Trading Off the News: Applications of News Algorithms

| © Copyright 2008 Dow Jones and Company

Trading Off the News: Applications of News Algorithms

July 1, 2008 CARISMA 2008

Alan SlomowitzDirector Product DevelopmentAlgorithmic and Trading SolutionsDow Jones Content Technology Solutions


Source: DJ Newswires

Trading Off The News - HP to Acquire EDS

15:02 *WSJ: Hewlett-Packard In Advanced Talks to Acquire EDS15:02 *WSJ: Hewlett-Packard-EDS Deal Could Come In Matter Of Days15:03 *WSJ: Premium Expected To Be In 30% Range –Sources15:03 *WSJ: EDS Overall Price Roughly $13 Billion -Sources >HPQ EDS


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Successful trading is about finding the crucial edge

First to take advantage of trading opportunities

Quantitative investors require: Analytical hypotheses Technology Data

Data = News Exploit this new information source Difficult-to-quantify qualitative content

News and the Quantitative Investor:The New Frontier


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Intuitively, investment professionals realize you can use news to create profitable trading models

Proprietary traders, Stat Arb desks and hedge funds now use news to create Alpha-based trading strategies

News based algorithms that can profit from market anomalies lasting from mere milliseconds to days or even longer

Economic indicator based algorithms Corporate news based algorithms

News-based algorithms are valuable additions to Alpha generation:

News is difficult to integrate into investment models

Difficulty means higher value – not so easy to commoditize Requires different tools – both data and text analysis tools Different ways of analyzing market trends

Taking Advantage of the News


Economic Indicator-Based Algorithms Arms Race – Must have a global, reliable and innovative news

provider Latency matters

FX and Futures markets

Multiple markets and countries

Corporate News-Based Algorithms Scheduled events: Earnings, Guidance Unscheduled events: Mergers, Ratings changes, Executive

changes Text mining events: surprise and sentiment

Analyzing the language of the news Quantifying that analysis

Making News Work for You: Tagged, Machine-Readable News Feed


Expected Corporate News Events Plan for releases incorporating expected, historical and actual data How long does it take for market to assimilate this news

Unexpected News Events Cannot build an alpha algorithm for every unexpected event Anticipate a controlled set of pre-defined news events

Ratings changes, executive changes, dividends

Capability to stop algorithms that are in flight when such news events occur

e.g., HP announces purchase of EDS

Adjust trading strategy for significant events

Solving the Corporate News Puzzle


Complex Event Processing Multiple signals combined

If EPS > X and Price moves > 3% in 5 minutes and market is up less than 1% Then BUY XXXX shares

Academic Research

Solving the Corporate News Puzzle (cont.)


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Paul Tetlock, Quantifying Language to Measure Firms’ Fundamentals (Sept 2006/May 2007)

Linguistic media content captures otherwise hard-to-quantify aspects of firms’ fundamentals, which investors quickly factor into stock prices

Simple counting of negative words can uncover negative sentiment in earnings releases

Antweiler and Frank, Do U.S. Stock Markets Typically Overreact to Corporate News Stories? (October, 2005)

Initial returns jump is typically accompanied by a temporary jump in trading volume

Reversal is typically accompanied by gradually declining trading volume

During business cycle expansions, the process is largely complete after two or three weeks

During recessions, the reversal is much more prolonged and we are not able to identify a clear point at which the process ends

What the Academics are Saying


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Paul Tetlock, Giving Content to Investor Sentiment: The Role of Media in the Stock Market, Journal of Finance (June 2007)

Academic Bibliography available

What the Academics are Saying (cont.)


Long/Short Portfolio

• Firms with positive news – Long

• Firms with negative news – Short

• Hold for one trading day

• Rebalance at end of next trading day

Trading off Positive/Negative Word Counts

More Than Words – Quantifying Language to Measure Firms’ FundamentalsMay 2007 – Tetlock, Saar-Tsechansky, Macskassy


S&P 500 Firms

All news stories with a fraction of negative words in the year’s top (bottom) quartile as negative (positive) stories

Compute difference between reaction to positive and negative news stories

Abnormal Event Returns 10 Days Prior and 10 Days Post News Releases



Cumulative SUE 10 quarters prior to news and 10 quarters after news

Negative stories 30 days prior to earnings announcements

Cumulative Standardized Unexpected Earnings (SUE)



40 Minutes of Alpha

Research in Progress

S&P 500 Companies

Beat the estimates and you are king …

Miss your estimates and you get hammered …

For 40 Minutes!


Rapidly changing market conditions and news events alter market prices and expectations

Belief is that prices should fully reflect expectations

News events do have significant effects on market prices and returns Merger announcements Earnings guidance Interest-rate cuts

News Impacts: Some Examples


Bank of America to Buy Countrywide


Source: DJ Newswires & MarketWatch

GE Earnings Guidance


Bank of England Cuts Rates

* DJ Bank of England Cuts Bank Rate To 5.25% From 5.5%


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The Ultimate Challenge: Deriving tradable meaning from the news Start with publicly available content like DJ News, press releases,

EDGAR filings Determine analytical methodology Back-test and determine viability Integrate multiple models, sources Apply to real-world

Requirements Large archive Text mining tools Sentiment models Skilled analysts

Good News/Bad News – Profit from Both


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Profiting from expected events Economic data changes

News and momentum: Evidence that news gives rise to long- and short-term momentum in

prices

Overreaction and underreaction: On news releases, stocks overshoot with prices misaligned with

fundamentals

Sentiment - Count of negative news: A simple count of negative words in news has proven to provide

strategies with positive Alpha

Potential Alphas


Profiting From News Has Its Challenges

What news events to capture Economic data, Earnings, Guidance – the easy stuff Mergers & acquisitions, executive changes, bankruptcies – that’s tough Sentiment - ??

Many news feeds are not tagged or machine-readable Need to parse, automate, and digest news Web-based content is another major challenge

Need to determine: Which news is relevant? So many sources! How to react to unexpected events? Trusted sources Is news already factored into price? Which direction will news push the price?


Final Thoughts Electronic and Quantitative Trading market is rapidly evolving

News is an increasingly important driver of automated trading strategies

Growing number of tradable news events News data algorithms

Economic data; earnings and guidance Elementized News events

Ratings changes, mergers & acquisitions, executive changes

Trading on planned events is profitable If you can react quickly enough

Not paying attention to news can cost you Lost Alpha opportunities Outright losses


Suite of advanced information and technology tools for a performance edge Unique news data, tagged and elementized Computer-readable Flexible, ultra-low-latency delivery options and seamless integration News sentiment tools 20-plus-year news archives for back-testing ideas Tools for creating, deploying and adapting complex, news-based trading strategies

Products

Dow Jones Elementized News Feed Economic Data – Major economic indicators Corporate Data – Earnings, Guidance, Mergers, Executive Changes, Ratings,

Bankruptcies, more

Dow Jones News & Archives for Algorithmic Applications 20-plus-year archive of news for analysis Viewer and toolkit available

Dow Jones News Analytics News transformed into actionable data Derive analytics based on sentiment, volume, more

Transforming News For Quantitative Trading And Research

Dow Jones SolutionsFor Algorithmic & Quantitative Trading


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Questions?

Trading Off The News


Trading Off The News: Applications Of News Algorithms

Alan Slomowitz: [email protected] (201) 938-2195

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Trading Off the News: Applications of News Algorithms