Dataset Information

Response of stock market volatility to COVID-19 announcements and stringency measures: A comparison of developed and emerging markets.

ABSTRACT: We investigate the relationship between the daily release of COVID-19 related announcements, defensive government interventions, and stock market volatility, drawing upon an extended time period of one year, to independently test, confirm and iteratively improve on previous research findings. We categorize stock markets into emerging and developed markets and consider differences and similarities utilizing an asymmetric measure of volatility. We find that there are major differences between these markets with respect to investors' interpretation of risk in response to daily new confirmed cases, death rates, recovery rates, and different defensive government interventions. We suggest explanations for these differences, in terms of national culture, and the quality of governance. Moreover, the development of Pfizer-BioNTech's vaccine is of immense importance to both markets. The findings have implications for tailoring government responses to crises in country-specific contexts.

SUBMITTER: Bakry W

PROVIDER: S-EPMC8994447 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Response of stock market volatility to COVID-19 announcements and stringency measures: A comparison of developed and emerging markets.

Bakry Walid W Kavalmthara Peter John PJ Saverimuttu Vivienne V Liu Yiyang Y Cyril Sajan S

Finance research letters 20210720

We investigate the relationship between the daily release of COVID-19 related announcements, defensive government interventions, and stock market volatility, drawing upon an extended time period of one year, to independently test, confirm and iteratively improve on previous research findings. We categorize stock markets into emerging and developed markets and consider differences and similarities utilizing an asymmetric measure of volatility. We find that there are major differences between thes ...[more]

PMID: 35431683

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Project description:Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. Most works focus on retrospective analysis of announcement impact on company stock prices, bypassing the consideration of the problem in the predictive paradigm. In this work, we aim to close this gap by proposing a framework that allows predicting the numerical values of announcement-induced changes in stock prices. In fact, it is a problem of the impact prediction of the specific event on the corresponding time series. Our framework includes a BERT model for extracting the sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. We operate with one of the biggest FDA (the Food and Drug Administration) datasets, consisting of 5436 clinical trial announcements from 681 companies for the years 2018-2022. During the study, we get several significant outcomes and domain-specific insights. Firstly, we obtain statistical evidence for the clinical result promulgation influence on the public pharma market value. Secondly, we witness inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to negative clinical news. Thirdly, we discover two factors that play a crucial role in a predictive framework: (1) the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of low diversification among drug products and (2) the announcement network effect, manifesting through an increase in predictive power when exploiting interdependencies of events belonging to the same company or nosology. Finally, we prove the viability of the forecast setting by getting ROC AUC scores predominantly greater than 0.7 for the classification of price change on historical data. We emphasize the transferability and generalizability of the developed framework on other datasets and domains but on the condition of the presence of two key entities: events and the associated time series.

Dataset Information

Response of stock market volatility to COVID-19 announcements and stringency measures: A comparison of developed and emerging markets.

Publications

Response of stock market volatility to COVID-19 announcements and stringency measures: A comparison of developed and emerging markets.

Similar Datasets

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