  Emerging interest of trading companies and hedge funds in mining social web
has created new avenues for intelligent systems that make use of public opinion
in driving investment decisions. It is well accepted that at high frequency
trading, investors are tracking memes rising up in microblogging forums to
count for the public behavior as an important feature while making short term
investment decisions. We investigate the complex relationship between tweet
board literature (like bullishness, volume, agreement etc) with the financial
market instruments (like volatility, trading volume and stock prices). We have
analyzed Twitter sentiments for more than 4 million tweets between June 2010
and July 2011 for DJIA, NASDAQ-100 and 11 other big cap technological stocks.
Our results show high correlation (upto 0.88 for returns) between stock prices
and twitter sentiments. Further, using Granger's Causality Analysis, we have
validated that the movement of stock prices and indices are greatly affected in
the short term by Twitter discussions. Finally, we have implemented Expert
Model Mining System (EMMS) to demonstrate that our forecasted returns give a
high value of R-square (0.952) with low Maximum Absolute Percentage Error
(MaxAPE) of 1.76% for Dow Jones Industrial Average (DJIA). We introduce a novel
way to make use of market monitoring elements derived from public mood to
retain a portfolio within limited risk state (highly improved hedging bets)
during typical market conditions.
