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This study endeavours to examine the relationship between information asymmetry and expected stock returns at the National Stock Exchange (NSE) of India, with a sample of NIFTY 500 stocks for a period ranging from 1st April 2000 to 31st March 2018, by employing three different proxies of information asymmetry: number of transactions, institutional ownership and idiosyncratic volatility.

The return differential amongst information-sorted decile portfolios has been assessed to understand the effect of information risk on stock returns by employing (1) traditional measures of performance evaluation like mean, Sharpe,  Treynor and information ratios, (2) regression models like the capital asset pricing (CAPM), Fama and French three-factor, Carhart's four-factor, information-augmented CAPM, information-augmented Fama and French three-factor and information-augmented Carhart's four-factor models and (3) an autoregressive distributed lag (ARDL) model.

The empirical evidence indicated that as information asymmetry associated with portfolio increases, returns also expand to recompense investors for bearing information risk validating the existence of a significant positive relationship between information asymmetry and expected stock returns at the NSE. Amongst the various asset pricing models employed in this study, the information-augmented Fama and French three-factor model turned out to be the best in explaining cross-sectional variations in portfolio returns.

Strong information premium was observed such that high information stocks outperformed low information stocks which have strong inference for investors and portfolio managers, who all continuously look out for investment strategies that can lend hand to beat the market.

Easley and O'Hara (2004) proposed that stocks with more information asymmetry have higher expected returns. Very few studies have examined this relationship between information risk and stock returns that too restricted to the US market only, with a few on other emerging markets. No work has been conducted on the concerned issue in the Indian context. Therefore, it seems to be the first study to explore the relationship between information asymmetry and expected stock returns in the Indian securities market.

The present study seeks to investigate the relative edge between the market microstructure of the two leading stock exchanges of the Indian capital market, that is BSE and NSE with a focus on analysing their trading mechanism, efficiency, liquidity and volatility.

We analyse the microstructure of BSE and NSE on the basis of: (1) trading mechanism – ownership structure, listing of securities, trading system and settlement and clearing process; (2) information efficiency using unit root test, serial correlation, runs test, variance ratio and the ARIMA model; (3) liquidity using trading statistics no. of listed Companies, market capitalisation, no. of trades etc. and (4) volatility using standard deviation and GARCH(1,1) model.

A comprehensive scrutiny on microstructure of BSE and NSE makes it evident that the two leading stock exchanges of India are mostly similar and leave no scope to choose between them. Both the exchanges are demutualised corporate entities with a fully automated trading system in an order-driven market, informationally inefficient as evidenced by the predictability of returns, have shown tremendously growing trading statistics and by and large a declining trend in volatility over the years.

Understanding the components of the microstructure black-box will provide the regulatory bodies with an intellectual framework to strengthen the market architecture. Both the exchanges will get aware of the dynamics of trading, can grow to be more competitive and attract more firms for listing and investors for trading of securities. Also, investors, portfolio managers and equity analysts will be able to make better investment strategies by understanding how the market works.

Research in the area of market microstructure has been severely neglected, especially in the context of the Indian market. India is the world's fastest growing economies and we have witnessed tremendous reforms in the capital market. The past two and a half decades have brought about several innovations via demutualisation, screen-based trading, emergence of clearing corporations, innovative financial products and intense use of IT in the Indian stock market. A spurt of reforms and the emerging environment make it crucial to deeply analyse the market structure and design of two premier stock exchanges of India – BSE and NSE.

This research uses the distinction between communal relationships (based on mutual care and concern) and exchange relationships (based on the principle of quid pro quo) to propose a framework that predicts differences in the shape of consumer response function to increasing levels of brand transgressions.

The paper proposes a conceptual model based on insights from prior research on brand relationships.

The premise being proposed in this paper is that exchange-oriented consumers, being focused on the balance of inputs and outcomes, base their evaluations on an objective assessment of the final outcome, such that their response function will be relatively proportional to increasing levels of brand transgression. On the other hand, communally oriented consumers are concerned with whether or not the relationship partner cares for them, such that up to a point brand transgressions are overlooked while beyond a threshold there is a sudden negative shift in brand evaluations. These consumers thus exhibit a step-function response to brand transgressions.

This paper proposes a conceptual framework and leaves it to future researchers to test it empirically.

Managers now have a toolkit to better manage instances of product and service failure.

Findings from this model can be applied to better manage interpersonal relationships too.

This paper proposes a model that shows how and why consumers might differ in their response to brand transgressions. Further, this is a dynamic model in that it traces the response function of the consumers at increasing levels of transgressions.

While sustainable growth extends the use of resources, it is crucial to explore green growth (GG) that ensures growth sustainability through the adoption of renewable energy. Thus, this study is motivated to investigate the influence of renewable energy on GG in 19 emerging countries spanning a decade and a half (2000–2020). This study aims to provide a quantitative examination of how renewable energy contributes to sustainable economic growth.

This study uses advanced dynamic common correlated effect techniques to assess the long-term effectiveness of renewable energy on GG. Additionally, it uses Dumitrescu and Hurlin causality tests to identify synchronicity between the respective variables.

The findings of this study reveal that the adoption and utilisation of renewable energy effectively promote GG in emerging economies. However, in contrast, the significantly greater negative influence of trade openness on GG compared to renewable energy highlights the inadequacy and limited impact of cleaner energy alone.

To the best of the authors’ knowledge, existing literature predominantly focuses on investigating the relationship between renewable energy and economic growth, with only a limited number of studies exploring the impact on GG. To the best of the authors’ knowledge, this study would be the first to analyse this relationship in these emerging countries. Furthermore, previous estimation frameworks used in prior studies often overlook the crucial factor of cross-sectional dependence (CSD) among countries. Therefore, this study addresses this issue using a contemporary econometric approach that deals not only with CSD but other biases, like endogeneity, autocorrelation, small sample bias, etc.

The difficulties higher education institutions in developing countries face in finding adequate and relevant onsite student internship opportunities make a case for online internships. The purpose of this paper is to present an online internship model, developed over a two-year period, which challenged students to engage in learning-by-doing projects that addressed a key barrier in the implementation of ICT policies in public education, the paucity of audio-visual content in local languages.

The design of the model comprised the development of instructional videos by 340 interns, the evaluation of the videos by two interns and their testing by 31 interns through a field experiment using a between-subjects pre-test – post-test design in 54 schools. The process was repeated the following year with the field experiment replaced by the development of teaching manuals. The changes in reflective learning among 112 of the 119 interns who developed video content in this repeat round were assessed.

The field experiment found that the intern-developed videos improved Mathematics and Science scores among school students but not the attitudes to these subjects. Participation in online internships improved reflective learning.

The evaluation of change in reflective learning is based on self-reported measures.

The online internship model presented in the paper can address concerns related to inadequate internship opportunities, while addressing gaps in public policy implementation by systems such as education, health and rural development.

The paper outlines the design of an online student internship model and a methodology for implementing it. The study indicates the feasibility of a low-cost, large-scale online model of internship.