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 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 and validate performance of market monitoring elements derived from public mood that can be exploited to retain a portfolio within limited risk state during typical market conditions.
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Notes
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Twitter API is easily accessible through an easy documentation available at—https://dev.twitter.com/docs Also Gnip—http://gnip.com/twitter, the premium platform available for purchasing public firehose of tweets has many investors as financial customers researching in the area.
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lag at k for any parameter M at \(x_{t}\) week is the value of the parameter prior to \(x_{t-k}\) week. For example, value of returns for the month of April, at the lag of one month will be \(return_{april-1}\) which will be \(return_{march}.\)
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The reason behind purchase of long put options at different time intervals is because in a fully hedged portfolio, profit arrow has lower slope as compared to partially hedged portfolio (refer P/L graph). Thus the trade off between risk and security has to be carefully played keeping in mind the precise market conditions.
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Rao, T., Srivastava, S. (2014). Twitter Sentiment Analysis: How to Hedge Your Bets in the Stock Markets. In: Can, F., Özyer, T., Polat, F. (eds) State of the Art Applications of Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-05912-9_11
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