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Quantified News based Trading:
is it the next big thing in algorithmic
trading ?
Rajib Ranjan Borah
Nov 8, 2013PrincetonUChicago Quant Trading Conference
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Agenda
Background - how is news quantified
Profitability using quantitative news analysis
Machine learning techniques for designing quant news strategies
Q&A
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Agenda
Background - how is news quantified
Profitability using quantitative news analysis
Machine learning techniques for designing quant news strategies
Q&A
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.
The world runs on information and few areas as directly so as in
finance
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Historical Perspective
1. Rothschild:
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Historical Perspective
1. Rothschild:
A family network spread across Europe (Frankfurt, London,
Paris, Naples, Vienna)
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Historical Perspective
1. Rothschild:
A family network spread across Europe (Frankfurt, London,
Paris, Naples, Vienna) enabled obtaining financial
information before peers
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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
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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
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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:
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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 becomefastest news disseminator
How is news quantified Profitability Machine learning techniques QA
H i ifi d P fi bili M hi l i h i
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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 becomefastest news disseminator
Continued focus on being the fastest news source
How is news quantified Profitability Machine learning techniques QA
H i tifi d P fit bilit M hi l i t h i
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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 becomefastest news disseminator
Continued focus on being the fastest news source $12.4
billion conglomerate
How is news quantified Profitability Machine learning techniques QA
H i tifi d P fit bilit M hi l i t h i QA
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Historical Perspective
How have things progressed since 1850s ?
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Historical Perspective
How have things progressed since 1850s ?
1850s : Carrier pigeons
1860s : Telegraph
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How is news quantified Profitability Machine learning techniques QA
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Historical Perspective
How have things progressed since 1850s ?
1850s : Carrier pigeons
1860s : Telegraph
1960s : Teleprinter
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How is news quantified Profitability Machine learning techniques QA
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Historical Perspective
How have things progressed since 1850s ?
1850s : Carrier pigeons
1860s : Telegraph
1960s : Teleprinter 1980s : Electronic network (and internet)
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Historical Perspective
How have things progressed since 1850s ?
1850s : Carrier pigeons
1860s : Telegraph
1960s : Teleprinter 1980s : Electronic network (and internet)
2000s : Machine Readable News a.k.a. Quantitative News
How is news quantified Profitability Machine learning techniques QA
How is news quantified Profitability Machine learning techniques QA
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What is Quantitative News Trading?
News is the first order factor that affects prices, volume,
volatility of stocks, currencies, commodities, etc
How is news quantified Profitability Machine learning techniques QA
How is news quantified Profitability Machine learning techniques QA
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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
How is news quantified Profitability Machine learning techniques QA
How is news quantified Profitability Machine learning techniques QA
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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
How is news quantified Profitability Machine learning techniques QA
How is news quantified Profitability Machine learning techniques QA
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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
How is news quantified Profitability Machine learning techniques QA
How is news quantified Profitability Machine learning techniques QA
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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
q y g q Q
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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
q y g q
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What is Quantitative News Trading?
Sample output of a News Analytics feed: News
represented by numbers
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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
How is news quantified Profitability Machine learning techniques QA
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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
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
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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
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 is news quantified Profitability Machine learning techniques QA
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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
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 ?
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Quantifying News - 1. Sentiment
News articles are assigned a score called sentiment
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Quantifying News - 1. Sentiment
News articles are assigned a score called sentiment
Sentiment says whether the article has a positive / negative or
neutral tone
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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)
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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
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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)
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Quantifying News - 1. Sentiment
How is sentiment scored ?
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Quantifying News - 1. Sentiment
How is sentiment scored ?
Naive parser: based on word count ofve / +ve keywords
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Quantifying News - 1. Sentiment
How is sentiment scored ?
Naive parser: based on word count ofve / +ve keywords
Company Xs sales were good
Company Xs sales were fantastic
(Both have one positive keyword, but the intensity differs)
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Quantifying News - 1. Sentiment
How is sentiment scored ?
