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

1. Deriving Trading Signals
from Google Trends and
Wikipedia Page Views
Thomas Wiecki

2. This is Joe.
He is worried about the debt ceiling.

3. What does he do?

5. After gathering information he calls
his broker.

6. Who sells all of his clients stock.

8. Stock market 101
The price is the result of the trading decisions
of many individuals.

9. Decision Making:
Multiple stages

10. Motivation
Quantify information gathering behavior that
precedes investment decisions.

11. ● Stock prices follow news.
● News can't be predicted ⇒ Random walk.
● However: Stocks do not follow random walk.
● What about bubbles?
● More and more research casting doubt...
Efficient Market Hypothesis

12. Quantitative Behavioral Finance
● Online chat activity predicts books sales [1]
● Blog sentiment analysis predicts movie sales
[2].
● Google search queries predict disease
infection and consumer spending [3].
● ⇒ News impact markets, but so does public
mood and sentiment.

13. Cognitive Bias: Loss Aversion

14. Subject of recent research

16. Simple investment strategy based
on Google search volume
for 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 week
if search_vol[t-1] < avg_search_vol:
long DJIA for one week

18. Quantopian Demo:
Google Trends
https://www.quantopian.com/posts/google-
search-terms-predict-market-movements

19. Top predictors

20. Bottom predictors

25. Quantopian Demo:
Wikipedia
https://www.quantopian.com/posts/deriving-
trading-signals-from-wikipedia-page-views-
new-feature

26. Twitter Sentiment
Analysis

27. Can Twitter move the market?

28. Cautionary Tale

29. ● Founded February 2011
● Closed after one month in service...
● However: return of 1.86% (beating the
market and average hedge fund)
Twitter Fund (Derwent Capital
Markets)

30. ● Preliminary evidence that information
gathering can be quantified and exploited.
● Quantopian - Reproducibility Science
● Mountains of data, waiting to be explored!
Departing thoughts...

31. Thanks!
Questions?
Contact:
● thomas@quantopian.com
● Twitter: @twiecki
● GitHub: twiecki

32. 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 online
chatter. (ACM, New York, NY, USA), pp. 78–87.
● Mishne, G & Glance, N. (2006) Predicting Movie Sales from Blogger Sentiment. AAAI 2006
Spring 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), Technical
report.
● Liu, Y, Huang, X, An, A, & Yu, X. (2007) ARSA: a sentiment-aware model for predicting sales
performance using blogs. (ACM, New York, NY, USA), pp. 607–614.