess credit risk in home loan business, as the results show that these parameters also act as default triggers.

This study contributes on the micro side of the housing market in India, since it uses unique and robust loan information data from banks and HFCs. The empirical results obtained in this paper are useful to regulators, policy makers, market players as well as the researchers to understand housing market demand and risk characteristics in an emerging market economy such as India.

This study aims to investigate the relationship between digital financial inclusion and economic growth in specific Asian countries, emphasizing the exploration of how digital financial inclusion dynamics impact gross domestic per capita income.

The study creates a digital financial inclusion composite index (DFII) by incorporating essential metrics from the Global Findex report. Economic growth is measured using Gross Domestic Product per capita income in its natural logarithmic form (LnPCI), with three control variables– employment-to-population ratio; population growth and inflation. The analysis utilizes a fixed-effect dummy variable model to examine the relationship, considering unobserved country-specific heterogeneity. 30 Asian countries have been selected for the study for the periods 2014, 2017 and 2021 based on their availability, as outlined in Table 4.

The research revealed a robust positive correlation between the Digital Financial Inclusion Index (DFII) and logarithmic GDP per capita income (LnPCI), indicating higher per capita income with enhanced digital financial inclusion. Employment and population exhibited minimal influence, whereas inflation had a notable negative effect on per capita income. Population growth showed a limited impact. The model demonstrated a high explanatory power for the dependent variable (high R-squared), and the residuals displayed low autocorrelation (Durbin–Watson of 1.96).

This study adds to the existing literature by examining the intricate connection between digital financial inclusion (DFI) and economic growth in 30 Asian countries, employing a comprehensive composite index for analysis.

The purpose of this paper is to investigate whether existing capital structure target adjustment models are able to identify whether companies adjust their capital structures towards an (unobservable) target.

Existing capital structure target adjustment models are applied to a specific dataset by using different regression techniques (ordinary least square, fixed effect, Fama‐MacBeth, least square dummy variable “corrected”, SYS‐GMM).

Existing capital structure target adjustment models are not able to identify whether companies adjust their capital structures towards a target or not. They might indeed indicate target adjustment behaviour when companies' capital structures actually move away from their targets.

As target adjustment behaviour is often used as support for the trade‐off and against the pecking order theory, the “horse race” between both theories seems still to be open.

This paper highlights some of the fallacies of existing capital structure target adjustment models and demonstrates that the results obtained by those models can be highly misleading.