Cohesiveness in financial news and its relation to market volatility
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hesiveness in Financial News and its
Relation to Market Volatility
Matija Pisˇkorec1, Nino Antulov-Fantulin1, Petra Kralj Novak2, Igor Mozeticˇ2, Miha Grcˇar2, Irena Vodenska3 & Tomislav Sˇmuc1
1Laboratory for Information Systems, Division of Electronics, Rud–er Bosˇkovic´ Institute, Croatia, 2Department of Knowledge Technologies, Jozˇef Stefan Institute, Slovenia, 3Department of Administrative Sciences, Metropolitan College, Boston University, USA.
Motivated by recent financial crises, significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said regarding the influence of financial news on financial markets. We propose a novel measure of collective behaviour based on financial news on the Web, the News Cohesiveness Index (NCI), and we demonstrate that the index can be used as a financial market volatility indicator. We evaluate the NCI using financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and finance-related news. We hypothesise that strong cohesion in financial news reflects movements in the financial markets. Our results indicate that cohesiveness in financial news is highly correlated with and driven by volatility in financial markets.
T he exponential growth of online media, expansion of communication and mobility-tracking capabilities
have spawned research regarding the utility of the big data available from these sources. Big-data analytics aims to provide tools for better understanding large techno-social systems1,2, improve predictions of differ- ent socio-economic outcomes and optimise processes. For example, Gonzales et al.3 use 100,000 trajectories of mobile phone users to explain human mobility patterns. Ginsberg et al.4 use Google search queries to help detect outbreaks of influenza epidemics in areas with a large population of web-search users. Whereas the aforemen- tioned work estimates the current state of disease spread, other works focus on the predictive value of online information. For example, Goel et al.5 demonstrate that Google search query volumes significantly improve predictions for the revenue of featured movies, video game sales and rank of songs. Similar to the above studies, our work explores the relationship between large corpora of online news and financial markets.
In this context, previous studies have analysed the relationship of search query volumes of specific terms with movements in financial markets of related items6. Bordino et al.7 demonstrate that daily trading volumes of stocks traded on the NASDAQ 100 are correlated with the daily volumes of Yahoo queries related to the same stocks and that query volumes can anticipate peaks of trading by one or more days. Dimpfl et al.8 report that Internet search queries for the term ‘‘dow’’ obtained from Google Trends can help predict the Dow Jones Industrial Average (DJIA) realised volatility. Vlastakis et al.9 study information demand and supply using Google Trends at the company and market level for 30 of the largest stocks traded on the NYSE and NASDAQ 100. Chauvet et al.10 devise an index of investor distress in the housing market, the housing distress index (HDI), which is alsobased on Google search query data. Preis et al.11 demonstrate how Google Trends data can be used to design a market strategy or define a future orientation index12.
In principle, different effects between information sources and financial markets are expected when
Relation to Market Volatility
Matija Pisˇkorec1, Nino Antulov-Fantulin1, Petra Kralj Novak2, Igor Mozeticˇ2, Miha Grcˇar2, Irena Vodenska3 & Tomislav Sˇmuc1
1Laboratory for Information Systems, Division of Electronics, Rud–er Bosˇkovic´ Institute, Croatia, 2Department of Knowledge Technologies, Jozˇef Stefan Institute, Slovenia, 3Department of Administrative Sciences, Metropolitan College, Boston University, USA.
Motivated by recent financial crises, significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said regarding the influence of financial news on financial markets. We propose a novel measure of collective behaviour based on financial news on the Web, the News Cohesiveness Index (NCI), and we demonstrate that the index can be used as a financial market volatility indicator. We evaluate the NCI using financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and finance-related news. We hypothesise that strong cohesion in financial news reflects movements in the financial markets. Our results indicate that cohesiveness in financial news is highly correlated with and driven by volatility in financial markets.
T he exponential growth of online media, expansion of communication and mobility-tracking capabilities
have spawned research regarding the utility of the big data available from these sources. Big-data analytics aims to provide tools for better understanding large techno-social systems1,2, improve predictions of differ- ent socio-economic outcomes and optimise processes. For example, Gonzales et al.3 use 100,000 trajectories of mobile phone users to explain human mobility patterns. Ginsberg et al.4 use Google search queries to help detect outbreaks of influenza epidemics in areas with a large population of web-search users. Whereas the aforemen- tioned work estimates the current state of disease spread, other works focus on the predictive value of online information. For example, Goel et al.5 demonstrate that Google search query volumes significantly improve predictions for the revenue of featured movies, video game sales and rank of songs. Similar to the above studies, our work explores the relationship between large corpora of online news and financial markets.
In this context, previous studies have analysed the relationship of search query volumes of specific terms with movements in financial markets of related items6. Bordino et al.7 demonstrate that daily trading volumes of stocks traded on the NASDAQ 100 are correlated with the daily volumes of Yahoo queries related to the same stocks and that query volumes can anticipate peaks of trading by one or more days. Dimpfl et al.8 report that Internet search queries for the term ‘‘dow’’ obtained from Google Trends can help predict the Dow Jones Industrial Average (DJIA) realised volatility. Vlastakis et al.9 study information demand and supply using Google Trends at the company and market level for 30 of the largest stocks traded on the NYSE and NASDAQ 100. Chauvet et al.10 devise an index of investor distress in the housing market, the housing distress index (HDI), which is alsobased on Google search query data. Preis et al.11 demonstrate how Google Trends data can be used to design a market strategy or define a future orientation index12.
In principle, different effects between information sources and financial markets are expected when
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