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The present study empirically examines the impact of coronavirus disease 2019 (COVID-19) and policy uncertainty on stock prices in India during the COVID-19 pandemic.

To this end, the authors use the daily data by applying the autoregressive distributed lag (ARDL) model, which tests the short- and long-run relationship between stock price and its covariates.

The study finds that increased uncertainty has adverse short- and long-run effects on stock prices, while the vaccine index has favorable effects on stock market recovery.

From investors' perspectives, volatility in the Indian stock market has negative repercussions. Therefore, to protect investors' sentiments, policymakers should be concerned about the uncertainty induced by the COVID-19 pandemic and similar other uncertainty prevailing in the financial markets.

This study used the news-based COVID-19 index and vaccine index to measure recent pandemic-induced uncertainty. The result carries some policy implications for an emerging economy like India.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0244

The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.

The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.

The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.

The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.

Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.

This study aims to identify mutually exclusive risk categories and determine whether these categories effectively capture the potential impact of risk disclosures on the initial returns of initial public offerings (IPOs) in the financial and non-financial sectors.

Data were collected from 131 Indian IPO prospectuses (104 non-financial and 27 financial) issued between 2015 and 2021. Content analysis was performed to identify mutually exclusive risk categories, and the effects of these categories on initial IPO returns were assessed by regression analysis

The findings revealed that risk factor disclosures have a significant impact on underpricing, but not all risk factors are relevant. In the current study, in the financial sector, IPO underpricing was mostly driven by technological and competitive risk factors. In the non-financial sector, underpricing was predominantly influenced by operating risk and compliance risk factors.

The limitations of this study include the use of sentence-based context analysis, which does not assess the quality of risk disclosures. The statistical data reduction technique used to generate mutually exclusive risk categories may also be a limitation.

This research has the potential to assist companies in standardizing the disclosure of risks within IPO prospectuses. The insights gained can inform market regulators in designing policies aimed at aiding investors in formulating investment strategies, ultimately enhancing transparency and clarity regarding information disclosure. Moreover, the findings offer valuable guidance to investors in selecting IPOs aligned with their risk tolerance levels.

From a societal perspective, this study represents advancements by guiding regulators towards developing and regulating standardized, mutually exclusive risk factors. Such measures can aid investors in enhancing their decision-making perspectives regarding IPOs, promoting a more informed and confident investment environment.

This study is a pioneering attempt to address knowledge gaps by identifying distinct categories of risk disclosures in IPO prospectuses and examining their potential influence on IPO underpricing in the financial and non-financial sectors in India.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

This paper aims to explore the extent, nature and communication potential of companies’ use of three popular social media platforms (Facebook, X and LinkedIn) to report on sustainability.

Qualitative methodology through the use of the netnography approach was adopted to evaluate the use of social media for sustainability communication by the Top 50 ASX companies. Content analysis of all company posts determined those with social and environmental content. A thematic analysis was performed using the global reporting initiative (GRI) framework to examine the nature of the reporting. The media richness framework was used to measure the communication potential of the social media platforms for sustainability communication.

The results indicated that the extent of sustainability posts on social media represented less than 20% of total social media posts. The nature of posts by the Top 50 ASX companies was higher on social issues than on environmental issues, which is contradictory to many previous studies. The study also found that while the social media platforms afforded high levels of media richness, most companies failed to exploit the platforms’ full potential to disseminate sustainability information.

This work provides both empirical and theoretical contributions to the ongoing debate concerning the use of social media for sustainability communication. The paper extends Lodhia et al.’s (2020) study of social media use for legitimation purposes and adapts Lodhia’s (2004) media richness framework to social media for sustainability reporting. It adds empirical insights into social media’s communication potential and value for communicating sustainability information.

The extent and nature to which organisations use social media to disclose their sustainability performance has significant practical implications for a variety of stakeholders. The results reveal to these stakeholders and the companies themselves the level of utilisation of social media along with the potential that can be harnessed. These results can potentially improve the quantity, timeliness and usability of sustainability reporting using social media platforms.

The study provides valuable evidence to increase understanding of the sustainability social media communication landscape, which organisations can potentially leverage to communicate their messages. Additionally, sustainability awareness is increased across various demographics by disseminating sustainability information to the wider public. This study will assist policy-setters in developing guidance for using social media for sustainability reporting.

This study extends existing literature, particularly the Lodhia et al. (2020) study, which has primarily focused on examining sustainability content in the media with limited exploration of the communication potential of social media platforms to communicate sustainability content.

The cognitive biases exhibited by investors could hinder their capacity for logical reasoning and impact their perception and reaction to information when making financial choices. So, this study was done to identify the behavioral biases that hinder investors' sound decision-making at the Pakistan Stock Exchange (PSX).

A cross-sectional study was undertaken employing a causal research design approach. Questionnaires were administered to individual investors of the PSX as the data collection methodology. The data were subsequently analyzed through the utilization of the Smart PLS Structural Equation Modeling (SEM) technique.

The results suggest that information factors and cognitive biases, namely home bias, geographical bias, investor sentiment, salience, and over/under reaction have a positive association with the investors' choices at PSX.

The study’s emphasis is on the impact of behavioral biases on individual investors only, even though such biases also influence the investment decisions of institutional investors.

The study holds implications for scholars engaged in the field of behavioral finance as well as professionals involved in the stock market, particularly those interacting with individual investors and personal finance. Additionally, the current study will take into account investors, financial advisors, practitioners, policymakers, investment experts, stakeholders or target groups, etc. to support various groups in their professional activity and to help them overcome such biases that influence their sound decision-making power.

The innovative aspect of this research is its ability to advance the understanding of the conceptual underpinnings and social structure of behavioral biases by critically analyzing the body of prior research and adding value to the existing body of literature on behavioral finance in Pakistan by investigating the combined impact of never-studied variables, i.e. geographical bias and information variables, understudied behavioral variables, i.e. home bias and salience and studied variables, i.e. investor sentiment and over/under reaction on individual investor investment decisions at PSX.