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The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.

The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).

The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.

In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.

The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

Asset pricing revolves around the core aspects of risk and expected return. The main objective of the study is to test different asset pricing models for the Indian securities market. This paper aims to analyse whether leverage and liquidity augmented five-factor model performs better than Capital Asset Pricing Model (CAPM), Fama and French three-factor model, leverage augmented four-factor model and liquidity augmented four-factor model.

The data for the current study comprises records on prices of securities that are part of the Nifty 500 index for a time frame of 14 years, that is, from October 2004 to September 2017 consisting of 183 companies using time series regression.

The results indicate that the five-factor model performs better than CAPM and the three-factor model. The model outperforms leverage augmented and liquidity augmented four-factor models. The empirical evidence shows that the five-factor model has the highest explanatory power among the entire asset pricing models considered.

The present study bears certain useful implications for various stakeholders including fund managers, investors and academicians.

This study presents a five-factor model containing two additional factors, that is, leverage and liquidity risk along with the Fama-French three-factor model. These factors are expected to give more value to the model in comparison to the Fama-French three-factor model.

Analysts expect reduced bank earnings as a result of the impact of the increase in bad loans. Banks have strategically created high provision coverage ratios allocating large funds for possible deterioration in asset quality. Given the expected faster growth and recovery in the bank lending sector, investors have always been interested in banking stocks, despite the waves of non-performing assets (NPAs) and recessionary influences. Historical references reiterate that the banking stocks have been better performers in their returns compared to the capital markets.

The study aims to examine the impact of key accounting variables on the stock prices of Indian banks in the panel data framework.

The current study explores the impact of accounting variables on the market prices of shares. After the study, it may be concluded that earning per share (EPS), return on equity (ROE), capital adequacy ratio (CAR) and net interest margin (NIM) have an incremental impact on the prices of banking stocks, and the current ratio (CR) and NPAs have a detrimental impact on them.

Studying their impact on stock prices is the most convenient fundamental analysis that could be conducted on the stock prices of the banks.

To provide insights into the association of the accounting and regulatory variables there is a severe limitation in the quantity of the literature available. This study has attempted to build a relationship between the accounting and regulatory variables and the stock prices of banking stocks, to help investors with some reliable methods to estimate the stock prices in the future.

The purpose of this paper is to study the concept of purging and present a comparative study of the existing purging methodologies prevailing in the market with a view to evolving a more effective method of capturing the entire impure income to be purged.

To illustrate the present discussion, a case study of purging based on numerical examples has been included. The argument has also been supported with empirical data related to the universe of Sharīʿah-compliant stocks listed on Indian stock exchanges.

During the study, it was found that the existing purging methodologies of calculating impure income to be purged have conceptual and practical shortcomings.

The scope of the current research is limited to calculation of impure income which accrues on account of Sharīʿah non-compliant investments directly or indirectly. It does not try to quantify the benefit which may be imputed in the form of capital gains made in trading of the investee company shares due to higher market value of the shares as a result of the impure income earned by the investee company. The paper has focused on identifying and calculating the impure income on account of interest. Impure income earned from specific Sharīʿah non-compliant products or services has not been considered directly. The reason for this is that companies dealing in such products or services are generally excluded at the business screening stage itself. In the case of those companies which derive a relatively small proportion of their total income from such activities and pass the business screening stage, the quantum of the impure income is not generally reported separately in company accounts.

The result of adopting the proposed methodology will lead to complete purging of impure income (to the extent that is possible under present Company Law and stock exchange reporting regulations). Implementation of the proposed method requires a proper understanding of the working of listed companies and either a sound mathematical background or access to a software application to calculate the impure income to be purged.

The current paper is original and based on the authors’ personal understanding and experience of providing Sharīʿah consultancy services related to Sharīʿah-compliant investments.

The study has endeavored to assay the nexus between the converged version of the International Financial Reporting Standards (IFRS) on the performance of the Indian-listed manufacturing firms.

The study has randomly accessed the data of the Bombay Stock Exchange (BSE) listed Indian manufacturing firms using the Prowess IQ database. It has covered 2014–2016 as pre-IFRS and 2017–2020 as the post-IFRS convergence period. Moreover, the study has followed a longitudinal research design with cross-sectional time-series data and has used the difference-in-difference (DiD) technique to assess the effect of the IFRS convergence on firm performance (FP).

The results have indicated that the adoption of the Indian Accounting Standards (Ind AS) has unlikely reported better FP. It has concurred policy implications as full adoption rather than convergence could reap the benefits of the IFRS.

It has contributed to the existing body of knowledge by assaying the effect of the IFRS convergence on FP in developing economies like India using the DiD methodology. The study is an original piece of research and is free from plagiarism.

This paper aims to identify, examine, and present an empirical research design of behavioral finance of potential investors during Covid-19.

A well-structured questionnaire was designed; a survey was conducted among potential investors using convenience sampling, and 200 valid responses were collected. The research work uses multiple regression and discriminant function analysis to evaluate the influence of cognitive factors on the financial decision-making of investors.

Recency and familiarity bias are proven to have the highest significant impact on the financial decisions of investors followed by confirmation bias. Overconfidence bias had a negligible effect on the decision-making process of the respondents and found insignificant.

Covid-19 is a temporary phase that may lead to changes in financial behavior and investors’ decisions in the near future.

The paper will help academicians, scholars, analysts, practitioners, policymakers and firms dealing with capital markets to execute their job responsibilities with respect to the cognitive bias in terms of taking financial decisions.

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from literature on the chosen subject that no study has been undertaken to evaluate the impact of cognitive biases on financial behavior of investors during Covid-19.

This study aims to attempt to evaluate and establish the relationship between gender diversity (GD) on the board and corporate sustainability performance.

A sample of 212 non-financial companies listed on the National Stock Exchange has been considered for a period of 2013–2014 to 2018–2019. For the purpose of the analysis, this study has conducted the static panel data model analysis and also some diagnostics tests to arrive at robust results.

This study, from its analysis, interprets that GD or the proportion of women directors in the company plays a significant role in the decisions related to the sustainability performance of the company. Alongside GD, the profitability of the company, measured in terms of Tobin’s Q, and firm size are also seen to have a positive impact on the sustainability performance of the company.

This study from its findings contributes to the existing works of literature by highlighting the impact of GD on the sustainability performance of the firm. This study thus recommends the recruitment of an ample number of females in the top-notch positions of the board to create a gender-diverse management team to reap the benefits of leadership styles of both genders.

Very few studies have been conducted on the dynamics of women’s directorship, especially in an emerging economy like India. This study thus tries to fill this important gap in the literature by examining the relationship between board GD and sustainability performance of Indian firms.