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Thursday, December 15, 2016

Social networks under stress

Romero et al. aim to analyze how an organization’s social network changes in structure and communications as responses to external shocks. The authors analyze the complete instant-message (IMs) communication history among employees of a hedge fund and with outside contacts. Using such information, they define structural, affective and cognitive properties of social networks. They find that, faced with the external shocks in the form of extreme price change, the network tends to turtle up instead of open up, favoring strong ties, high clustering and communications among company insiders. Besides, they also show that network structure is a better prediction than stock prices for important behavioral patterns, including affective and cognitive communications, local optimality of transactions, and the sudden execution of new stocks. This work reveals a strong relationship between network structure and collective behaviors within a social network.
A lot of researches have been done regarding static network structure, communities, influential nodes in networks as well as spreading dynamics in stable networks. However, little work has focused on how network responses to external shocks, through changing structure or communicative properties among nodes. The shock-related questions are critical to understanding a networked system’s capability to deal with uncertainty, or even extreme events.
1.     Network structure features. Subgraph G(s,d) is constructed by following internal IMs mentioning stock s on the day d. A larger subgraph G+(s,d) containing G(s,d) is built by considering both of internal and border IMs.
a.     Clustering coefficient
b.     Strength of ties
They make use of the historical communication records prior day d. For each node x, they can rank the set of nodes y with whom x has contacted before day d in descending order. They use the fraction of edges connecting the most frequently-contacted nodes in G(s,d) to evaluate whether G(s,d) favors strong ties or weak ties.
c.     Percentage of border edges
They define openness O(s,d) as the fraction of edges in G+(s,d) that are border edges.
Note that: measures can be normalized in relation to comparable quantities in data.
2.     Shock is defined as D(s,d) = [b(s,d)-a(s,d)]/a(s,d), where a(s,d) and b(s,d) is the opening and closing prices of stock s on day d; x-shock means that a stock’s price change on day d is higher than x while its price change was lower than x on the previous three days.
3.     OLS regression. In regression, they disaggregate the analysis on stock-by-stock and industry-by-industry basis, and they also include the fixed effect variable for day of the week.
4.     Linguistic Inquiry and Word Count (LIWC) dictionary. They employ LIWC to identify words in IMs that reflect affective and cognitive information of traders. Affect includes positive emotion, negative emotion, anxiety, anger and sadness; Cognition contains insight, causation, discrepancy, tentative, certainty, inhibition, inclusive, and exclusive.
5.     Prediction task. They use binary classifiers to predict whether (s,d) confirms one LIWC category using network structure features and the stock price changes as predictors. They say the pair (s,d) confirms one LIWC category C if the words from C are used at a higher rate on day d than typical rate for stock s.
6.     A buy transaction is locally suboptimal if the price of a stock on that transaction is higher than the maximal price on the following day. A sell transaction is locally suboptimal if the price on that transaction is lower than the minimal price on the following day.
7.     Prediction task one. They predict whether a transaction t(s,d) is locally optimal using network features and price changes as predictors.

8.     Prediction task two. They predict new transactions of stocks that have not been traded for a given period of time prior to the transaction day.

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