  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.
