Date post: | 22-Jul-2015 |
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Economy & Finance |
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Rajib Ranjan Borah,
Co-Founder & Director at iRageCapital Advisory Pvt. Ltd.
Faculty at QuantInsti Quantitative Learning Pvt. Ltd.
15-MAY-2015
Mumbai
Quantifying News for Automated Trading- Methodology and Profitability
Methodology - the science behind quantifying news
Profitability - does it really make money
Q&A
Agenda
.
“The world runs on information and few areas as directly so as in finance”
Methodology → Profitability → QA
Historical Perspective - I
Methodology → Profitability → QA
Historical Perspective - I
Methodology → Profitability → QA
Historical Perspective - I
Rothschild:
family network spreadacross Europe
→
financial informationobtained before peers
e.g.
Knowledge of Battle ofWaterloo result
→one full day earlier
Methodology → Profitability → QA
Historical Perspective - II
Methodology → Profitability → QA
Historical Perspective - II
Methodology → Profitability → QA
Historical Perspective - II
Methodology → Profitability → QA
Historical Perspective - III
Methodology → Profitability → QA
March 27
$2.4 million
March 13
$1-2 million
April 1
< $1 million
What is Quantitative News Trading?
News is the first order factor that affects prices, volume, volatility of stocks, currencies, commodities, etc
Methodology → Profitability → QA
What is Quantitative News Trading?
Computer programs that scan news articles & quantify them :
Methodology → Profitability → QA
What is Quantitative News Trading?
Computer programs that scan news articles & quantify them :
Methodology → Profitability → QA
What is Quantitative News Trading?
News is the prime 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
Methodology → Profitability → QA
What is Quantitative News Trading?
News is the prime 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
-> can monitor a vaster amount of news reports
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”
Methodology → Profitability → QA
What is Quantitative News Trading?
News is the prime 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
-> can monitor a vaster amount of news reports
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”
Methodology → Profitability → QA
What is Quantitative News Trading?
News is the prime 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
-> can monitor a vaster amount of news reports
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”
Methodology → Profitability → QA
What is Quantitative News Trading?
News is the prime 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
-> can monitor a vaster amount of news reports
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 ?
Methodology → Profitability → QA
What is Quantitative News Trading?
• Sample output of a News Analytics feed: News represented by numbers
Methodology → Profitability → QA
Quantifying News - Factor 1
Methodology → Profitability → QA
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)
Methodology → Profitability → QA
Quantifying News - 1. Sentiment
How is ‘sentiment’ scored ?
Methodology → Profitability → QA
Quantifying News - 1. Sentiment
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
Methodology → Profitability → QA
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
Methodology → Profitability → QA
Quantifying News - Factor 2
Is Sentiment good enough to quantify a news report?
Methodology → Profitability → QA
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 a particular company
Methodology → Profitability → QA
Quantifying News - 2. Relevance
How is relevance scored ?
Methodology → Profitability → QA
Quantifying News - 2. Relevance
How is relevance scored ?
Methodology → Profitability → QA
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)
Methodology → Profitability → QA
Quantifying News - 2. Relevance
Issues with calculating relevance
Methodology → Profitability → QA
Quantifying News - 2. Relevance
Issues with calculating relevance
Methodology → Profitability → QA
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
Methodology → Profitability → QA
Quantifying News - Factor 3
Methodology → Profitability → QA
Quantifying News - 3. Novelty
• Often the news article is not reported in its entirety, but in multiple spurts– Alert
– News Article
– Update
– Append
Methodology → Profitability → QA
Quantifying News - 3. Novelty
• Often the news article is not reported in its entirety, but in multiple spurts– Alert
– News Article
– Update
– Append
• Moreover, multiple news sources report same news
Methodology → Profitability → QA
Quantifying News - 3. Novelty
• Often the news article is not reported in its entirety, but in multiple spurts– Alert
– News Article
– Update
– Append
• Moreover, multiple news sources report same news
• News also cause price changes which themselves become news
Methodology → Profitability → QA
Quantifying News - 3. Novelty
• If we do not keep track & respond to repeated instances of the same news => we will end up repeating our actions manifold for the same event
• Therefore every news article should be checked for newness or ‘novelty’ before responding
Methodology → Profitability → QA
Quantifying News - 3. Novelty
How is novelty measured ?
