Quantifying News For Automated Trading - Methodology and Profitability

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

Historical Perspective - I
Rothschild:
family network spread
across Europe
→
financial information
obtained before peers
e.g.
Knowledge of Battle of
Waterloo result
→
one full day earlier

Historical Perspective - II

Historical Perspective - III
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

Computer programs that scan news articles & quantify them :

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

-> 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”

-> 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 ?

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

Quantifying News - Factor 1

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 ?

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

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

Is Sentiment good enough to quantify a news report?

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

How is relevance scored ?

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)

Issues with calculating 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
• Often the news article is not reported in its entirety, but in
multiple spurts
– Alert
– News Article
– Update
– Append

multiple spurts
– Alert
– News Article
– Update
– Append
• Moreover, multiple news
sources report same news

multiple spurts
– Alert
– News Article
– Update
– Append
• Moreover, multiple news
sources report same news
• News also cause price
changes which themselves
become news

• 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

How is novelty measured ?

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

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

Types of news based on time of news report
• Asynchronous / unexpected
• Synchronous / fixed releases

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…

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

Quantifying News - 5.ii. Search Trends

Quantifying News - 5.iii. Social Media

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

Quantifying News – Market Psyche
Source: ThomsonReuters

Methodology - the science behind quantifying news
Profitability - does it really make money
Q&A
Agenda

Is it profitable ?

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

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

• 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)
Source: More Than Words: Quantifying Language to Measure Firms’ Fundamentals Tetlock,Saar-Tsechansky &
Macskassy

Late endofday 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

Long short strategy returns

Filtering sentiments increase profits
Increasing threshold from 90 to
95 percentile increases returns
from 55 to 138 bps in 3 days

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
Source: Trading Strategies to Exploit News Sentiment – Wenbin Zhang & Steven Skiena

Small cap firms more profitable
Smaller Cap firms show greater response to extreme sentiment
news event
(bigger firms have greater scrutiny)
Source: Leinweber & ThomsonReuters

Filter & trade fewer stocks
• More is not better. Quality over quantity
• Trading only stocks with very high sentiment/relevance is
better

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

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

Bigger moves happen when there is news in
• Stocks with low beta (i.e. surprises happen to sleepy stocks)
Surprises are more profitable

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

• 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
Source: JP Morgan

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

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

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

• 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

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
Source: Google Finance

• 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

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 (programming on algorithmic trading platform)
For algorithmic trading advisory: contact@iragecapital.com
To reach me directly: rajib.borah@iragecapital.com

E-PAT
Statistics and
Econometrics
Financial Computing &
Technology
Algorithmic &
Quantitative Trading
QI’s E-PAT course

E-PAT
Statistics and
Econometrics
Technology
Algorithmic &
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

E-PAT
Statistics and
Econometrics
Technology
Algorithmic &
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

E-PAT
Statistics and
Econometrics
Technology
Algorithmic &
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

E-PAT
Statistics and
Econometrics
Technology
Algorithmic &
Project work
E-PAT course structure - project

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
liability or warranty for the precision, correctness or completeness of any
statement, estimate or opinion. QuantInsti also accepts no liability for the
consequences of any actions taken on the basis of the information
provided.
The slides of this presentation cannot be taken separately from the whole
set of slides.
Prior approval from QuantInsti is necessary before usage of this
presentation for educational and (or) commercial purposes.
This document provides an outline of a presentation and is incomplete
without the accompanying oral commentary and discussion.
Disclaimer

Contact Us
To Learn Automated Trading
Email: contact@quantinsti.com
Connect With Us:
SINGAPORE
11 Collyer Quay,
#10-10, The Arcade,
Singapore - 049317
Phone: +65-6221-3654
INDIA
A-309, Boomerang,
Chandivali Farm Road, Powai,
Mumbai - 400 072
Phone: +91-022-61691400

Quantifying News For Automated Trading - Methodology and Profitability

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Quantifying News For Automated Trading - Methodology and Profitability

Similar to Quantifying News For Automated Trading - Methodology and Profitability (20)

More from QuantInsti

More from QuantInsti (20)

Recently uploaded

Recently uploaded (20)

Quantifying News For Automated Trading - Methodology and Profitability

Editor's Notes