1. Twitter Volume Spikes: Analysis and Application in Stock Trading Yuexin Mao, Wei Wei and Bing Wang COMP4332/RMBI4310 CHAN Chun Ting (20127086)

2. Agenda 1 Background 2 Methodology 3 Twitter Volume Spikes Analysis 4 Application in Stock Trading 5 Conclusion & Future Work 6 Q & A

3. Background Twitter: Online social media Researches on Twitter: - Detect and predict real-world events Researches on Twitter: - Box-office revenues of movies Stock Trading: Hot topic on Twitter

4. Methodology Stock Market Data Twitter Data Twitter Volume Spikes

5. 1. Stock Market Data Yahoo! Finance 500 stocks in the S&P 500 index Stock daily closing price Stock daily high price Stock daily low price Stock daily trading volume

6. 2. Twitter Data From February 21, 2012 to May 31, 2013, over 15 months Tweets having a dollar sign before a stock symbol

7. 3. Twitter Volume Spikes Stocks with average daily number of tweets larger than 10 Twitter Volume Spikes: At least 3 times the average number of tweets in the past 70 days

8. Twitter Volume Spikes Analysis When do the spikes occur? Are they expected? Possible causes of the spikes

9. 1. When do Twitter volume spikes occur? Number of tweets for a stock spikes around the earnings dates 79.2% of earning days are surrounded by a spike 46.4% of the spikes coincide with earnings days Conclusion: Significant fraction of Twitter volume spikes happen around earnings days

10. 2. Are Twitter volume spikes expected? Scheduled events: Expected Twitter volume spikes Implied volatility is larger than usual before the spike happens, and returns back to the usual status after the spike happens Obtain Data: Weighted average of implied volatilities of all options of stock

11. 2. Twitter volume spikes expected? (Cont’d) Short-term option: in 30 days Long-term option: in 30 to 60 days Comparison: randomly chosen day Conclusion: Significant % of Twitter volume spikes are expected

12. 2. Twitter volume spikes expected? (Cont’d) p-value of t-test

13. 3. Possible causes of Twitter volume spikes (i) Stock breakout point (ii) Intra-day price change rate (iii) Inter-day price change rate (iv) Earnings day (v) Stock option implied volatility

14. Application in Stock Trading Strategy based on Bayesian Classifier Enhanced Strategy 1 2

15. 1. Strategy based on Bayesian Classifier Using a set of features to predict the probability event G happens Training Set: from February 21, 2012 to October 19, 2012 Test Set: from October 20, 2012 to March 31, 2013

16. Bayesian Classifier (cont'd)

17. Lead to substantial profit Average gain: 9.6% when τ = 54 Outperform the random strategy Drawback: X the trend of a stock

18. 2. Enhanced Strategy

19. Enhanced Strategy (cont’d)

20. Achieve significant gain Average gain: 15% when τ = 55 Outperform the random strategy Conclusion: Bottom Picking Method + Bayesian Classifier

21. Conclusion Twitter volume spikes related to S&P 500 stocks Correlation analysis: Twitter volume spikes occur Possible causes of these spikes Two trading strategies: Bayesian Classifier + Bottom Picking Method

22. Future Work Improve the trading strategies (1) more sophisticated Twitter volume spike metric e.g. based on the number of users (2) more features to the Bayesian Classifier (3) learning λ from stock data instead of using a fixed λ

23. Q & A