Naive parser: based on word count ofve / +ve keywords
Discriminated parser: weighted word count The results were good, not bad.
The results were bad, not good.
(Both score equally. Both have the same words - but mean completely
opposite)
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Quantifying News - 1. Sentiment
How is sentiment scored ?
Naive parser: based on word count ofve / +ve keywords
Discriminated parser: weighted word count Grammatical parser: which verbs work on which objects.
check linguistic semantics
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Quantifying News - 1. Sentiment
How is sentiment scored ?
Naive parser: based on word count ofve / +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
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Quantifying News - 1. Sentiment
How is sentiment scored ?
Naive parser: based on word count ofve / +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
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Quantifying News - 1. Sentiment
Scoring sentiments: grammatical parsing
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Quantifying News - 1. Sentiment
Scoring sentiments: grammatical parsing
A database of words & phrases against which the article is
searched
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Quantifying News - 1. Sentiment
Scoring sentiments: grammatical parsing
A database of words & phrases against which the article is
searched
Which verbs act on which objects
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Quantifying News - 1. Sentiment
Scoring sentiments: grammatical parsing issues
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Quantifying News - 1. Sentiment
Scoring sentiments: grammatical parsing issues
Linguistic structures like negation, double negation, sarcasm,
intensification, hanging lemma
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Quantifying News - 1. Sentiment
Scoring sentiments: grammatical parsing issues
Linguistic structures like negation, double negation, sarcasm,
intensification, hanging lemma
(negation: Company X did not become the best in the world
double negation: Company X did not do bad
sarcasm: With such an attitude, X is sure to become the best firm
intensification: Company X did terribly well
hanging lemma: Company X loses lawsuit against company Y. They will
have to pay $1billion USD )
Word Sense Disambiguation - same word, different meanings
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Quantifying News - 1. Sentiment
Scoring sentiments: grammatical parsing issues
Linguistic structures like negation, double negation, sarcasm,
intensification, hanging lemma
(negation: Company X did not become the best in the world
double negation: Company X did not do bad
sarcasm: With such an attitude, X is sure to become the best firm
intensification: Company X did terribly well
hanging lemma: Company X loses lawsuit against company Y. They will
have to pay $1billion USD )
Word Sense Disambiguation - same word, different meanings Company X received a fine
X is doing fine X sells fine grained sand, etc
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Quantifying News - 2. Relevance
Is Sentiment good enough to quantify a news report?
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Quantifying News - 2. Relevance
Is Sentiment good enough to quantify a news report?
A news article might:
be predominantly about a company
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Quantifying News - 2. Relevance
Is Sentiment good enough to quantify a news report?
A news article might:
be predominantly about a company
mention that company and others as well
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Quantifying News - 2. Relevance
Is Sentiment good enough to quantify a news report?
A news article might:
be predominantly about a company
mention that company and others as well
mention that company in passing in the article
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Quantifying News - 2. Relevance
Is Sentiment good enough to quantify a news report?
A news article might:
be predominantly about a company
mention that company and others as well
mention that company in passing in the article
Relevance measures how relevant a news article is for aparticular company
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Quantifying News - 2. Relevance
How is relevance scored ?
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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
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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
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Quantifying News - 2. Relevance
Issues with calculating relevance
Requires synonym database:
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Quantifying News - 2. Relevance
Issues with calculating relevance
Requires synonym database:
IBM
International Business Machines
I.B.M.
