Recorded Future News Analytics for Financial Services

Recorded Future
David Moon
Global Head of Financial Services
Bill Ladd
Chief Analytic Officer

What is Recorded Future?
3/1/2011
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We believe that the content of the web has predictive power.
So...
We’ve harvested and organized the only real-time source for past, planned and speculative events on the web.
To...
Allow users to “slice-and-dice” the web to make predictions.

Web is Loaded with Predictions
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Silicon Valley executives head to Vail, Colo. next week for the annual Pacific Crest Technology Leadership Forum
Drought and malnutrition hinder next year’s development plans in Yemen...
“Strange new Russian worm set to unleash botnet on 4/1/2012...”
The carrier may select partners to set up a new carrier as early as next month
“According to TechCrunch China’s new 4G network will be deployed by mid-2010”
“... Dr Sarkar says the new facility will be operational by March 2014...”
“2010 is the year when Iran will kick out Islam. Ya Ahura we will.”
“...opposition organizers plan to meet on Thursday to protest...”
“Excited to see Mubarak speak this weekend...”

The RF Stack
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Application
Daily Average of Scores
API / FTP
RF Scores & Aggregates
Client Scores & Aggregates Clients can use the same underlying date to define their own
Scores: Proprietary sentiment, momentum, event score, etc
Aggregates
Aggregates
RF Scores – Sentiment & Momentum
Scores
Time
Pub Date
Harvest Date
Inferred Dates
Recorded Future Driven Linguistic Processing yields a corpus that is
Structured
Relationship driven
Machine-Readable
Back-testable
Events
Entities
Entities & Events –Extracted & Normalized
Sources

Case Studies
Liquidity Management
Predicting liquidity with media coverage
Short Term Trading
“Future event” study
Strategy Allocation
Measuring investment strategy crowdedness with online media.
Risk Modeling
Anticipating future volatility with media sentiment and macroeconomic discussion.
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Case 1 – Liquidity ManagementPredicting Liquidity with Momentum
Recorded Future momentum contains predictive information for dollar volume of S&P 500 companies.
Control for trailing market volume on a 1 and 20-day basis.
Use 1-day trailing momentum.
Call:lm(formula = Dollarvol.1 ~ 0 + lDollarvol.1 + smaDvol.Dollarvol.1 + smaxlMo, data = seriesdf)Residuals:Min 1Q Median 3Q Max -5.039e+09 -2.215e+07 -2.284e+06 1.813e+07 1.597e+10 Coefficients:Estimate Std. Error t value Pr(>|t|) lDollarvol.1 0.513193 0.003237 158.54 < 2e-16 ***smaDvol.Dollarvol.1 0.471645 0.003817 123.56 < 2e-16 ***smaxlMo 0.077162 0.015683 4.92 8.67e-07 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 170900000 on 72109 degrees of freedomMultiple R-squared: 0.8539, Adjusted R-squared: 0.8539 F-statistic: 1.405e+05 on 3 and 72109 DF, p-value: < 2.2e-16
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Case 2 – Short Term TradingFuture Event Distributions
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Non-earnings related events are negative.
We controlled for earnings and non-earnings related news.
The study queried instances where there was advance notice of specific future events.
Events defined as one day long with S&P 500 constituents
These typically provided one to three days advance notice
~19,000 unique events satisfied these criteria
~1-3 days
t(days)

Case 2 – Short Term TradingNews “Should” be Priced in Immediately
Buy the rumor, sell the news describes earnings related events.
Market adjusted returns increase on approach to the event day and decline thereafter.
It does not describe non-earnings related events.
No increase in returns on approach to event-day
Statistically significant increase in volume (0.3σ) and decrease in market adjusted returns.
Non-earnings related events were net negative.
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Typical Publication Day
Predicted Event Day

Case 3 – Strategy AllocationQuantifying Strategy Crowdedness
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Recorded Future data yielded an inverse correlation between the performance of a momentum strategy and the business media’s discussion of momentum.
The study introduced a synthetic linguistic score.
Relied on standard API queries
Scored fragments based on momentum-related terms
Increased discussion of momentum-related trading correlated with declining returns.
Inverse correlation with $NAV/share of momentum mutual fund
Monthly correlation of -0.56 over the past year

