Quantitative trading using sentiment analysis by Rajib Ranjan Borah 28 June 2016

transcript

Quantitative Trading using Sentiment AnalysisRajib Ranjan BorahDirector & Co-Founder,iRageCapital and QuantInsti

Methodology - the science behind quantifying news

Profitability - does it really make money

Agenda

“The world runs on information and few areas as directly so as in finance”

Methodology → Profitability → QA

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

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”

What is the most critical part of the problem?

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 ?

• 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

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

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 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

• 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

• 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 ?

• 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

Source: ThomsonReuters

Agenda

Is it profitable ?

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

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

• Company figures provided in xml format instead of text• Economic figures provided in binary format instead of textual

news articles

• Company figures provided in xml format instead of text• Economic figures provided in binary format instead of textual

news articlesFor 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 end of 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

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

• Stocks with low beta (i.e. surprises happen to sleepy stocks)• VIX is low (i.e. surprises during calm times)

• 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)

• 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

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

• 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

Agenda

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.comTo reach me directly: rajib.borah@iragecapital.com

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.

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Quantitative trading using sentiment analysis by Rajib Ranjan Borah 28 June 2016

Economy & Finance