key: cord-0913948-taqghha9
authors: Ahelegbey, Daniel Felix; Cerchiello, Paola; Scaramozzino, Roberta
title: Network based evidence of the financial impact of Covid-19 pandemic
date: 2022-03-21
journal: International Review of Financial Analysis
DOI: 10.1016/j.irfa.2022.102101
sha: fcc7ceee9ce718f1081c0527c5d8c244585143eb
doc_id: 913948
cord_uid: taqghha9

How much the largest worldwide companies, belonging to different sectors of the economy, are suffering from the pandemic? Are economic relations among them changing? In this paper, we address such issues by analysing the top 50 S&P companies by means of market and textual data. Our work proposes a network analysis model that combines such two types of information to highlight the connections among companies with the purpose of investigating the relationships before and during the pandemic crisis. In doing so, we leverage a large amount of textual data through the employment of a sentiment score which is coupled with standard market data. Our results show that the COVID-19 pandemic has largely affected the US productive system, however differently sector by sector and with more impact during the second wave compared to the first.

Covid-19 is not the first pandemic that the world has been experiencing but the conditions we are in, changed our lives permanently and have consequences in every field. If, from the social point of view, the generated change is visible, the impact triggered at the macroeconomic level needs some time to be appreciated and quantified. As never before, the "on-line" life is so intensive, the entire world has the urgency to communicate. From the perspective of the economic market, as information spreads out, the associated sentiments change and increase the impact on the market trends. In the era of social networks, the information moves instantaneously and can amplify or damper the dynamics of the financial markets. Not only purely financial information that has an impact on the economic trend, but the price is also more and more affected by the sentiment of people. The mechanisms involved are many, from the purely economic aspects to the sociological and psychological ones. This is the reason why, at the beginning of the 2000s, sentiment analysis has been developed and largely employed, involving different sectors from marketing to politics, passing through psychology and finance.

This paper focuses specifically on the latter and, indeed, it is well known that market prices originate from complex interaction mechanisms that often reflect speculative behaviours, rather than the fundamentals of the companies to which they refer. Market models and, specifically, financial network models based on market data may, therefore, 1 that the market reaction is delayed by 6-13 days after the information publication and that the link between these two actors is very high. Considering the statistical analysis of the twitter messages, Yin et al. (2020) analyzing 13 million of tweets for 2 weeks, noted a stronger ratio of positive sentiment than negative one with particular attention to some specific topics such as "staying at home". Rajput et al. (2020) , considering tweets from January 2020 until March 2020, show that most of the tweet are positive, only about 15% negative.

Considering the Italian stock market, Colladon et al. (2020) propose a new textual index (ERKs) able to predict stock market prices and demonstrate the improvement using a forecasting model. Mamaysky (2020) examining the financial markets, note that until mid-March 2020 the markets are hypersensitive, that is very volatile and overreacting to news. From mid-March on-wards, the markets show a structural break reducing largely the hypersensitive trait. Gormsen and Koijen (2020) , analyzing equity market and dividend futures, show how these move in response to investors' expectations of economic growth. They note that the programs implemented by governments have not improved growth expectations in the short term. Baker et al. (2020) , analyzing the previous pandemics (1918, 1957 and 1968) , show how the Covid-19 pandemic has unprecedented effects on the US market. The authors note that this is imputable to government restrictions on commercial activities and social distancing. The socioeconomic effects of Covid-19 on every aspect of the economy have been reviewed by Nicola et al. (2020b) and Zhang et al. (2020) map general risk patterns and systemic risks in markets around the world. We pay particular attention to the recent literature that has studied the impact of the pandemic on the US market with a specific focus on sub sectors specificity. Lee (2020) explored the correlation between sentiment score and 11 sector indices of the US Market through a set of t-test with different lags. Results demonstrate that all sectors present a significant boost in volatility due to the pandemic. Looking at correlation between Covid-19 sentiment and stock prices, they show that the link is different across sectors, in particular, consumer, industrial, energy and communication services are in the group of the high-medium level of correlation, utility sector in the low-level group, while tech and healthcare in the high, medium, and low group. The impact of Covid-19 was stressed also from Federal reserve in some notes. Chen et al. (2020) show the "disconnection" between stock market and real economy. High price stocks, in particular tech stocks (Facebook, Amazon, Apple, Netflix, and Google), have performed better throughout the pandemic while low price stocks performed worse, losing the 10% of their values pre-pandemic. Ahmed et al. (2020) analyze the impact of Covid-19 on Emerging Market Economies (EMEs) in particular relationship between pandemic outcomes and financial developments considering 22 financial indicators. They show that the access of EMEs to international capital markets is determined by the spread of the virus and in particular by the lockdown measures adopted to deal with it, rather than by the strength of their economies.

