Date and LocationMar 11, 2019 - 1:00pm to 2:00pm
Twitter Sentiment Analysis: How to Hedge Your Bets in the Stock Markets (presented by James Bird, Computer Science)
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
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.
Statistical analysis of fMRI data (presented by Sikun Lin, Computer Science)
Ashby, F. G. (2011). Statistical analysis of fMRI data. MIT press.
Sikun's presentation on Granger causality and VAR applied in neuroscience will draw primarily from Chapter 9 of Ashby’s Statistical analysis of fMRI data. In addition, she will briefly mention a series interesting papers (http://www.jneurosci.org/content/35/8/3293, https://www.pnas.org/content/114/34/E7063, https://www.sciencedirect.com/science/article/pii/S1053811918304932) that are arguing whether it is valid for neuroscience to use G-causality.