Incorporating News into Algorithmic Market Trading Presented by Philip Gagner, Vice President RavenPack International, S.L. With the kind assistance of Dow Jones Newswires

Algorithmic Trading The use of formal computer models to trade markets has a long history Algorithmic execution works but is limited Technical trading strategies don't work News content and market events correlate News is available online, but not in the right form for computer programs

Formal Computer Models Start with a model of group behaviors, generally in the area of behavioral finance Think about why news events might correlate with market changes –Real world events drive price, volatility, and volume changes –Good news organizations report relevant events quickly, before markets react –Markets are sensitive to rumor Think about reasonable time frames for leading or lagging correlations

Building a Market Model that Incorporates News Market Indexes and news generally, the problems and why one might believe it possible –Indexes combine very different companies –But, equities all share common characteristics for investors –Investors have choices: money might flow to/from equity markets generally, –News might drive volatility, as well as (or instead of) price As a matter of observation, most exchange-traded companies highly correlate on stock price changes We could trade a derivative against Index

Disney News/Citibank News Disney Stock/Citibank Stock

What do we know about news? Reasons to be discouraged News is highly periodic – Markets are not News is not structured, except for primitive tags News contains meaning, computer programs do not deal well with meaning News is language: ambiguous and fuzzy Numbers are easier to work with than words, in any algorithmic way There is too much news

What do we know about news? News is Periodic, Markets Not So

What do we know about news? Reasons to be encouraged News is relevant to value. Price should reflect value Traders read news and believe they act on it Advances in AI, understanding language and complexity Success (for example) in spam filtering Statistics works There is so much news and it is so alike

The RavenPack Approach Statistics, not meaning, is within our grasp –Engineers use thermodynamics, not quantum mechanics, to study systems –Thermodynamics is (nothing but) statistical quantum mechanics Discover patterns in news: Make them explicit Normalizing the News Use AI technology: Reduce News to Numbers –Classifier systems –Neural Networks –Image Processing technologies (compression, anti-aliasing) –Other

What is Required to Turn News into Numbers? More data is better, but quality data is best –Quote from customer: “I never believe in back-testing or historical results, but I can't live without them” –Huge problems with in-sample testing and apparently true observations Normalizing News requires gathering every news event in a stream—missing data are fatal New technologies, especially for time series, must be developed

Simple Things that Work Counting works –Simply counting certain types of news events over time, correlates with some market behaviors! But you must normalize and filter first. –Why? Probably because nobody is looking at it. Without equal access to the information that drives market changes, arbitrage is possible. –DJ Elementized News Feed, a different approach Stochastic Methods work Common Sense works –Simplistic assumptions don't: If a certain type of story has a market impact within an hour, will it have that effect at 2 AM, market time? No! –Can it safely be ignored? No!

An Example: Predicting Price from Corporate Press Releases Companies issue to: –Boost short term profits and (therefore) short term stock price –Regulatory compliance –Name recognition and long term profits Empirical question: Do they boost short term stock prices? Empirical answer:

Tools Available in Dow Jones News Analytics Historical News Archive (20 years) designed for algorithmic traders Historical Market Archive The MIS Application –Display Tools –Mathematical Tools –Retrieval Tools

Some Results IBM Stock v. IBM Press Releases Baysian MarketTalk and Equities (BMQ) Volatility Index ($VIX) and News Volatility Latent Semantic Analysis in Petroleum Market