Quantified News Based Trading: Is it the next big thing in algorithmic trading?

© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited

Quantified News based Trading: is it the next big thing in algorithmic

trading ?

Rajib Ranjan Borah

Nov 8, 2013

Princeton – UChicago Quant Trading Conference


Contents

Sr.No Topic Slide No

1 How is news quantified 5-20

2 Profitability using quantitative news analysis 22-42

3 Machine learning techniques for designing quant news strategies

44-47


Agenda

Background - how is news quantified

Profitability using quantitative news analysis

Machine learning techniques for designing quant news strategies

How is news quantified → Profitability → Machine learning techniques → QA


Historical Perspective

1. Rothschild:

A family network spread across Europe (Frankfurt, London, Paris, Naples, Vienna) → enabled obtaining financial information before peers

Knowledge of Battle of Waterloo result one full day before others → largest private fortune in the world

2. Reuters:

News service used pigeons & telegraph in 1850s to become fastest news disseminator

Continued focus on being the fastest news source → $12.4 billion conglomerate



What is Quantitative News Trading?

News is the first order factor that affects prices, volume, volatility of stocks, currencies, commodities, etc

Computer programs that scan news articles & quantify them

-> can respond to price moving factors faster than humans

-> can monitor a vaster amount of news reports than humans

This field is known as ‘Quantitative News Trading’

Apart from trading, quantification of news is also utilized in

• Media evaluation

• Market research

• Brand & reputation management

• Political analysis




• Sample output of a News Analytics feed: News represented by numbers









‘‘During the 200 milliseconds a human is reading the latest news headline, a trading program will have downloaded the entire article, analyzed its meaning, & traded based on the content”









‘‘During the 200 milliseconds a human is reading the latest news headline, a trading program will have downloaded the entire article, analyzed its meaning, & traded based on the content”

How do you quantify news reports and articles ?



Quantifying News - 1. Sentiment

News articles are assigned a score called ‘sentiment’

Sentiment says whether the article has a positive / negative or neutral tone

(Sale of Apple iPhones drop = -ve sentiment)

Sentiment at document level is different from sentiment at entity level

(Samsung beats Apple in smart phone sales = -ve sentiment for entity named Apple, +ve sentiment for Samsung)




How is ‘sentiment’ scored ?

• Naive parser: based on word count of –ve / +ve keywords

• Discriminated parser: weighted word count

• Grammatical parser: which verbs work on which objects. check linguistic semantics

• Machine Learning: From the data and the answers, try to find the factors– Generate bag-of-words: distance of subject from these sentiment

words

– Overfitting (and large vector sets), hitch-hiking and ignorance of linguistic structure




Scoring sentiments: grammatical parsing

• A database of words & phrases against which the article is searched

• Which verbs act on which objects

• Phrases which use adjectives & adverbs emphasize sentiments, therefore greater weightage



Quantifying News - 2. Relevance

How is relevance scored ?

• How many companies are mentioned in the news article

• Is the company mentioned in the headline as the subject/object

(‘Headline:UBS downgrades HSBC’ is not relevant to UBS)

• In which sentence number is the company first mentioned

• Length of the article & how many times is the firm mentioned

• Number of sentiment words & total words in article

• Two firms mentioned in a news article can both have a relevance of 1.0 (HP & Compaq announce merger)



Quantifying News - 2. Relevance

Issues with calculating relevance

• Requires synonym database:– IBM

– International Business Machines

– I.B.M.

– Big Blue

– BAML

– Bank of America

– Merrill Lynch

– Bank of America Merrill Lynch

– Merrill

– BoA



Quantifying News - 3. Novelty

How is novelty measured ?

• The keywords in the current news article are compared to historical articles about that company for similarity of digital fingerprints

• A linked articles count is generated

• Novelty is reported for – Within same news feed novelty (i.e. all Bloomberg news articles only)

– Across all news feeds novelty (i.e. across Reuters, Dow Jones, Bloomberg articles)



Quantifying News - 4. Market Impact

• Different types of news articles have different impacts on the price of the asset

• Another aspect of relevance is the likely market impact of the news article

• Market Impact is therefore a function of the type of news



Quantifying News - News Types

Types of news:

• Accounting news– Earnings

– Trading updates (broker action, market commentary)

– Guidance

– Financial issues (buybacks, dividends, equity offerings, etc)

– Regulatory filings

• Strategic news– M&A

– Restructuring

– Product, customer, competition related

– Corporate Governance



Quantifying News - 5. Volume

The number of news articles on the same topic can be a useful input to validate the impact

• Volume of news in Social Media also checked sometimes

• News Analytics strategies also check market based qualitative parameters along with news -> these help check if reaction to news is not already factored in– Trading Volume in last 24 hours (and historical average volume)

– Price change in last 24 hours



Quantifying News - 6. Social Media

Long term trading strategies try to gauge market sentiment from the plethora of information in the social media front

• Search engine volume counts (e.g. Google Trends) - global search for news keywords.

Can be used to confirm market impact of news

• Facebook, Twitter - user sentiment evaluated at macro level.