Case 4 – Risk ModelingVolatility Forecasting Methodology
Data Extraction
Extract all references to S&P 500 Companies from Recorded Future’s structured content database from January 1, 2009 to December 9, 2010.
Includes synonyms (IBM vs. International Business Machines, etc.)
Reduce to only mentions on “Blog” sources.
Compute sentiment and momentum of text surrounding references to the Index over the time period.
Data Aggregation
Compute daily series of count-weighted mean sentiment and momentum.
Modeling
Calculate exponential moving averages of these values over a 26-day trailing window.
Regress against 1-month forward realized volatility of S&P 500.
Model Assessment
Economic evaluation of model parameters – do they make sense?
Comparison to other volatility metrics – how does the signal compare?
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Case 4 – Risk ModelingModel Summary
Call:
lm(formula = spyvol ~ vix + emamo + emaneg, data = blogus)
Residuals:
Min 1Q Median 3Q Max
-0.0087503 -0.0020655 -0.0004415 0.0020463 0.0100361
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.237e-02 2.460e-03 -5.028 7.03e-07 ***
vix 3.938e-04 2.511e-05 15.681 < 2e-16 ***
emamo 2.337e-02 8.164e-03 2.863 0.00439 **
emaneg 3.204e-01 3.631e-02 8.824 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.003263 on 478 degrees of freedom
(25 observations deleted due to missingness)
Multiple R-squared: 0.6867, Adjusted R-squared: 0.6848
F-statistic: 349.3 on 3 and 478 DF, p-value: < 2.2e-16
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Regressors are VIX value, and 28-day EMAs of average momentum and negative sentiment in text surrounding S&P500 companies.
Controlling for VIX, an increase in chatter around S&P 500 companies and an increase in negative sentiment around S&P500 companies lead increases in one-month forward realized volatility.
Positive sentiment NOT a statistically significant term in this model. Volatility driven by fear, not euphoria?
R-squared of 0.68 respectable compared to VIX’s ability to predict 1-month forward volatility – R-squared 0.63.
RF data orthogonal to market data – controlling for VIX leads to models with R-squared > 0.63

Getting Started – Data & Aggregates
Data Instances
Sources & Documents
Entities & Events
Canonical events
Entity identifiers: tickers, industry taxonomy
Time
Publication Date
Event Date
Calculated Scores
Momentum, Sentiment
Aggregates
US equities aggregates
Daily composite momentum and sentiment scores for constituents of the Russell 3000
Custom aggregates built on data elements
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Canonical info
Sentiment
Momentum
Event time
Co-occurring entities
Source metadata
Document metadata
RF State Data
Entity information

Access – Historical & Live Data
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Recorded Future Web Service API
Recorded Future FTP Archive
Data Formats – JSON, CSV
Historical Data Delivery – API, FTP
API – Historical results from raw data via web-service calls
FTP – Files of aggregates, and bulk history
Live Data Delivery – API
Customized calls – as frequently as intra-day
RF Aggregates – calculated daily
JSON HTTP
Request
.zip archive
csv
aggregates
json/tsv
instances
FTP Request
JSON/CSV
Response
Historical Batch Download
Live Download
Load RF Data
RF Customer Analytic Environment
(R, Matlab, Java, Python, Excel, etc.)

Applications – Slicing the Data
Case Studies, revisited
Liquidity Management
Pull aggregate Day/Company momentum data for S&P 500
Short Term Trading
Pull instance data for S&P 500 companies where publish date is before event date
Strategy Allocation
Pull instance data where document category is “Business/Finance” and score fragments based on word/phrase choice
Risk Modeling
Pull aggregate momentum and sentiment data for the S&P 500 Companiesfor specified time period
Different slices entail unique media-analytic feeds
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Summary
Recorded Future provides the world’s only real-time source of past, planned and speculative events.
Designed for clients to create unique media-analytic feeds via web-services API and FTP access.
Applied to liquidity planning, short term trading, strategy allocation, risk modeling, among other scenarios
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Recorded Future News Analytics for Financial Services

by Chris Holden on Mar 03, 2011

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Recorded Future News Analytics for Financial Services — Presentation Transcript