Big Blue
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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
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Quantifying News - 3. Novelty
Often the news article is not reported in its entirety, but inmultiple spurts
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Quantifying News - 3. Novelty
Often the news article is not reported in its entirety, but inmultiple spurts
Alert
News Article
Update Append
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Quantifying News - 3. Novelty
Often the news article is not reported in its entirety, but inmultiple spurts
Alert
News Article
Update
Append
Moreover, multiple news
sources report same news
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Quantifying News - 3. Novelty
Often the news article is not reported in its entirety, but inmultiple spurts
Alert
News Article
Update
Append
Moreover, multiple news
sources report same news
News also cause price
changes which themselves
become news
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Quantifying News - 3. Novelty
If we do not keep track & respond to repeated instances ofthe same news
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Quantifying News - 3. Novelty
If we do not keep track & respond to repeated instances ofthe same news => we will end up repeating our actions
manifold for the same event
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Quantifying News - 3. Novelty
If we do not keep track & respond to repeated instances ofthe same news => we will end up repeating our actions
manifold for the same event
Therefore every news article should be checked for newnessor novelty before responding
How is news quantified Profitability Machine learning techniques QA
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Quantifying News - 3. Novelty
How is novelty measured ?
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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
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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
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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)
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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)
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Quantifying News - 4. Market Impact
Different types of news articles have different impacts on theprice of the asset
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Quantifying News - 4. Market Impact
Different types of news articles have different impacts on theprice of the asset
Another aspect of relevance is the likely market impact of the
news article
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f
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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
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Q if i N N T
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Quantifying News - News Types
Types of news based on time of news report
Asynchronous / unexpected
Synchronous / fixed releases
Q if i N K F
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Quantifying News - Key Factors
While the following are the four key inputs:
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Q tif i N K F t
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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
Q tif i N 5 V l
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Quantifying News - 5. Volume
The number of news articles on the same topic can be a usefulinput to validate the impact
Q tif i N 5 V l
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Quantifying News - 5. Volume
The number of news articles on the same topic can be a usefulinput to validate the impact
Volume of news in Social Media also checked sometimes
Q tif i N 5 V l
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Quantifying News - 5. Volume
The number of news articles on the same topic can be a usefulinput 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
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Quantifying News 6 Social Media
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Quantifying News - 6. Social Media
Long term trading strategies try to gauge market sentiment fromthe 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
Quantifying News 6 Social Media
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Quantifying News - 6. Social Media
Long term trading strategies try to gauge market sentiment fromthe 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.
Quantifying News 6 Social Media
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Quantifying News - 6. Social Media
Long term trading strategies try to gauge market sentiment fromthe 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
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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
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Quantifying NewsMarket Psyche
News Analytics tools calculate Market Psychology Indices -evaluating broad psychological sentiments from global news
Quantifying News Market Psyche
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Quantifying NewsMarket Psyche
News Analytics tools calculate Market Psychology Indices -evaluating broad psychological sentiments from global news
Country : sentiment, conflict, fear, joy, optimism, trust,
uncertainty, urgency, violence, government corruption,government instability, social unrest, default, inflation, credit
tightening, etc
Quantifying News Market Psyche
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Quantifying NewsMarket Psyche
News Analytics tools calculate Market Psychology Indices -evaluating broad psychological sentiments from global news
Country : sentiment, conflict, fear, joy, optimism, trust,
uncertainty, urgency, violence, government corruption,government instability, social unrest, default, inflation, credit
tightening, etc
Equity: Gloom, Anger, Innovation, Stress, Optimism, Earnings
Expectations, Market Risk, Market Forecast
Quantifying News Market Psyche
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Quantifying NewsMarket Psyche
News Analytics tools calculate Market Psychology Indices -evaluating broad psychological sentiments from global news
Country : sentiment, conflict, fear, joy, optimism, trust,
uncertainty, urgency, violence, government corruption,government instability, social unrest, default, inflation, credit
tightening, etc
Equity: Gloom, Anger, Innovation, Stress, Optimism, Earnings
Expectations, Market Risk, Market Forecast Currency: Forecast, Currency Peg Instability, Carry Trade
Quantifying News Market Psyche
How is news quantified
Profitability
Machine learning techniques QA
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Quantifying NewsMarket Psyche
News Analytics tools calculate Market Psychology Indices -evaluating broad psychological sentiments from global news
Country : sentiment, conflict, fear, joy, optimism, trust,
uncertainty, urgency, violence, government corruption,government instability, social unrest, default, inflation, credit
tightening, etc
Equity: Gloom, Anger, Innovation, Stress, Optimism, Earnings
Expectations, Market Risk, Market Forecast Currency: Forecast, Currency Peg Instability, Carry Trade
Agriculture: Acreage cultivated, weather damage, subsidies,
production volume, supply vs demand, surplus vs shortage,
price up
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Quantifying News Market Psyche
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Quantifying News Market Psyche
Agenda
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Agenda
Background - how is news quantified
Profitability using quantitative news analysis
Machine learning techniques for designing quant news strategies
Q&A
Is it profitable ?