Many tools use certified twitter/facebook feeds only



Quantifying News - Key Factors

While the following are the four key inputs:

• Sentiment

• Relevance

• Novelty

• Market Impact

Some news analytics based strategies use other factors as well…

• Volume

• Social Media



Quantifying News – Market Psyche



Agenda




Q&A



Where Quantified news work

Machines are faster at responding to events than humans

Low latency event based trading (first to respond)

Machines can process a much vaster amount of information without any fatigue

Analyze broad spectrum of news to formulate broad views




Analyze broad spectrum of news to formulate broad views





For synchronous (fixed releases) expected events (earnings releases/ economic figures)

• Company figures provided in xml format instead of text





For synchronous (fixed releases) expected events (earnings releases/ economic figures)

• Company figures provided in xml format instead of text

• Economic figures provided in binary format instead of textual news articles

For asynchronous / unexpected news

• Are quantification algorithms robust enough to calculate trust-worthy sentiment, relevance, novelty scores ?



Opportunities : initial under-reaction

Quantified news driven trades work even when the trade is done at the end of the day

(under-reaction to news immediately. Tetlock, et al)



Late endof day response also profitable

Trading the news immediately = very profitable

At a broad level there is underreaction to news => entering into trades at the end of the day also makes profits



Certain sectors more profitable

Moving from Non-Cyclicals to

Financials increased the profit

from 135BP to 147BP



Sectors like Pharma, Defense, Auto, Energy, Banking more sensitive to news

Sensitivity of different sectors



Small cap firms more profitable

Smaller Cap firms show greater response to extreme sentiment news event

(bigger firms have greater scrutiny)



Hedged (market-neutral) is better

• Long +ve sentiment stocks only

OR

Short -ve sentiment stocks only. Will fail in different regimes

• Being long +ve sentiment stocks & short -ve sentiment stocks at the same time gives consistent returns



Bigger moves happen when there is news in

• Stocks with low beta (i.e. surprises happen to sleepy stocks)

Surprises are more profitable



Bigger moves happen when there is news in

• Stocks with low beta (i.e. surprises happen to sleepy stocks)

• VIX is low (i.e. surprises during calm times)

• When markets are improving (i.e. surprise to mostly long position holders)

Surprises are more profitable



Strategy variation - sentiment changes

• Instead of absolute sentiment scores, look at changes in sentiment scores of firms

• Bought stocks with highest increase in sentiment

• Shorted stocks with highest decrease in sentiment



Strategy variation - bottom fishing

• Bottom - fishing / turnaround stories

• Buying stocks with reversal in sentiment from grossly negative (a lot of the stocks turned out to be buybacks)



Generating Alpha

• Soft (opinion based) vs. Hard (fact based) news

Hard news has a stronger short term reaction than soft news


Source: RavenPack, FactSet, Macquarie Research, September 2012


• Scheduled/expected vs. Unscheduled/unexpected

Investors react more strongly to unscheduled/ unexpected news than scheduled/ expected


Generating Alpha



• Forecast vs Actual earnings

Investors react more strongly to forecasts than actual earnings news


Generating Alpha



News Analytics works best with

• Small cap stocks

• Sectors like pharma, banking, etc

• Stocks with low beta

• When VIX is low

• When markets are improving

• Hard news (vis-a-vis Soft news)

• Unscheduled news events (vis-a-vis scheduled news events)

• Being market-neutral

• Doing fewer stocks, but those with stronger signals

To summarize



Quantifying News - Where it fails ?

• On Sep. 7, 2008 Google’s newsbots picked up an old 2002 story about United Airlines possibly filing for bankruptcy

• UAL stock dived immediately



Quantifying News - Where it fails?

• News analytics were taught that ‘Osama-Bin-Laden’, and ‘killed’ had -ve sentiments for the markets

• On May 2 2012 when news reporting “Osama Bin-Landenkilled” were published, news bots treated this as a negative news article and sold stocks

• The two examples cited and their impacts show the extent to which people have embraced news analytics to automate trading



Quantifying News – challenges

• Languages like Chinese and Japanese with large number of alphabetic symbols and complex grammar

However, there is a lot of development in this domain already

• The ever increasing volume of news articles from increased news sources, and from increased volumes in social media



Agenda




Q&A



Machine Learning methodologies

Traditional approach => formulate hypothesis based on experience/expertise, validate statistically using historical data

Machine learning approach => output + raw data fed into a system. System reports factors within data that lead to output

Three broad approaches

• Tree

• Forest

• Planet



Machine Learning - TREE method

Output: Post-event abnormal results

Input: Quantitative news analytics

Issues: Overfitting

(works with training data

does not work on real data)

Solution: Pruning



Machine Learning - FOREST method

Multiple factors might impact output

Instead of one tree to solve everything,

have a forest of trees

Each tree has a vote in the output.

Weightage of vote depends on accuracy

of that tree



Machine Learning - PLANET method

Instead of linear relationships between input and output,

Planet breaks the variable space into sections, fits linear functions within those sections



Agenda




Q&A



Contacts

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• Statistics

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For algorithmic trading advisory: [email protected]

To reach me directly: [email protected]


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Quantified News Based Trading: Is it the next big thing in algorithmic trading?

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