%0 Generic %1 Mao2011 %A Mao, Huina %A Counts, Scott %A Bollen, Johan %D 2011 %K prediction social_media %T Predicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Data %U http://arxiv.org/PS_cache/arxiv/pdf/1112/1112.1051v1.pdf %X Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from factors that are actually involved in the apparent relation between online sentiment and market values. In this paper, we survey a range of online data sets (Twitter feeds, news headlines, and volumes of Google search queries) and sentiment tracking methods (Twitter Investor Sentiment, Negative News Sentiment and Tweet & Google Search volumes of financial terms), and compare their value for financial prediction of market indices such as the Dow Jones Industrial Average, trading volumes, and market volatility (VIX), as well as gold prices. We also compare the predictive power of traditional investor sentiment survey data, i.e. Investor Intelligence and Daily Sentiment Index, against those of the mentioned set of online sentiment indicators. Our results show that traditional surveys of Investor Intelligence are lagging indicators of the financial markets. However, weekly Google Insight Search volumes on financial search queries do have predictive value. An indicator of Twitter Investor Sentiment and the frequency of occurrence of financial terms on Twitter in the previous 1-2 days are also found to be very statistically significant predictors of daily market log return. Survey sentiment indicators are however found not to be statistically significant predictors of financial market values, once we control for all other mood indicators as well as the VIX.

@misc{Mao2011, abstract = { Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from factors that are actually involved in the apparent relation between online sentiment and market values. In this paper, we survey a range of online data sets (Twitter feeds, news headlines, and volumes of Google search queries) and sentiment tracking methods (Twitter Investor Sentiment, Negative News Sentiment and Tweet & Google Search volumes of financial terms), and compare their value for financial prediction of market indices such as the Dow Jones Industrial Average, trading volumes, and market volatility (VIX), as well as gold prices. We also compare the predictive power of traditional investor sentiment survey data, i.e. Investor Intelligence and Daily Sentiment Index, against those of the mentioned set of online sentiment indicators. Our results show that traditional surveys of Investor Intelligence are lagging indicators of the financial markets. However, weekly Google Insight Search volumes on financial search queries do have predictive value. An indicator of Twitter Investor Sentiment and the frequency of occurrence of financial terms on Twitter in the previous 1-2 days are also found to be very statistically significant predictors of daily market log return. Survey sentiment indicators are however found not to be statistically significant predictors of financial market values, once we control for all other mood indicators as well as the VIX. }, added-at = {2011-12-06T11:55:36.000+0100}, author = {Mao, Huina and Counts, Scott and Bollen, Johan}, biburl = {https://www.bibsonomy.org/bibtex/21b485857225ba13c4903534c89a96d47/maxirichter}, description = {Predicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Data}, interhash = {a435f4f80cc37b4283fbdbac92ae5665}, intrahash = {1b485857225ba13c4903534c89a96d47}, keywords = {prediction social_media}, note = {cite arxiv:1112.1051Comment: This paper includes 10 pages, 6 figures and 10 tables}, timestamp = {2012-01-16T12:18:19.000+0100}, title = {Predicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Data}, url = {http://arxiv.org/PS_cache/arxiv/pdf/1112/1112.1051v1.pdf}, year = 2011 }