Evidence map›Paper›PMID 34848898›Full record

ArticleTechnological forecasting and social change2022

US biopharmaceutical companies' stock market reaction to the COVID-19 pandemic. Understanding the concept of the 'paradoxical spiral' from a sustainability perspective.

Juan Piñeiro-Chousa, M Ángeles López-Cabarcos, Lara Quiñoá-Piñeiro, Ada M Pérez-Pico

Abstract read
In one paragraph

Article in Technological forecasting and social change, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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  5. The sum of all SCARES COVID-19 sentiment and asset return.The Quarterly review of economics and finance : journal of the Midwest Economics Association · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Juan Piñeiro-ChousaUniversity of Santiago de Compostela, Spain.
M Ángeles López-CabarcosUniversity of Santiago de Compostela, Spain.
Lara Quiñoá-PiñeiroUniversity of Santiago de Compostela, Spain.
Ada M Pérez-PicoUniversity of Santiago de Compostela, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In an uncertain and finite world, actions towards the development of a green economy are attracting wider support. The damaging anthropogenic impact on earth systems is leading humanity to devastating situations, jeopardizing its very survival. A new world scenario has emerged with COVID-19, where the biopharmaceutical sector has arisen as a powerful effective solution for the health, economic and social crisis derived from the pandemic. This research aims to study the stock market reaction of the two US biopharmaceutical companies that first developed messenger RNA vaccines against COVID-19 (Pfizer and Moderna), considering two time periods, before and during COVID. In the analysis, the influence of the technological market index, market volatility, and investor sentiment are also considered. The results show an unequal influence of market volatility and market sentiment on the returns of both companies, as well as a different volatility behaviour. Furthermore, a contagion effect is observed during the COVID period between both companies and the technological market. The study's findings provide investors, organizations, policy-makers and society with useful information for the design of policies and strategies that ultimately are called to ensure sustainable growth for future generations.

Indexed as

Biopharmaceutical companiesCOVID-19Green economyMarket sentimentSustainabilityVolatility

Identifiers

PMID34848898
PMCPMC8612827

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.