Methodology → Profitability → QA
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)
Methodology → Profitability → QA
Quantifying News - Factor 4
Methodology → Profitability → QA
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
Methodology → Profitability → QA
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
Methodology → Profitability → QA
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
Methodology → Profitability → QA
Quantifying News - News Types
Types of news based on time of news report
• Asynchronous / unexpected
• Synchronous / fixed releases
Methodology → Profitability → QA
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…
Methodology → Profitability → QA
Quantifying News - 5.i. 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
Methodology → Profitability → QA
Quantifying News - 5.ii. Search Trends
Methodology → Profitability → QA
Quantifying News - 5.iii. Social Media
Methodology → Profitability → QA
Quantifying News – Market 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
Methodology → Profitability → QA
Quantifying News – Market Psyche
Source: ThomsonReuters
Methodology → Profitability → QA
Quantifying News – Market Psyche
Source: ThomsonReuters
Methodology → Profitability → QA
Source: ThomsonReuters
Methodology → Profitability → QA
Quantifying News – Market Psyche
Source: ThomsonReuters
Methodology → Profitability → QA
Quantifying News – Market Psyche
Source: ThomsonReuters
Methodology → Profitability → QA
Methodology - the science behind quantifying news
Profitability - does it really make money
Q&A
Agenda
Methodology → Profitability → QA
Is it profitable ?
Source: ThomsonReuters
Methodology → Profitability → QA
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
Methodology → Profitability → QA
Where Quantified news work
Analyze broad spectrum of news to formulate broad views
Source: ThomsonReuters
Methodology → Profitability → QA
Where Quantified news work
Analyze broad spectrum of news to formulate broad views
Source: ThomsonReuters
Methodology → Profitability → QA
Where Quantified news work
Low latency event based trading (first to respond)
Methodology → Profitability → QA
Where Quantified news work
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
Source: ThomsonReuters
Methodology → Profitability → QA
Where Quantified news work
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
Source: ThomsonReuters
Methodology → Profitability → QA
Where Quantified news work
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 ?
Methodology → Profitability → QA
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)
Source: More Than Words: Quantifying Language to Measure Firms’ Fundamentals Tetlock,Saar-Tsechansky &
Macskassy
Methodology → Profitability → QA
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
Source: ThomsonReuters
Methodology → Profitability → QA
Long short strategy returns
Source: ThomsonReuters
Methodology → Profitability → QA
Filtering sentiments increase profits
Increasing threshold from 90 to
95 percentile increases returns
from 55 to 138 bps in 3 days
Source: ThomsonReuters
Methodology → Profitability → QA
Certain sectors more profitable
Moving from Non-Cyclicals to
Financials increased the profit
from 135BP to 147BP
Source: ThomsonReuters
Methodology → Profitability → QA
Sectors like Pharma, Defense, Auto, Energy, Banking more sensitive to news
Sensitivity of different sectors
Source: Trading Strategies to Exploit News Sentiment – Wenbin Zhang & Steven Skiena
Methodology → Profitability → QA
Small cap firms more profitable
Smaller Cap firms show greater response to extreme sentiment news event
(bigger firms have greater scrutiny)
Source: Leinweber & ThomsonReuters
Methodology → Profitability → QA
Filter & trade fewer stocks
• More is not better. Quality over quantity
• Trading only stocks with very high sentiment/relevance is better
Source: Trading Strategies to Exploit News Sentiment – Wenbin Zhang & Steven Skiena
Methodology → Profitability → QA
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
Source: Trading Strategies to Exploit News Sentiment – Wenbin Zhang & Steven Skiena
Methodology → Profitability → QA
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
Volatility regimes and news
Source: RavenPack, IBES, Macquarie Research, September 2012
Methodology → Profitability → QA
Bigger moves happen when there is news in
• Stocks with low beta (i.e. surprises happen to sleepy stocks)
Surprises are more profitable
Source: ThomsonReuters
Methodology → Profitability → QA
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)
Surprises are more profitable
Source: ThomsonReuters
Methodology → Profitability → QA
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
Source: ThomsonReuters
Methodology → Profitability → QA
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
Source: ThomsonReuters
Methodology → Profitability → QA
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
Source: JP Morgan
Methodology → Profitability → QA
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)
Source: JP Morgan
Methodology → Profitability → QA
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
Methodology → Profitability → QA
• Scheduled/expected vs. Unscheduled/unexpected
Investors react more strongly to unscheduled/ unexpected news than scheduled/ expected
Generating Alpha
Source: RavenPack, FactSet, Macquarie Research, September 2012
Methodology → Profitability → QA
• News type Event Study Results
Generating Alpha
Source: RavenPack, FactSet, Macquarie Research, September 2012
Methodology → Profitability → QA
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
Methodology → Profitability → QA
Quantifying News - Where it fails?
