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

Quantified News based Trading:
is it the next big thing in algorithmic
trading ?
Rajib Ranjan Borah
Nov 8, 2013
Princeton – UChicago Quant Trading Conference
© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited

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

How is news quantified → Profitability → Machine learning techniques → QA
Agenda
Background - how is news quantified
Profitability using quantitative news analysis
Machine learning techniques for designing quant news strategies

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”

How do you quantify news reports and articles ?
‘‘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”

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
Background - how is news quantified
Profitability using quantitative news analysis
Machine learning techniques for designing quant news strategies
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

Sensitivity of different sectors
Sectors like Pharma, Defense, Auto, Energy, Banking more sensitive to news

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

Surprises are more profitable
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)

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

Generating Alpha
• 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

To summarize
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

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-Landen
killed” 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

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

Contacts
For 4-month Executive Program in Algorithmic Trading:
contact@quantinsti.com
E-PAT: 4 month weekend online program (3hrs every Sat + Sun)
• Statistics
• Quant Strategies
• Technology (coding on algorithmic trading platform)
For algorithmic trading advisory: contact@iragecapital.com
To reach me directly: rajib.borah@iragecapital.com

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

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