We studied the impact of Covid-19 on stocks' relationship through the application of network model. Boccaletti et al. (2006) review the structure of the networks and the ap-4 plications in the different fields. Related to the financial area, Pantaleo et al. (2011) build a network structure based on covariance estimators to improve the portfolio optimization. Peralta and Zareei (2016) propose a portfolio optimization strategy through network-based method in which the securities are the nodes of the network and the links are the correlations of returns. Pozzi et al. (2008) compare the stability of two graph methods: the Minimum Spanning Tree and the Planar Maximally Filtered Graph using financial data. Network models approach are commonly used in the field of systemic risk. Sheldon et al. (1998) , Upper and Worms (2004) , Eisenberg and Noe (2001) and in particular, frequently, are based on correlations between agents. There is a myriad of studies on the application of network models to uncover these vulnerabilities in financial systems to identify channels of shock transmission among financial institutions and markets (Acemoglu et al., 2015; Battiston et al., 2012; Billio et al., 2012; Cerchiello et al., 2017; Diebold and Yilmaz, 2014; Elliott et al., 2014; Nicola et al., 2020a) . Mantegna (1999) , studying daily time series, finds a hierarchical arrangement between them through the construction of a graph calculated on the correlations between each pair of actions. Onnela et al. (2004) construct a network using return correlations and explain the methodology for constructing asset graphs. Giudici and Abu-Hashish (2019) propose a correlation network VAR model to explain the structure between bitcoin prices and classic asset. Steinbacher et al. (2013) studied network-based model of credit contagion related to the banking system to analyze the effect of shocks to the financial system. Billio et al. (2012) construct a Granger-causality networks on hedge funds, banks, broker/dealers, and insurance companies showing that banks are the most important actor in transmitting shocks than others, Giudici and Spelta (2016) improve financial network model applying static and dynamic Bayesian graphical models. Ahelegbey et al. (2016a,b) proposed a Bayesian graphical VAR (BGVAR) model to identify channels of financial interconnectedness for systemic risk analysis. Bouri et al. (2018) applied the BGVAR model to examine the predictive power of implied volatility in the commodity and major developed stock markets. Souza and Aste (2019) demonstrate the predictability of future stock market using a network approach that combines textual information and financial data. propose a model for improving the credit risk of peer-to-peer platforms by exploiting the topological information embedded in similarity networks.

This work contributes to the literature specifically devoted to the assessment of the impact of the Covid-19 pandemic by means of network models. In particular, Shahzad et al. (2021a) recently analyzed the reactions of the US stock market. They extend the Diebold-Yilmaz spillover index to quantile analysis showing that the network of sectorial connections is solid but changes in reaction to unfavorable market shocks but also to favorable ones. In the case of the pandemic period, the structure of the network has undergone a change leading to a closer connection between the closest clusters. In a further work, Shahzad et al. (2021b) still focuses on the US market with a specific interest on the tourism sector. They analyze 95 US tourism companies through tail risk spillover analysis showing that the whole sector was negative affected by the crisis. However, they found an increase in the importance of the small tourism firms. In both cases, the analysis of the pandemic impact is achieved through network models. The main difference with our 5 work lies in the type of data as we consider explicitly public sentiment measured through textual data. Given the huge impact of the pandemic on people daily life and the high levels of uncertainty about the future and the evolution of the pandemic itself, we deem crucial to take directly into account opinions and sentiments. The impact of the crisis on the Chinese stock market is analyzed by Shahzad et al. (2021c) who examined the asymmetric volatility spillover among sectors using a VAR model. They highlight the sectors, such as industries, utilities, energies and materials, to which governments should pay more attention to maintain the stability of the Chinese stock market since they experience high volatility spillovers. From policy-making perspective, Bouri et al. (2021) analyzed the changes in the connections of the returns of assets such as gold, crude oil, USD index, bond index, and MSCI World index, during the epidemic. They show how the network is affected by the epidemic, in particular, MSCI World and USD index appear as primary transmitters of shock before the pandemic while the bond index appears primary transmitter during the Covid-19 period. These papers differ from our work in the choice of the modeling approach. The methodology used in the former is a VAR model to calculate the spillover asymmetry measure, while the latter apply a TVP-VAR by modeling the coefficients as a stochastic process. This work, instead applies the BGVAR model in Ahelegbey et al. (2016b Ahelegbey et al. ( , 2021 to analyze the intra-and inter-layer connectivity between return indexes and sentiments of different industries.

