ArticleFrontiers in public health2025
"Good and bad investments" in public health stocks amid the COVID-19 shock: evidence from a transformer-based model.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Major public health emergencies have profoundly reshaped the risk structure and resource allocation logic of capital markets. The market performance of public health-related enterprises has exhibited substantial heterogeneity across different stages of the pandemic, characterized by both considerable risks and emerging opportunities. Understanding this dynamic process is essential for maintaining financial stability and promoting rational investment behavior. Methods: Using the COVID-19 pandemic as the research background, this study selects 55 constituent stocks from the China Securities Index (CSI) Public Health Index as the research sample. A deep learning model based on the Transformer architecture is employed to forecast stock returns and construct long-short investment portfolios. By conducting stage-wise comparisons spanning the pre-pandemic period, the initial outbreak, the normalization phase, and the post-pandemic era, the study reveals the profound temporal evolution and dynamic impacts of public health crises on market investment behavior. Results: The empirical results reveal that the capital market underwent substantial structural reshaping during the initial phase of the pandemic. The Transformer model effectively identified excess return signals from healthcare and epidemic-prevention enterprises, thereby achieving outstanding investment performance. In the mid-pandemic stage, increased market volatility and policy uncertainty weakened the model's stability. As the market transitioned into the post-pandemic period, rationality gradually returned. Similar to the pre-pandemic stage, firms' performance became increasingly driven by fundamentals rather than policy influences, leading to a marked improvement in the model's predictive accuracy and screening capability. Conclusion: This study systematically reveals the structural differentiation and dynamic evolution of public health-related enterprises during the pandemic, thereby extending the research frontier at the intersection of public health emergency response, financial risk, and investment portfolio construction. By bridging these domains, it provides both theoretical foundations and empirical evidence to guide investment strategies and policy formulation for industries closely associated with public health, contributing to more resilient and informed financial decision-making amid future crises.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.