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This paper analyzes how air pollution and the public attention to it influence the returns of stocks in the Indian context.

The study uses firm-level data for the stocks listed on National Stock Exchange in India. Air quality is measured using the Air Quality Index (AQI) values provided by US Embassy and Consulates’ Air Quality Monitor in India. Google Search Volume Index (GSVI) of the relevant terms acts as the measure of public attention. Appropriate regression models are used to address how AQI and attention influence stock returns.

It is observed that degrading air quality alone is unable to explain the stock returns. It is the combined effect of increasing AQI and subsequent rise in associated public attention that negatively impacts these returns. Returns of firms with poor environment score component in their environmental, social, governance (ESG) scores are more negatively affected compared to firms with higher environment scores.

Investors can make use of this knowledge to formulate effective trading strategies and ensure higher chances of profitability in the share market.

To the knowledge of the authors, no earlier study has investigated the effects of AQI and attention together to explain stock price movements. The study is conducted in the Indian context providing a unique opportunity to study the behavioral impact of these effects in one of the fastest growing global economies, which is also plagued by an alarming increase in ambient air pollution.

This study aims to explore the asset pricing implications of attention allocation theories in the Indian stock market.

Investor attention is captured through investors' search behavior, the Google search volume index. Panel least square method is used in this study, and the research is performed at firm-level upon NSE100 constituent firms with 21,566 firm-week observations.

The authors find a significant increase in abnormal return following an increase in abnormal attention. Also, this effect is strengthened for smaller firms and firms with positive sentiments. Further, applying a geographic lens to the investigation, it is found that the attention impact is attributable to local investors. Finally, the study demonstrates that local attention-based portfolio formation and trading strategy, i.e. long in high abnormal local attention stocks and short in low abnormal local attention stocks, leads to a significant return premium.

This study reveals that behavioral factors like investor attention drive the Indian Stock Market. Also, the geography analysis shows that observing investors' behavior enables predicting the arrival of private information. Thus abnormal local attention can be a potential input factor for forecasting exercises and trading strategy formation, thereby aiding in exploiting profitable opportunities.

The study captures asset pricing implications of investor attention and explores the effect of firm size and sentiment on the attention–return relationship in an emerging economy, India. It also relates location proximity with investors' attention allocation and tests its implications on stock prices.

This study aims to empirically examine the relationship between corporate social responsibility disclosure (CSRD) and financial performance (FP) in Indian firms.

Data for CSRD is collected by conducting content analysis of CSRD in annual reports of the sampled firms. A multidimensional measure of CSRD is constructed based on the stakeholder theory, consisting of six stakeholder groups – employees, customers, investors, community, environment and others. The aggregate CSRD measure is created by combining disclosure of the six CSR dimensions. Multiple regression analysis is used to examine the CSRD–FP linkage, controlling for the confounding effects of size, risk, age, industry, ownership and period.

The results of this study indicate that the aggregate CSRD measures, both for quality and quantity, have a positive association with the accounting measures of firms’ FP. However, the market measure of FP is observed to have a statistically insignificant association with aggregate quality and quantity of CSRD of Indian firms.

The results reveal that adopting transparent and extensive CSRD is relevant for the profitability of firms, and that government interventions are required to promote CSR programs, with a specific focus on the CSR dimensions that provide no apparent financial gains.

This study recommends the adoption and reporting of CSR practices by Indian firms for their stakeholders.

This study contributes to the scarce literature on the CSRD–FP linkage in the context of emerging economies by using a more inclusive data set, creating a reliable measure of CSRD applicable to a large universe of firms and including relevant control variables that affect the CSRD–FP relationship.

The study examines the effect of increased Economic Policy uncertainty on analyst optimism in the Indian market. The study also explores whether the SEBI Research Analyst Regulation, 2014, has effectively contained the optimistic nature of analysts.

The study is based on firms in the Indian market. The sample period is 2003–2020. It runs a linear panel regression to measure the impact of Economic Policy uncertainty on the optimism level of analysts' forecasts and recommendations, controlling for firm fixed effects. Further, the impact of the SEBI Research Analyst Regulation, 2014, has been assessed with the help of the difference-in-difference approach.

The Economic Policy uncertainty is significantly and positively related to the analyst optimism, reflected in the forecast bias and recommendation in the Indian context. The experience of analysts and the age of the firm positively drive optimism. However, introducing the Research Analyst Regulation by SEBI led to a decline in analyst optimism. The regulation decoupled the analysts' compensation from brokerage service transactions. Thus, the results suggest that the regulation has effectively curbed the incentive to produce optimistic output.

This is the first study in the Indian market to assess the impact of uncertainty on analyst output. It also investigates the effectiveness of the first analyst-specific regulation in India, i.e. The Research Analyst Regulation, 2014.

The study aims to address two objectives. First, to examine the socioeconomic and demographic factors contributing to financial literacy and second, to analyze if financial literacy affects investment choices.

The study uses financial inclusion insights (FII) survey data conducted by Intermedia, comprising 47,132 individuals in India. Further, instrument variable estimation has been used to analyze the relationship between financial literacy and individuals' investment choices.

The study finds that differences in financial literacy level can be attributed to various socioeconomic/demographic factors like age, gender, education levels, income, location of residence, sources of information, etc. Econometric analyses indicate that financial literacy influences investment decisions, mainly in businesses and traditional assets such as gold, property, etc.