In this paper, we propose a network model to stress the relationship among companies in different sectors, considering the dynamic pre and during Covid-19 pandemic. Our study focuses on the top 50 world companies due to their important role in the entire world economy and also due to the amount of available textual information. We want to assess how much the largest worldwide companies are suffering from the pandemic and whether the relationship between them is changing. To answer these questions we build a network model that considers two sources of information: textual data from news and blog and financial stock prices. We decided to analyze separately the different sectors to stress in which sector the pandemic affects more and how.

Let R t denote the returns of the stock market indices of n institutions at time t, and S t denote the sentiment of the institutions. Let Y t = (R t , S t ) be a 2n × 1 vector whose dynamic evolution can be described by a VAR(Y t ) process:

where p is the lag order, B l is 2n × 2n matrix of coefficients with B ij|l measuring the effect of Y j,t−l on Y i,t , U t is a vector independent and identically normal residuals with covariance matrix Σ u , B 0 is a zero diagonal matrix where B ik (0) records the contemporaneous effect 6 of a shock to Y k on Y i , and ε t is a vector of orthogonalized disturbances with covariance matrix Σ ε . From (2), the Σ u can be expressed in terms of B 0 and Σ ε as

A network model is a convenient representation of the relationships among a set of variables. They are defined by nodes joined by a set of links, describing the statistical relationships between a pair of variables. The use of networks in VAR models helps to interpret the temporal and contemporaneous relationships in a multivariate time series. To analyze (1) and (2) through networks, we assign to each coefficient in B l a corresponding latent indicator in G l ∈ {0, 1} 2n×2n , such that for i, j = 1, . . . , n, and l = 0, 1, . . . , p:

where 1(G ij > 0) is the indicator function, i.e., unity if G ij > 0 and zero otherwise, A w R|R and A w R|S are sub-matrices of A w that measure the cumulative effect of R t−l and S t−l on R t for l = 0, . . . , p, respectively. The sub-matrices of A reports the following:

where R j → R i exist if there is a contemporaneous or lagged directed link from R j to R i . Similar reasoning holds for S k → R i , R j → S q , and S k → S q . Thus, A R|R specifies adjacency matrix of equity-to-equity connections, A R|S for sentiment-to-equity, A S|R for equityto-sentiment, and A S|S for sentiment-to-sentiment linkages. A w = (A w R|R , A w R|S , A w S|R , A w S|S ) specifies the weights of the relationship in A = (A R|R , A R|S , A S|R , A S|S ) obtained as a sum of the estimated contemporaneous and lagged coefficients. The correspondence between (G, B) and (A, A w ) is such that the former captures the short-run dynamics in Y t = (R t , S t ) while the latter can be viewed as long-term direct relationships when Y t = Y t−1 = . . . = J o u r n a l P r e -p r o o f Journal Pre-proof we apply the Bayesian graphical vector autoregressive (BGVAR) approach of Ahelegbey et al. (2016b) to separate and estimate the contemporaneous and lagged interactions associated with the VAR.We apply the BGVAR to study the intra-and inter-layer connectivity between return indexes and sentiments of different industries. We build on the collapsed Gibbs algorithm in Ahelegbey et al. (2016b) and Ahelegbey et al. (2021) by sampling the temporal dependence from its marginal distribution and the contemporaneous network from it's conditional distribution. We complete the Bayesian formulation with prior specification and posterior approximations to draw inference on the model parameters.

