The coronavirus SARS-CoV-2 and its impact on the world

Nowosad, Andrzej; Turanli, Ümit; Lorenc, Katarzyna

doi:10.4324/9781003211891-1

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Previous studies investigated this issue [9,10], but few focused on Europe [11]. Hence, more research is necessary, especially since Europe is one of the continents that suffered most from the economic and social ravages of the pandemic [12].…”

Section: Introductionmentioning

confidence: 99%

Fear of COVID-19 Effect on Stock Markets: A Proposal for an Algorithmic Trading System Based on Fear

Paule-Vianez

Orden‐Cruz

Martínez

et al. 2023

JTAER

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This study analyzes the fear of COVID-19 effect on European stock market returns. For this purpose, the search volumes (SV) collected by Google Trends (GT) and Wikipedia were used as proxies of fear of COVID-19. In a sample from 13 European stock markets, fear of COVID-19 was found to be associated with negative European stock returns. Our research employed this observation to propose an algorithmic trading system based on fear of COVID-19. Back-testing results show the possibility of extraordinary returns based on this system. These findings have important implications for political authorities, the mass media, and investors.

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Section: Introductionmentioning

confidence: 99%

Fear of COVID-19 Effect on Stock Markets: A Proposal for an Algorithmic Trading System Based on Fear

Paule-Vianez

Orden‐Cruz

Martínez

et al. 2023

JTAER

View full text Add to dashboard Cite

show abstract

“…Viruses have a long history of posing threat to living organisms ( 1 ) as they have caused more than 300 million deaths worldwide ( 2 ). A recent emanation Coronavirus) is an example of an acute virus that caused a global pandemic ( 3 ). According to World Health Organization, Coronavirus has caused approximately more than 400 million infections and 6 million deaths across the globe ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses

et al. 2022

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Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.

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The coronavirus SARS-CoV-2 and its impact on the world

Cited by 2 publications

References 1 publication

Fear of COVID-19 Effect on Stock Markets: A Proposal for an Algorithmic Trading System Based on Fear

Fear of COVID-19 Effect on Stock Markets: A Proposal for an Algorithmic Trading System Based on Fear

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses

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