deriving trading signals from google trends and wikipedia page views
DESCRIPTION
This was a talk I gave at the http://www.Quantopian.com meet-up in Boston and NYC.TRANSCRIPT
Deriving Trading Signals from Google Trends and
Wikipedia Page ViewsThomas Wiecki
This is Joe.He is worried about the debt ceiling.
What does he do?
After gathering information he calls his broker.
Who sells all of his clients stock.
Stock market 101
The price is the result of the trading decisions of many individuals.
Decision Making:Multiple stages
Motivation
Quantify information gathering behavior that precedes investment decisions.
● 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
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.
Cognitive Bias: Loss Aversion
Subject of recent research
Simple investment strategy based on 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 week
if search_vol[t-1] < avg_search_vol:
long DJIA for one week
Quantopian Demo:Google Trends
https://www.quantopian.com/posts/google-search-terms-predict-market-movements
Top predictors
Bottom predictors
Quantopian Demo:Wikipedia
https://www.quantopian.com/posts/deriving-trading-signals-from-wikipedia-page-views-
new-feature
Twitter Sentiment Analysis
Can Twitter move the market?
Cautionary Tale
● 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)
● Preliminary evidence that information gathering can be quantified and exploited.
● Quantopian - Reproducibility Science● Mountains of data, waiting to be explored!
Departing thoughts...
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.