We specify the prior distributions of (p, G, B, Σ ε ) as follows:

wherep, η, π ij , δ, and S 0 are hyper-parameters. The specification for p is a discrete uniform prior on the set {1, . . . ,p}, 1 <p. The specification for B ij conditional on G ij follows a normal distribution with zero mean and variance η. Thus, relevant explanatory variables that predict a response variable must be associated with coefficients different from zero and the rest (representing not-relevant variables) are restricted to zero. We consider G ij as Bernoulli distributed with π ij as the prior probability. We assume Σ −1 ε is Wishart distributed with prior expectation 1 δ Λ 0 and δ > n the degrees of freedom parameter.

n matrix collection of all observations, and X = (X 1 , . . . , X N ) be an N × np matrix collection of lagged observations. We fixed p = 5 to allow us select the relevant variables in different equations of the system. Following the Bayesian framework of Geiger and Heckerman (2002), we integrate out the structural parameters analytically to obtain a marginal likelihood function over graphs. Following Ahelegbey and Giudici (2020), we approximate inference of the parameters via a collapsed Gibbs sampler such that the algorithm proceeds as follows:

1. Sample via a Metropolis-within-Gibbs [G 0 , G 1:p |Y, p] by (a) Sampling from the marginal distribution:

See Ahelegbey and Giudici (2020) for a detailed description of the network sampling algorithm and convergence diagnostics. For our empirical application, we set the hyper-parameters as follows: π ij = 0.5 (which leads to a uniform prior on the graph space), η = 100, δ = n + 2 and Λ 0 = δI n . We set the number of MCMC iterations to sample 200,000 graphs and we ensured that the convergence and mixing of the MCMC chains are tested via the potential scale reduction factor (PSRF) of Gelman and Rubin (1992) .

For our analysis, we focus on some of the most important American companies: the top 50 of the S&P. We obtain the daily stock prices of these companies from yahoo finance covering a period that ranges from August 2016 to November 17th, 2020. We also employ a sentiment index referred to the same companies and period produced by Brain 1 a research company .

Brain is a research company specialized in the production of alternative datasets and in the development of proprietary algorithms for investment strategies on financial markets. The Brain Sentiment Indicator dataset comprises of a daily sentiment indicator for the largest listed worldwide companies. Such indicator represents a score that ranges between -1 and +1 and is based on financial news and blogs written in English. Each news is preanalyzed to assign the corresponding company through the use of a dictionary of company names; then news are categorized using syntactic rules or machine learning classifiers. If this step fails a dictionary based approach is used.

The final dataset is composed of 1021 observations and 100 variables (for each company we have two columns: one for the closing market price and one for the sentiment score). The complete list of companies is available in represented in our dataset, in addition we decided to unify the two consumer categories thus obtaining 7 final groups. Table 2 reports the summary statistics of the first four moments (i.e., mean, standard deviation, skewness and excess kurtosis) of the equity returns and sentiment scores. The statistics show that almost all the equity returns and sentiment scores have a near-zero mean. The equity returns, however, appear more volatile than the sentiment scores. That is, the standard deviation of the equity returns are relatively higher (greater than 1) compared to that of the sentiment scores (less than 1). A greater majority of the returns and sentiments exhibit fairly symmetric behaviour, i.e., they are characterized mostly by small but consistent positive outcomes and, occasionally, large negative returns. The excess kurtosis of the sentiment scores are largely less than 3, which indicates that the sentiments J o u r n a l P r e -p r o o f data are approximately normal (via skewness-kurtosis summary), while that of the equity returns confirms the stylized facts of leptokurtic behavior of daily return series.

J o u r n a l P r e -p r o o f Journal Pre-proof

We apply the BGVAR estimation methodology to study the dynamics of interconnectedness among the top 50 of S&P companies and the sub-sectors via a two-and-half year (approximately 504 days) rolling window. Our choice of window size is motivated by the need to have enough data points to capture 24-months dynamic dependence among the companies. We set the increments between successive rolling windows to one month. The first window covers August 2016 -July 2018, followed by September 2016 -August 2018, and the last from December 2018 -November 2020. In total, we have 29 rolling windows.

To unify the dataset, we compute the daily returns as the log difference of successive daily adjusted close prices of the companies equities. Since stock prices are not recorded for weekends, we consider the weekend sentiment scores in the computation of the Monday sentiment via a simple mean of the three days. In this way, both R t and S t express time variations: in the equity price and in the sentiment scores, respectively, for each company.

