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Stock Market Prediction without Sentiment Analysis Using a Web-traffic based Classifier and User-level Analysis


Stock Market Prediction withou

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Abstract


This paper provides further evidence on the predictive power of online community traffic with regard to stock prices. Using the largest dataset to date, spanning 8 years and almost the complete set of SP500 stocks, we train a classifier using a set of features entirely extracted from web-traffic data of financial online communities. The classifier is shown to outperform the predictive power of a baseline classifier solely based on price time-series, and to have similar performances as the classifier built considering price and traffic features together. The best predictive performances are achieved when information about stock capitalization is coupled with long-term and midterm web traffic levels. In the second part of the paper we show how there exists a group of users whose traffic patterns constantly outperform the other users in predictive capacity. The findings set interesting future works in the definition of novel market indicators for market analysis.

KeyWords
predictive,traffic,classifier,Stock.



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