• News analytics were taught that ‘Osama-Bin-Laden’, and ‘killed’ had -ve sentiments for the markets
Methodology → Profitability → QA
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
Methodology → Profitability → QA
Quantifying News - Where it fails ?
• On Sep. 7, 2008 Google’s newsbotspicked up an old 2002 story about United Airlines possibly filing for bankruptcy
• UAL stock dived immediately
Source: Google Finance
Methodology → Profitability → QA
Quantifying News - Where it fails?
Methodology → Profitability → QA
• Dow Jones dropped 0.8% on 23 Apr 2013
– Reasons:
• Twitter account of news publisher hacked – false news of White house explosion
• News Analytics based automated traders reacted to it
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
Methodology → Profitability → QA
Methodology - the science behind quantifying news
Profitability - does it really make money
Q&A
Agenda
Methodology → Profitability → QA
Contacts
For 4-month Executive Program in Algorithmic Trading:
E-PAT: 4 month weekend online program (3hrs every Sat + Sun)
• Statistics
• Quant Strategies
• Technology (programming on algorithmic trading platform)
For algorithmic trading advisory: [email protected]
To reach me directly: [email protected]
Methodology → Profitability → QA
E-PAT
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
QI’s E-PAT course
Methodology → Profitability → QA
E-PAT
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
E-PAT course structure - module I
Basic Statistics
Advanced Statistics
Time Series Analysis
Probability and Distribution
Statistical Inference
Linear Regression
Correlation vs. Co-integration
ARIMA, ARCH-GARCH Models
Multiple Regression
Stochastic Math
Causality
Forecasting
Methodology → Profitability → QA
E-PAT
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
E-PAT course structure - module II
Programming
Technology for Algorithmic Trading
Statistical Tools
Intro to Programming
Language(s)
Programming on Algorithmic
Trading Platforms
System Architecture
Understanding an Algorithmic
Trading Platform
Handling HFT Data
Excel & VBA
Financial Modeling using R
Using R & Excel for Back-testing
Methodology → Profitability → QA
E-PAT
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
E-PAT course structure - module III
Trading Strategies
Derivatives & Market Microstructure
Managing Algo Operations
Statistical Arbitrage
Market Making Strategies
Execution Strategies
Forecasting & AI Based Strategies
Pair Trading Strategies
Trend following Strategies
Option Pricing Model
Dispersion Trading
Risk Management using Higher
Order Greeks
Option Portfolio Management
Order Book Dynamics
Market Microstructure
Hardware & Network
Regulatory Framework
Exchange Infrastructure &
Financial Planning (Costing)
Risk Management in Automated
systems
Performance Evaluation &
Portfolio Management
Methodology → Profitability → QA
E-PAT
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
Project work
E-PAT course structure - project
Methodology → Profitability → QA
Copyright © 2015 by QuantInsti Quantitative Learning Private Limited.
Although great care has been taken to ensure accuracy of the information
in this presentation – however the author (and QuantInsti) accepts no
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Disclaimer
Methodology → Profitability → QA
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