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Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty.

Casnici N, Dondio P, Casarin R, Squazzoni F - PLoS ONE (2015)

Bottom Line: By measuring the investors' expertise, we found that their behaviour could help predict changes in daily stock returns.We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum's reaction after bad news.Our findings confirm the crucial role of e-communication platforms.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.

ABSTRACT
This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors' network structure by measuring the investors' communication patterns. Our results showed that the investors' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum's reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.

No MeSH data available.


Related in: MedlinePlus

The communication network during the three volatility regimes.Initial period: day 31/01/2006 (a). Low volatility: day 02/01/2012 (b). High volatility: day 09/01/2012 (c).
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pone.0133712.g006: The communication network during the three volatility regimes.Initial period: day 31/01/2006 (a). Low volatility: day 02/01/2012 (b). High volatility: day 09/01/2012 (c).

Mentions: Fig 6 shows the forum network during the three volatility regimes, i.e., the initial period, low and high market volatility respectively. We selected these three days as important market news was released during them. These snapshots were taken given that the stronger the link between two investors, the thicker the graph’s ties would be. The node size was proportional to the investor’s in-degree. The difference of node colour indicates a different modularity class. The red nodes represented the most competent investors as calculated by our model. Results confirmed that the forum was more active during periods of higher volatility and that more competent investors were more present on the forum during high volatility phases.


Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty.

Casnici N, Dondio P, Casarin R, Squazzoni F - PLoS ONE (2015)

The communication network during the three volatility regimes.Initial period: day 31/01/2006 (a). Low volatility: day 02/01/2012 (b). High volatility: day 09/01/2012 (c).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526688&req=5

pone.0133712.g006: The communication network during the three volatility regimes.Initial period: day 31/01/2006 (a). Low volatility: day 02/01/2012 (b). High volatility: day 09/01/2012 (c).
Mentions: Fig 6 shows the forum network during the three volatility regimes, i.e., the initial period, low and high market volatility respectively. We selected these three days as important market news was released during them. These snapshots were taken given that the stronger the link between two investors, the thicker the graph’s ties would be. The node size was proportional to the investor’s in-degree. The difference of node colour indicates a different modularity class. The red nodes represented the most competent investors as calculated by our model. Results confirmed that the forum was more active during periods of higher volatility and that more competent investors were more present on the forum during high volatility phases.

Bottom Line: By measuring the investors' expertise, we found that their behaviour could help predict changes in daily stock returns.We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum's reaction after bad news.Our findings confirm the crucial role of e-communication platforms.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.

ABSTRACT
This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors' network structure by measuring the investors' communication patterns. Our results showed that the investors' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum's reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.

No MeSH data available.


Related in: MedlinePlus