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Is it profitable ?
Are computers smart enough to read news and make profitabletrades?
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Where Quantified news work
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Where Quantified news work
Machines are faster at responding to events than humans
Where Quantified news work
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Where Quantified news work
Machines are faster at responding to events than humans
Machines can process a much vaster amount of information
without any fatigue
Where Quantified news work
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Where Quantified news work
Machines are faster at responding to events than humansLow latency event based trading (first to respond)
Machines can process a much vaster amount of information
without any fatigue
Where Quantified news work
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Where Quantified news work
Machines are faster at responding to events than humansLow 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
Where Quantified news work
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Where Quantified news work
Machines are faster at responding to events than humansLow 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
Where Quantified news work
How is news quantified Profitability Machine learning techniques QA
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Where Quantified news work
Analyze broad spectrum of news to formulate broad views
Where Quantified news work
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Q
Analyze broad spectrum of news to formulate broad views
Where Quantified news work
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Q
Analyze broad spectrum of news to formulate broad views
Where Quantified news work
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Q
Low latency event based trading (first to respond)
Where Quantified news work
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Q
Low latency event based trading (first to respond)
For synchronous (fixed releases) expected events (earnings
releases/ economic figures)
Company figures provided in xml format instead of text
Where Quantified news work
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Low latency event based trading (first to respond)
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
Where Quantified news work
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Low latency event based trading (first to respond)
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
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pp
Quantified news driven trades work even when the trade is doneat the end of the day
(under-reaction to news immediately. Tetlock, et al)
Lateendofdayresponsealsoprofitable
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y p p
Trading the news immediately = very profitableAt a broad level there is underreaction to news => entering into
trades at the end of the day also makes profits
Long short strategy returns
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Filtering sentiments increase profits
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Increasing threshold from 90 to
95 percentile increases returns
from 55 to 138 bps in 3 days
Certain sectors more profitable
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Moving from Non-Cyclicals to
Financials increased the profit
from 135BP to 147BP
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Small cap firms more profitable
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Smaller Cap firms show greater response to extreme sentimentnews event
(bigger firms have greater scrutiny)
Filter & trade fewer stocks
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More is not better. Quality over quantity
Trading only stocks with very high sentiment/relevance is
better
Hedged (market-neutral) is better
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Long +ve sentiment stocks onlyOR
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
Volatility regimes and news
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Volatile vs stable Economic regimes
In more volatile markets people tend to react less strongly to
positive news and react more strongly to negative news
Surprises are more profitable
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Bigger moves happen when there is news in
Stocks with low beta (i.e. surprises happen to sleepy stocks)
Surprises are more profitable
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Bigger moves happen when there is news in
Stocks with low beta (i.e. surprises happen to sleepy stocks)
Surprises are more profitable
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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)
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Surprises are more profitable
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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 longposition holders)
Surprises are more profitable
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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 longposition holders)
Surprises are more profitable
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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 longposition holders)
Strategy variation - sentiment changes
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Instead of absolute sentiment scores, look at changes insentiment scores of firms
Bought stocks with highest increase in sentiment
Shorted stocks with highest decrease in sentiment
Strategy variation - bottom fishing
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Bottom - fishing / turnaround stories Buying stocks with reversal in sentiment from grossly
negative (a lot of the stocks turned out to be buybacks)
Strategy variation - trading volatility
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News articles definitely lead to increased volatility, eventhough direction of move might be difficult to predict
