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Deriving Trading Signals from Google Trends and Wikipedia Page Views


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This was a talk I gave at the meet-up in Boston and NYC.

Published in: Economy & Finance, Business

Deriving Trading Signals from Google Trends and Wikipedia Page Views

  1. 1. Deriving Trading Signalsfrom Google Trends andWikipedia Page ViewsThomas Wiecki
  2. 2. This is Joe.He is worried about the debt ceiling.
  3. 3. What does he do?
  4. 4. After gathering information he callshis broker.
  5. 5. Who sells all of his clients stock.
  6. 6. Stock market 101The price is the result of the trading decisionsof many individuals.
  7. 7. Decision Making:Multiple stages
  8. 8. MotivationQuantify information gathering behavior thatprecedes investment decisions.
  9. 9. ● Stock prices follow news.● News cant be predicted ⇒ Random walk.● However: Stocks do not follow random walk.● What about bubbles?● More and more research casting doubt...Efficient Market Hypothesis
  10. 10. Quantitative Behavioral Finance● Online chat activity predicts books sales [1]● Blog sentiment analysis predicts movie sales[2].● Google search queries predict diseaseinfection and consumer spending [3].● ⇒ News impact markets, but so does publicmood and sentiment.
  11. 11. Cognitive Bias: Loss Aversion
  12. 12. Subject of recent research
  13. 13. Simple investment strategy basedon Google search volumefor t in [1:T]:avg_search_vol = mean(search_vol[t-2:t-5])if search_vol[t-1] > avg_search_vol:short DJIA for one weekif search_vol[t-1] < avg_search_vol:long DJIA for one week
  14. 14. Quantopian Demo:Google Trends
  15. 15. Top predictors
  16. 16. Bottom predictors
  17. 17. Quantopian Demo:Wikipedia
  18. 18. Twitter SentimentAnalysis
  19. 19. Can Twitter move the market?
  20. 20. Cautionary Tale
  21. 21. ● Founded February 2011● Closed after one month in service...● However: return of 1.86% (beating themarket and average hedge fund)Twitter Fund (Derwent CapitalMarkets)
  22. 22. ● Preliminary evidence that informationgathering can be quantified and exploited.● Quantopian - Reproducibility Science● Mountains of data, waiting to be explored!Departing thoughts...
  23. 23. Thanks!Questions?Contact:●● Twitter: @twiecki● GitHub: twiecki
  24. 24. Image sources and references●●●●●● Gilbert, E & Karahalios, K. (2010) Widespread worry and the stock market.● [11] Gruhl, D, Guha, R, Kumar, R, Novak, J, & Tomkins, A. (2005) The predictive power of onlinechatter. (ACM, New York, NY, USA), pp. 78–87.● Mishne, G & Glance, N. (2006) Predicting Movie Sales from Blogger Sentiment. AAAI 2006Spring Symposium on Computational Approaches to Analysing Weblogs● S. Asur and B. A. Huberman 2010 Predicting the Future with Social Media arXiv:1003.5699v1● Choi, H & Varian, H. (2009) Predicting the present with google trends., (Google), Technicalreport.● Liu, Y, Huang, X, An, A, & Yu, X. (2007) ARSA: a sentiment-aware model for predicting salesperformance using blogs. (ACM, New York, NY, USA), pp. 607–614.