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Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
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Recorded Future News Analytics for Financial Services

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  • 1. Recorded Future
    David Moon
    Global Head of Financial Services
    Bill Ladd
    Chief Analytic Officer
  • 2. What is Recorded Future?
    3/1/2011
    2
    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.
  • 3. Web is Loaded with Predictions
    3/1/2011
    3
    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...”
  • 4. The RF Stack
    3/1/2011
    4
    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
    • 5. 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
    • 6. Relationship driven
    • 7. Machine-Readable
    • 8. Back-testable
    Events
    Entities
    Entities & Events –Extracted & Normalized
    Sources
  • 9. 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.
    3/1/2011
    5
  • 10. 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
    3/1/2011
    6
  • 11. Case 2 – Short Term TradingFuture Event Distributions
    3/1/2011
    7
    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)
  • 12. 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.
    3/1/2011
    8
    Typical Publication Day
    Predicted Event Day
  • 13. Case 3 – Strategy AllocationQuantifying Strategy Crowdedness
    3/1/2011
    9
    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
  • 14. 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?
    3/1/2011
    10
  • 15. 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
    3/1/2011
    11
    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
  • 16. 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
    3/1/2011
    12
    Canonical info
    Sentiment
    Momentum
    Event time
    Co-occurring entities
    Source metadata
    Document metadata
    RF State Data
    Entity information
  • 17. Access – Historical & Live Data
    3/1/2011
    13
    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.)
  • 18. 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
    3/1/2011
    14
  • 19. 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
    3/1/2011
    15

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