through news analytics
Take vega positions (var-swaps) using options in anticipationof increased volatility
Generating Alpha
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Soft (opinion based) vs. Hard (fact based) newsHard news has a stronger short term reaction than soft news
Source: RavenPack, FactSet, Macquarie Research, September 2012
How is news quantified Profitability Machine learning techniques QA
Generating Alpha
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Scheduled/expected vs. Unscheduled/unexpectedInvestors react more strongly to unscheduled/ unexpected
news than scheduled/ expected
Source: RavenPack, FactSet, Macquarie Research, September 2012
How is news quantified Profitability Machine learning techniques QA
Generating Alpha
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Forecast vs Actual earningsInvestors react more strongly to forecasts than actual earnings
news
Source: RavenPack, FactSet, Macquarie Research, September 2012
How is news quantified Profitability Machine learning techniques QA
Generating Alpha
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Guidance vs Actual earningsInvestors react more strongly to guidance to actual earnings
Source: RavenPack, FactSet, Macquarie Research, September 2012
How is news quantified Profitability Machine learning techniques QA
Generating Alpha
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News type Event Study Results
Source: RavenPack, FactSet, Macquarie Research, September 2012
To summarize
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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 ?
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On Sep. 7, 2008 Googles newsbots picked up an old 2002story about United Airlines possibly filing for bankruptcy
Quantifying News - Where it fails ?
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On Sep. 7, 2008 Googles newsbots picked up an old 2002story about United Airlines possibly filing for bankruptcy
UAL stock dived immediately
Quantifying News - Where it fails?
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News analytics were taught that Osama-Bin-Laden, andkilled had -ve sentiments for the markets
Quantifying News - Where it fails?
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News analytics were taught that Osama-Bin-Laden, andkilled 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 negativenews article and sold stocks
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Quantifying Newschallenges
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Languages like Chinese and Japanese with large number ofalphabetic symbols and complex grammar
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Agenda
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Background - how is news quantified
Profitability using quantitative news analysis
Machine learning techniques for designing quant news strategies
Q&A
Machine Learning methodologies
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Traditional approach => formulate hypothesis based onexperience/expertise, validate statistically using historical data
Machine Learning methodologies
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Traditional approach => formulate hypothesis based onexperience/expertise, validate statistically using historical data
Machine learning approach =>
Machine Learning methodologies
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Traditional approach => formulate hypothesis based onexperience/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
Machine Learning methodologies
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Traditional approach => formulate hypothesis based onexperience/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
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Output: Post-event abnormal resultsInput: Quantitative news analytics
Machine Learning - TREE method
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Output: Post-event abnormal resultsInput: Quantitative news analytics
Machine Learning - TREE method
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Output: Post-event abnormal resultsInput: Quantitative news analytics
Issues: Overfitting
(works with training data
does not work on real data)
Machine Learning - TREE method
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Output: Post-event abnormal resultsInput: Quantitative news analytics
Issues: Overfitting
(works with training data
does not work on real data)
Solution: Pruning
Machine Learning - TREE method
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Output: Post-event abnormal resultsInput: Quantitative news analytics
Issues: Overfitting
(works with training data
does not work on real data)
Solution: Pruning
Machine Learning - FOREST method
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Multiple factors might impact output
Machine Learning - FOREST method
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Multiple factors might impact output
Instead of one tree to solve everything,
have a forest of trees
Machine Learning - FOREST method
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Multiple factors might impact output
Instead of one tree to solve everything,
have a forest of trees
Machine Learning - FOREST method
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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
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Machine Learning - PLANET method
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Instead of linear relationships between input and output,
Planet breaks the variable space into sections, fits linear
functions within those sections
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Text Mining: An example
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Converting a lineof news into
metadata to be
used for analysis
or trade
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