Abstract:
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 be-
tween tweet board literature (like bullishness, volume, agreement etc) with the
nancial 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 tech-
nological stocks. Our results show high correlation (upto 0.88 for returns)
between stock prices and twitter sentiments. Further, using Granger's Causal-
ity Analysis, we have validated that the movement of stock prices and indices
are greatly a ected 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 Av-
erage (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.