The study contributes to the growing literature on financial literacy in the context of developing countries like India and highlights the role of financial literacy in how individuals make investment choices. Using a novel instrument, i.e. participation in the stock market by family or peers for advanced financial literacy, the results provide evidence that advanced financial literacy among individuals increases the probability of their stock market participation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2021-0764.

The study examines the cross-sectional and asymmetric relationship of investor sentiment with the stock returns and volatility in India.

The investor sentiment is captured using a market-based measure Market Mood Index (MMI) and a survey-based measure Consumer Sentiment Index (CSI). The asymmetric effect of the relationship is examined using quantile causality approach and cross-sectional effect is examined by considering indices such as the BSE Sensex, and the various size indices such as BSE Large cap, BSE Mid cap and BSE Small cap.

The result of the study found that investor sentiment (MMI) cause stock returns at extreme quantiles. Lower sentiment induces fear-induced selling, thereby lowers the returns and high sentiment is followed by lower future returns as market reverts to fundamentals. On the other hand, bullish shifts in sentiment lower the volatility. There exists a positive feedback effect of stock return and volatility in the formation of investor sentiment.

The study captures both asymmetric and cross-sectional relationship of investor sentiment and stock market in an emerging economy, India. The study uses a novel data set (i.e.) MMI which captures the sentiment based on market indicators and are widely disseminated to the public.

In the current study, the significance of extreme positive returns has been investigated in the pricing of stocks in the Indian equity market. This study aims to understand if investors in India have a preference for lottery-like stocks. The existing literature provides support for MAX effect in several countries, where risk seeking in the form of gambling is an acceptable form of social behavior, suggesting a preference for lottery-like stocks. This motivates the authors to investigate whether such preference for lottery-like stocks is prevalent in a country such as India with a different cultural setting, where gambling is not socially and legally encouraged.

The MAX effect is tested in the Indian market for the period from January 2003 to March 2017. The average number of firms per month in this study is 2,949. Univariate and bivariate portfolio-level analyses, as well as Fama MacBeth regressions, are conducted to observe the difference between average raw and risk-adjusted returns between the stocks lying in the highest and lowest MAX deciles. Several tests have been performed for checking the robustness of the findings.

Unlike the extant literature, the authors have not found any evidence of a negative relationship between extreme positive returns and expected returns. The univariate and bivariate analyses suggest that high MAX deciles over-perform low MAX deciles. Fama Macbeth regressions also do not support the negative relationship documented for other markets. This suggests that investors are not euphoric about lottery-like stocks in India. One may devise profitable trading strategies by going long on high MAX deciles and short on low MAX deciles.

This study finds a behavioral aspect of Indian investors, which seems to be in contrast to that of other countries. While there is a strong preference for lottery-like stocks in other markets, investors in India do not end up overpaying for such stocks in the market. This tendency might be an outcome of a different social and regulatory setting in India. In view of the fact that India is increasingly becoming an important investment destination, it becomes important to devise investment strategies based on the peculiarities of this market rather than simply extrapolating the findings of other markets.

The purpose of this paper is to capture the investors' mood related to the COVID-19 pandemic and analyze its impact on the stock market returns.

To capture the investor mood related to the COVID-19 pandemic, the authors construct a unique COVID-19 fear index based on the Search Volume Index (SVI) from Google Trends (http://www.Google.com/trends/) of the search terms related to COVID-19 words and phrases as revealed by Google and Internet dictionaries. The COVID-19 fear index was used to investigate its impact on the stock market returns.

The study finds a strong negative association between COVID-19 fear and stock returns. Unlike other studies, the relationship is persistent for a significant period. This relationship is not found to reverse in the following days. The results also highlight that COVID-19 fear strongly impacts the stock market. The sentiment persists for a significant period and is not reversed soon, unlike the regular times in earlier studies.

The study is among the very few studies that constructed COVID-19 fear index using several Google search terms and captured its impact on the stock market returns.

The purpose of this paper is to explore whether stock selection strategies based on four fundamental quality indicators can generate superior returns compared to overall market.

The sample of stocks comprises the constituents of BSE-500 index, which is a broad based index consisting of highly liquid stocks from all 20 major industries of the Indian economy. Portfolios are constructed on the basis of quality indicator rankings of companies and the returns of these portfolios are compared with the overall market. Excess returns on quality based portfolios are also determined using OLS regressions of quality portfolio returns on market, size, value and momentum factor returns.

The results suggest that two of the four quality strategies, namely Grantham Quality indicator and Gross Profitability have generated superior returns after controlling for market returns as well as common anomalies such as size, value and momentum. Combining value strategies with quality strategies do not yield any significant gains relative to quality only strategies.

For investors looking to invest in the Indian stock market for a long term, this study provides evidence on the performance of some fundamental indicators that can help predict long run stock performance. The findings suggest that investors can distinguish between high-performing and low-performing stocks based on stock quality indicators.

This is the first such study to look into the performance of quality investing in the Indian stock market. As most quality investing studies have been focussed on developed economies, this paper provides out-of-sample evidence for quality investing in the context of an emerging market.

The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets.

The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R²).

The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable.

Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.