1. Stock Market Prediction Using Sentiment Detection C. LEE FANZILLI ADVISORS: PROF. DVORAK AND PROF. WEBB

2. Hypothesis  Can we use Twitter sentiment mentioning a stock in the NYSE to predict future returns of that stock?  Can we predict contemporaneous returns?  Do returns predict Twitter sentiment instead?

3. Background  People have tried many different ways of predicting prices in the market.  Technical Analysis is a methodology for forecasting the direction of prices through the study of past market data, primarily price and volume.  In Jan Larson’s paper he saw 300% gains on initial investment with this method (June 2010).

4. Challenges  Efficient Market Hypothesis states that the market is always at equilibrium.  Once we have a dataset, there is a fair amount of organizing and cleaning up to be done.  Not all data is useful data, and the data that is useful may not be sufficient enough to make a claim.

5. Data  For this experiment we collected daily stock price information on AMD, Google, and Apple from Yahoo Finance.  We retrieved a list of the top 101 tech tweeters from a Business Insider article to extract our tweets from.  Using Twitter’s API we created a corpus of tweets. DateOpenHighLowCloseVolumeAdj Close 2/24/2015530536.79528.25536.091002300536.09 2/23/2015536.05536.44529.41531.911448900531.91 2/20/2015543.13543.75535.8538.951440400538.95 2/19/2015538.04543.11538.01542.87986400542.87 2/18/2015541.4545.49537.51539.71447600539.7 Google Daily Price Information

6. Organization  We uploaded our Twitter data to CouchDB, an Apache database.  Next we pulled the date posted and text from tweets then separated them based on which of our stocks was mentioned.  Then wrote a script to score each tweet’s overall sentiment.

7. Sentiment Detection  Sentiment Detection, a form of textual analysis.  The university of Pittsburgh provides the MPQA corpus.  For a given dataset, we were able to calculate the total number of positive, negative, neutral, and true neutral tweets. Mean# Pos# Neg# Neutral # Balanced # True Neutral Total 1.194192622492256024724145735 Percentages16.16%39.22%44.64%4.31%40.33% Apple Stats

8. Example of Scoring Love @sunrise update - smoother calendar sync with GOOG apps and iPad app!1 @Simonkhalaf @BenedictEvans no doubt, apps are winning but I still have sense that GOOG can change trajectory1 Another protest against techies at 24th st-- google continues to be a rallying symbol for protestors http://t.co/NebJQ4pwrZ-2 The Beer Game -or- Why Apple Can't Build iPads in the US by @marksweep http://t.co/u2cl4Xne apple analyst releases analysis based on another apple analysts analysis0

9. Results  We ran linear regression models in RStudio.  Our results indicate that there is little to no correlation between sentiment and future returns.  But each case tends to vary. Our analysis on Google showed that sentiment was indeed significant.  Certain values can be explained by not enough data. Return t-1 Return t Return t+1 Intercept (AAPL) Intercept (AMD) Intercept (GOOG) t-value = 2.04 t-value = 0.497 t-value = 0.15 t-value = 1.80 t-value = 0.79 t-value = -0.37 t-value = 2.48 t-value = 0.99 t-value = 0.62 Sentiment (AAPL) Sentiment (AMD) Sentiment (GOOG) t-value = -0.52 t-value = 0.12 t-value = -0.09 t-value = 0.15 t-value = -1.02 t-value = 1.28 t-value = -0.22 t-value = -0.51 t-value = 2.61** R 2 (AAPL) R 2 (AMD) R 2 (GOOG) -0.000336 -0.01565 -0.00291 -0.000448 0.000789 0.001875 -0.000436 -0.01091 0.01745

10. Apple Graphs Future ReturnsReturns Predicting Sentiment Returns Tweet Sentiment

11. Google Graphs Future ReturnsReturns Predict Sentiment Tweet Sentiment Returns

12. AMD Graphs Future ReturnsReturns Predicting Sentiment Tweet Sentiment Returns Tweet Sentiment

13. Future Work  In the future we would take a look at indices in addition to individual stocks.  As well as a broader range of Twitter data, not just tech tweets  Rather than calculating return, we would also include the Cumulative Abnormal Return.  More Twitter data would have to be collected, many papers about similar experiences have millions of tweets not thousands.  Instead of using a linear regression model, we would consider using Support Vector Machines and other Machine Learning tools.

14. Works Cited  B. Wiithrich, D. Permunetilleke, S. Leung, V. Cho, J. Zhang, W. Lam, "Daily Prediction of Major Stock Indices from textual WWW Data", The Hong Kong University of Science and Technology  J. Bollen, H. Mao, X. J. Zeng, "Twitter mood predicts the stock market", School of Informatics and Computing, Indiana University-Bloomington, October 2010  J. I. Larsen, "Predicting Stock Prices Using Technical Analysis and Machine Learning", Masters in Computer Science, Norwegian University of Science and Technology, June 2010