Deriving Trading Signals from Google Trends and Wikipedia Page Views
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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 http://www.Quantopian.com meet-up in Boston and NYC.

This was a talk I gave at the http://www.Quantopian.com meet-up in Boston and NYC.

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Deriving Trading Signals from Google Trends and Wikipedia Page Views Deriving Trading Signals from Google Trends and Wikipedia Page Views Presentation Transcript

  • Deriving Trading Signalsfrom Google Trends andWikipedia Page ViewsThomas Wiecki
  • This is Joe.He is worried about the debt ceiling.
  • What does he do?
  • After gathering information he callshis broker.
  • Who sells all of his clients stock.
  • Stock market 101The price is the result of the trading decisionsof many individuals.
  • Decision Making:Multiple stages
  • MotivationQuantify information gathering behavior thatprecedes investment decisions.
  • ● 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
  • 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.
  • Cognitive Bias: Loss Aversion
  • Subject of recent research
  • 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
  • Quantopian Demo:Google Trendshttps://www.quantopian.com/posts/google-search-terms-predict-market-movements
  • Top predictors
  • Bottom predictors
  • Quantopian Demo:Wikipediahttps://www.quantopian.com/posts/deriving-trading-signals-from-wikipedia-page-views-new-feature
  • Twitter SentimentAnalysis
  • Can Twitter move the market?
  • Cautionary Tale
  • ● Founded February 2011● Closed after one month in service...● However: return of 1.86% (beating themarket and average hedge fund)Twitter Fund (Derwent CapitalMarkets)
  • ● Preliminary evidence that informationgathering can be quantified and exploited.● Quantopian - Reproducibility Science● Mountains of data, waiting to be explored!Departing thoughts...
  • Thanks!Questions?Contact:● thomas@quantopian.com● Twitter: @twiecki● GitHub: twiecki
  • Image sources and references● http://www.ng.all.biz/img/ng/service_catalog/502.jpeg● http://www.123rf.com/photo_10037927_businessman-or-stock-broker-with-cellphone.html● http://www.financetwitter.com/wp-content/uploads/2011/08/SP500_Crash_4Aug2011.jpg● http://lydiakimblesellsvegas.com/images/buy-sell-keyboard.jpg● http://venturebeat.com/2012/05/28/twitter-fueled-hedge-fund-bit-the-dust-but-it-actually-worked/● 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.