Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics

G. Ranco, I. Bordino, G. Bormetti, G. Caldarelli, F. Lillo, M. Treccani

PLoS ONE (01/16)

#sentiment#economics#socialnetwork

LQ placeholderComplementary of the cumulative distribution function of the number of clicks a news receives for the ten assets.

Complementary of the cumulative distribution function of the number of clicks a news receives for the ten assets.

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

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Online reading behavior predicts stock movements

Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics

Science News, 2016-01-25

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Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics

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