The study reveals how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics.
Ashu Lamba, School of Business Studies, Vivekananda Institute of Professional Studies, New Delhi, India.
Priti Aggarwal, Assistant Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Sachin Gupta, School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India.
Mayank Joshipura, School of Business Management, Narsee Monjee Institute of Management Studies Deemed to be University, Mumbai, 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.
Published in: International Journal of Pharmaceutical and Healthcare Marketing
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