We study the equity-sentiment interconnectedness of the top 50 of S&P companies by considering them jointly as well as sub-sectors separately. Following the sector division of the companies in Table 1 , we created five categories, namely: Consumer, Financial, Health Care, Tech and Miscellaneous and analyzed the interconnectedness for each sub-sector. Due to the low number of companies in the communication, energy and industrial sector, we combined them to create a unique sub-sector, which we refer to as "Miscellaneous".

We compare the sub-period networks, the pre-COVID-19 phase and the COVID-19 (Wave-1 and Wave-2) phase in terms of the number of links, the network density, the average degree, the clustering coefficient, and the average path length. We characterize, through numerical summaries, the time-varying nature of interconnections by monitoring the network density, average degree, clustering coefficient and average path length. In along the analyzed period. Two curves are compared, different in terms of employed lags, 1 vs 5. It clearly emerges the presence of two separated periods as of starting from late June 2020. The density increases by a factor of 6.5 -8 points. As the pandemic enters in the most hitting phase, all the connections increase greatly, meaning that the exogenous shock affects the entire system as a whole, increasing the vulnerability as well. Indeed, a more interconnected system amplifies more and more any impact through a contagion spreading mechanism (see Cerchiello and Giudici (2016b) ).

If we focus on the three periods of the data at hand (pre-pandemic, first wave, second wave), we can visualize the networks in Figure 2 and the metrics in Table 3 . If the difference between the pre-covid and the first wave is not so evident, we notice a change in the values of the number of links, the density, average degree and average path length during the second wave. This suggest that, although the system appears resilient as the first wave arrives, with the prolongation of the pandemic, companies can not stand any longer the shock. 

To further analyze the Covid-19 pandemic effects on the system, we split the analysis in the two components: equity data on one hand and sentiment data on the other. In particular we investigate what happens to the Equity to Equity connections, that is focusing only on the intra-equities layer linkages. As far as we are concerned with the equity market, Figure 3 and Table 4 report the results. In particular, Table 4 network along the three periods: similarly to previous results the financial market reacts more during the second wave. As we would have expected, there is a huge number of links which remains rather stable, confirming once again the deep interconnection of the financial market. In Figure 4 and Table 5 we report another set of results looking at the effect of equity markets on sentiments, that is capturing sentiment reactions to changes in financial market performance. In this case, the analysis considers a different perspective: the explored links are directed from equity market data to sentiment indicator. From a computational point of view the number of possible links is lower, as only one specific direction is explored. Table 5 : Statistics for sub-period Equity-to-Sentiment network before and during COVID-19 period.

The reader can immediately notice a number of relevant facts: the total number of links is much less. That is to say that the financial market has a lower impact on sentiments and this is consistent along the whole time horizon. However, there is a clear difference in the three periods: the pre-covid period recorded three times higher sentiment reactions to changes in financial market indexes than in the Covid-19 periods. We could say that the influence from the financial world to the public perception one has been frozen by the virus, lowering down largely the influence channel. Such apparently weird result can be explained by considering the type of shock affecting the system. The pandemic has a completely different nature, it is an exogenous diffused and pervasive shock which cannot be assimilated to other system perturbations like the financial ones. Modern populations and economies are not prepared or used to cope with so impacting restrictions and limitations of daily life. Results in Figure 4 and Table 5 and similarly in Figure 5 and Table 6 seem to suggest that the companies reacted by lowering down the interrelations, that is to say that the system was frozen and in attendance of the events. Companies became isolated entities waiting for a clearer evolution of the virus spread, blocking investments and activities planning and this is reflected in basically no correlations in either directions (equity to sentiment or sentiment to equity). Such phenomenon is even more evident if we consider the opposite direction of transmission: from sentiment to equity. Figure 5 and Table 6 However, it is important to stress that such first analysis was run on the whole dataset, with no distinction made on the business sectors and looking at only one influence direction at the time (either equity to sentiment or sentiment to equity). That is to say that possible peculiar behaviours can occur sector by sector as we are going to discuss in the following. Given the heterogeneity of the activities of the 50 companies at hand, it is relevant to deepen the analysis with regards to each specific sub-sector. Starting from the Financial sector, we notice from Figure 6 and Table 7 that all the indexes remains exactly the same.

