Network Based Evidence of the Financial Impact of COVID-19 Pandemic

Df, Ahelegbey; Cerchiello, Paola; Scaramozzino, Roberta

doi:10.2139/ssrn.3780954

Cited by 6 publications

(6 citation statements)

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“…Indeed, the largest average Transfer Entropy is measured from Healthcare to Energy with 0.92. These results are in line with [28], which showed that the healthcare sector increased the level of importance (expressed in terms of network connectivity) during the waves of the pandemic outbreak in the US market.…”

Section: Varsupporting

confidence: 89%

“…However, this is in line with what was already reported in [31] where a reduction of centrality of the financial sector was pointed out. This was also reported by [28], where, through a temporal dynamic network analysis, the authors show that the financial sector behaves differently as an isolated cluster which reacts mainly to market price data (more on such peculiar pattern in [32]). Another important sector is the technological one, either as influencer or follower depending on the network we may consider.…”

Section: Discussionsupporting

confidence: 68%

“…In Appendix B, we add two plots for the time series (the first for returns and the second for sentiment) and also two more images on a subset for an improved visualization. This result could be deemed as a bit surprising in the light of the COVID-19 virus outbreak started in the spring of 2020, but, as already showed in [28], such shock had a small impact on the overall statistics of sentiment time series. After these pre-processing steps, we obtain a complete dataset, with values on the same scale for a total of 100 variables (50 prices log-returns and 50 sentiment index) and 515 observations (two years of work-daily data).…”

mentioning

confidence: 72%

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Information Theoretic Causality Detection between Financial and Sentiment Data

Scaramozzino

Cerchiello

Aste

2021

Entropy

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The interaction between the flow of sentiment expressed on blogs and media and the dynamics of the stock market prices are analyzed through an information-theoretic measure, the transfer entropy, to quantify causality relations. We analyzed daily stock price and daily social media sentiment for the top 50 companies in the Standard & Poor (S&P) index during the period from November 2018 to November 2020. We also analyzed news mentioning these companies during the same period. We found that there is a causal flux of information that links those companies. The largest fraction of significant causal links is between prices and between sentiments, but there is also significant causal information which goes both ways from sentiment to prices and from prices to sentiment. We observe that the strongest causal signal between sentiment and prices is associated with the Tech sector.

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Section: Varsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 68%

mentioning

confidence: 72%

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Information Theoretic Causality Detection between Financial and Sentiment Data

Scaramozzino

Cerchiello

Aste

2021

Entropy

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“…Sentiment analysis can be applied in several life applications and employed in different sectors from marketing and finance to health and politics [12]. People are expressing their feelings about products, services, events, organizations or public figures [13].…”

Section: Introductionmentioning

confidence: 99%

“…The World Health Organization (WHO) announced it as a global pandemic on 11 March 2020 [23]. The pandemic had a negative effect on the biggest companies in different sectors and the whole productive system around the globe [12]. On the other hand, some companies in the medical product sector producing personal protective equipment (PPE) witnessed an increased demand for their products during the pandemic.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era

et al. 2021

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Due to the accelerated growth of symmetrical sentiment data across different platforms, experimenting with different sentiment analysis (SA) techniques allows for better decision-making and strategic planning for different sectors. Specifically, the emergence of COVID-19 has enriched the data of people’s opinions and feelings about medical products. In this paper, we analyze people’s sentiments about the products of a well-known e-commerce website named Alibaba.com. People’s sentiments are experimented with using a novel evolutionary approach by applying advanced pre-trained word embedding for word presentations and combining them with an evolutionary feature selection mechanism to classify these opinions into different levels of ratings. The proposed approach is based on harmony search algorithm and different classification techniques including random forest, k-nearest neighbor, AdaBoost, bagging, SVM, and REPtree to achieve competitive results with the least possible features. The experiments are conducted on five different datasets including medical gloves, hand sanitizer, medical oxygen, face masks, and a combination of all these datasets. The results show that the harmony search algorithm successfully reduced the number of features by 94.25%, 89.5%, 89.25%, 92.5%, and 84.25% for the medical glove, hand sanitizer, medical oxygen, face masks, and whole datasets, respectively, while keeping a competitive performance in terms of accuracy and root mean square error (RMSE) for the classification techniques and decreasing the computational time required for classification.

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