Deriving Trading Signals from Google Trends and Wikipedia Page Views

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...

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 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.