Our analysis reveals that the linkage among the financial institutions revolve around their equity market performance with no effect from sentiments. Thus, the change in the networks structure that we have noticed in the previous tables, is not driven by the financial companies. We, however, notice that although the connections remain unchanged during the pre-covid and covid periods, the sign and magnitude of the interactions seems 16 to change over the sub-periods. More specifically, Citigroup (C) and Berkshire Hathaway (BRK.B) seem to exhibit bi-directional relationship through out all periods. However, the pre-covid reported an almost equally positive link. The first and second wave of the Covid, however, recorded a positive impact of C on BRK.B, and a negative reverse impact of BRK.B on C. A look at the centrality of the network in terms of Hub and Authority scores (in Table 8 ) shows that of the 5 companies, Citigroup was central to risk transmission during the pre-covid period, while JPM dominate in the Covid period.

In analyzing the Consumer sub-sector, Figure 7 shows the resulting network structure over the three sub-periods. Unlike, the Financial sub-sector, the Consumer sub-sector network record links are at all levels: equity-equity, equity-sentiment, sentiment-equity. For instance, the sentiment associated with Netflix (S_NFLX) react strongly positive to the equity performance of Netflix (NFLX) during the pre-covid, which reduced slightly in the first wave of the Covid but varnished in the second wave. We also observe a reaction from Netflix (NFLX) to the sentiment associated with Philip Morris Int. (S_PM). From Table 9 : Statistics for sub-period Consumer sub-sector network before and during COVID-19 period. Table 9 , we notice a slight variation in the metrics of the second wave Consumer subsector network. In particular the clustering coefficient increases and the average path length decreases. Table 10 confirms the different behaviour of the consumer sub-sector: the hub companies during the pandemic change and increase in coefficient magnitude.

The consumer system appears less resilient in comparison to the financial one. McDonalds, which is not in the top 5 hubs before the pandemics, not only appears all of a sudden, but it is also first ranked. Also Comcast Corp. and Amazon enter the ranking. The Health-Care sub-sector network, represented in Figure 8 and to consumer sub-sector, the links in Figure Table 12 tend to change, not only the rankings, but also the relevant companies. This suggest that the pandemic has deeply affected the health care sub-sector, as it is plausible to expect. Figure 9 and Table 13 reports the network structure and its summary statistics for the Tech sector over the three sub-periods. What immediately emerges is the presence of much more connected networks regardless the period. The indexes are coherent and decrease as the periods pass by. Table 14 confirms the change in the network structure: in particular two new players in the pandemic, namely Apple Inc. and Adobe Inc. for the hub score and Broadcom Inc. and Alphabet Inc. (Google) for the authority score.

The result of the miscellaneous sector which comprises Industrial, Communication and Energy companies are reported in Figure 10 , Tables 15 and 16. We observe that similar to the Financial sub-sector, network among the group of companies in the miscellaneous sector is centered around the equity market performance, except for a links depicting the policy makers. It is well know in the literature (Nicola et al. (2020a) ,Cerchiello and Giudici (2016b) Cerchiello et al. (2017) ) that the interconnections among actors belonging to an economic system play a crucial role during turbulence and crisis phases. A strongly interconnected network of companies can be considered either more resilient or vulnerable to shocks according to such events nature. In periods of financial crisis, arising from both real economy or financial markets, a high degree of interconnections can exert an extremely impacting systemic risk, which can cause a collapse of the whole sector given the strong dependencies among the actors. On the contrary, exogenous diffused shocks, not originally related to financial causes like the pandemic ones, can be much more impacting on poorly interconnected systems in which isolated companies can likely experience lack of aid from economic and sector peers increasing their probability of failure. Nevertheless, crisis not induced by real economy or equity markets can trigger very quickly consequent financial crisis, making even more difficult the evaluation of the optimal interconnections degree of a sector. That said, it is evident the reason why the monitoring and assessment of the levels of interconnection of the economic sectors should represent one of the main concern for regulators and supervisors. In case of downturns, it is important to quantify and monitor the level of vulnerability of the systems. To the same aim, the clear identification of pivotal actors (measured through hubs and authorities scores) can help in avoiding the activation of domino effects by supplying financial aids or cutting down connections. Moreover, the breakdown of the analysis in waves, is not only interesting from a descriptive point of view but it can represent a useful monitoring tool for non economically driven crisis like the pandemics. As the virus spreading moves on, the crisis becomes progressively and rapidly double-edged: epidemic and economic. Lastly, by directly leveraging also on the public sentiment, we account for the moods and perceptions of populations rather than only for speculators and investors. Such implementation allows for a more comprehensive and holistic view of the economic status, putting policy makers in an informed and aware framework.

The Covid-19 pandemic has deeply affected the population and all the relative activities. Health impact, social restrictions, economic downturn, overall instability are all direct consequences of the spread of the virus. Researchers worldwide have focused on studying, measuring and assessing such consequences at the different levels. In this paper we cope with the analysis of the economic impact of the pandemic, looking at the US top 50 companies of S&P market. In particular we employ advanced network models able to leverage the temporal-dynamic dimension of the phenomenon through a novel specification of a Bayesian graphical vector autoregressive (BGVAR) approach. Moreover, we do not only rely on market data but emphasize the population perception and opinions by adding to the analysis a sentiment index built upon blogs and regular news. The analysis has revealed several interesting findings. First of all, the American top 50 companies market appears rather resilient as the first wave arrives but it is not able to stand the second one. The shock hits the whole system, increasing the interconnections and consequently the associated system risk. However the sub-sectors, which the 50 companies belong to, show different reactions, fully dependant on the involved type of business. The Financial sector shows a particular resilience since all the indexes remains exactly the same. The linkage among the financial institutions revolve around their equity market performance with no effect from sentiments. Differently from the financial sector, the consumer one witnesses the strong interconnection between the equity and the sentiment components. 22

Moreover, we notice clear signs of reactions as the pandemic moves on. The Health-Care sector is, as we would expect, affected by the instability induced by the pandemic. There is no a clear common pattern in the evolution of the networks, but it definitely reacts to the turbulence especially if we look at the most important hubs and authorities. Regarding the big Tech we obtain much more connected networks regardless the period. It is interesting to notice two new central players in the pandemic, namely Apple Inc. and Adobe Inc. for the hub score and Broadcom Inc. and Google for the authority score. Moreover, We contribute to the ongoing discussions on the spillover effects of news and investors sentiment on equity returns in financial markets and interconnectedness among sectors. Some of our findings are as follows: from the equity-sentiment nexus there is evidence of more equity-to-sentiment pre-Covid than during the Covid-19 pandemic outbreak. There is more sentiment-to-equity pre-Covid-19 than during the Covid-19 pandemic outbreak, before Covid-19, more sentiment-to-equity influence than equity-to-sentiment. Finally, during the Covid-19 pandemic, more equity-to-sentiment than sentiment-to-equity influence. For what concerns the sectoral interconnectedness, we found that there is no significant difference in total interconnectedness among Financial, Consumer and Miscellaneous sub-sectors. A drop in interconnectedness among Health and Tech sub-sectors, but with a much closely connected community and faster rate of shock propagation in COVID-19 Wave-1 and Wave-2, respectively. Further improvement of this study would consider up to date data, as the pandemic keeps on hitting the whole system. Indeed, the recent start of the vaccination campaign would be a further variable of interest that for sure would impact, not only the virus diffusion, but also the renovate confidence of the economic sectors and the population sentiment. Moreover, an analogous study with comparative purposes would be extremely useful on top 50 European companies.

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Word frequency and sentiment analysis of twitter messages during coronavirus pandemic

The effects of twitter sentiment on stock price returns

Impact of the covid-19 outbreak on the us equity sectors: Evidence from quantile return spillovers

From pandemic to systemic risk: contagion in the us tourism sector

Asymmetric volatility spillover among chinese sectors during covid-19

Interbank lending and systemic risk: An empirical analysis for switzerland

Predicting future stock market structure by combining social and financial network information

Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry

Credit contagion in financial markets: A networkbased approach

Giving Content to Investor Sentiment: The Role of Media in the Stock Market

More than Words: Quantifying Language to Measure Firms' Fundamentals

Estimating bilateral exposures in the german interbank market: Is there a danger of contagion?

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Detecting topic and sentiment dynamics due to covid-19 pandemic using social media

Financial markets under the global pandemic of covid-19