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Incorporating News and Sentiment Analysis into Investment and Trading Strategies, Richard Brown
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Incorporating News and Sentiment Analysis into Investment and Trading Strategies, Richard Brown


News is and always has been a major force in driving financial markets. Moreover, its impact is growing more immediate. Technology is driving the volume and rate of change of the news to the point …

News is and always has been a major force in driving financial markets. Moreover, its impact is growing more immediate. Technology is driving the volume and rate of change of the news to the point where it has overwhelmed people's ability to exploit it effectively.

Technology, of course, also has an answer to this problem. It now is possible to incorporate news in the investment and trading process in ways that were not possible just a few years ago. Doing so enables analysts and traders to respond to the ever greater torrent of information faster, more consistently and with increased accuracy. It now is possible to work with breaking news using sentiment and other news analytics which make it possible to better exploit market inefficiencies and more effectively manage event risk.

Machine readable news and sentiment analysis has often been categorized by use only by those most sophisticated firms operating secretive high frequency black box trading strategies. In this session, we will explore some practical uses applicable across all trading frequencies and short to mid-term investment horizons.

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  • Timestamp to millisecond (corresponds to RNA) Company Identifier Contextual Relevance (substantiveness score) – point out .29 in first row – low on scale from 0 – 1.0; read headline, not apparent story is about IBM – full text mining produces casual mention of IBM, hence low relevance score -- vs second line 1.0 clearly about IBM when reading headline Prevailing Sentiment 1 = +; 0=neutral; -1 = negative – which of the next three scores is the highest POS/NEG/NEUTRAL – expressed as probabilities, additive to 1.0; studies have shown it’s the relationship between the scores, not the scores themselves which have the most value; we focus on Pos-Neg, but you might want ratio; vary thresholds for optimal signal (I might want pos/neg >3, but you’re may be more conservative and want the ratio to be >7 Linked counts measures novelty/repetition – takes vocabulary fingerprint of article and compares against other articles. To the extent it looks similar, it adds to linked count (I’ve heard it before and it’s linked to this indexed item – index in another field) Item type – we score alerts, articles/stories, updates (appends); and corrections (overwrites) Headlines Topic Codes – MRG=Merger & Acquisitions; RCH=Research; RES=Results; RESF=Results Forecast Hypothetical trade – Buy IBM when Relevance>.5 (substantive article) and ratio of pos/neg >3 (or 5 for more conservative) and low linked count (0, 1, or 2 in HF (12 hr window) or 7 over last week for lower frequency traders; double the order if about results or results forecast
  • Pricing (blue) and sentiment (orange) on same axis Y axis range 0-100% where 0 is smallest value and 100 is the largest value in each data set; everything else is normalised in between. Sentiment calculated by taking (Pos-Neg) x Relevance netted on a daily basis Channel of normal sentiment is between 25% and 35% on the graph Spikes above/below this range indicate better/worse news than normal A series of positive spikes (points 1-6 shaded in green), lead to the longer term share price increase outlined with first black line. A set of negative spikes (points A, B) precede downward stock price movements Next series of positive spikes (points 7-10) drive continued stock price appreciation
  • Setup: Blue is price; orange is sentiment, on same axis 0-100% - 0 is smallest value in independent data set, 100 the largest, everything else is normalized in between Cumulative Sentiment = Sum of all articles’ (Pos – Neg) x Relevance Bad news day, big neg price movement Jan 19 (IBM reported earnings) Large run up in sentiment afterwards preceding large price movement Strong indications that cumulative sentiment (orange) precede large price movements
  • Top graph = price of IBM (reveal 1 st ) Second pane is a graphical representation of the stories – green = pos; blue=neutral; red=neg; sized by relevance (small items = lower relevance); shape is item bype (stars=alerts, squares=articles, diamonds=appends, no overwrites, but they’d be triangles); scale is how large (pos-neg) * relevance (click 2 nd time) Third pane represents stories (click 3 rd ); you can see that these stories are driven by the china growth fears Click fourth (good way to visualize sentiment for humans to confirm/contrast quant signals)
  • News flow is a good indicator of volume and volatility; this chart looks at news volume (top pane); coincident with large trading days (third pane), especially when negative news days (2 nd pane) Important to look at relevant news (filtering by high relevant items (1.0) moves headline count from 63 in first highlighted bar to 39); also need incorporate relative amount of news – news on Intel and Dell also likely to move IBM; need relative count of news on IBM vs tech sector


  • 1. INCORPORATING NEWS ANALYTICS INTO INVESTMENT AND TRADING STRATEGIES Richard Brown Global Business Manager, Machine Readable News Sentiment Symposium April 13, 2010
    • Differentiated content
      • Generate alpha (offense)
      • Manage event risk (defense)
    + = Content But how?
    • News flow is a good indicator of volume and volatility.
    • Pricing movements accompanied by news tend to be momentum in nature; those with a lack of news tend to reverse to average trends.
    • The market tends to overreact when there is a lot of news on something and under-react when there is a small quantity of news.
    • For direction and magnitude, find cause:effect relationships
    • Scores text across three dimensions
      • Sentiment (Author tone – positive, neutral, negative)
      • Relevance (Is it substantively about the company?)
      • Novelty (How unique is the article?)
    • Combinations of these factors enhance traditional approaches to understanding the market impact from news
      • News = volume and volatility
      • Momentum and mean reversion
      • Over/under reactions
      • Signals for returns: Direction and magnitude
    Powered by:
    • Circuit breaker / halt trading alert (Wolf detection)
    • News flow algorithms (more participatory algos)
    • Alpha generating signal
    • Risk Management – Quantify “event risk” and manage portfolio volatility
    • Compliance monitoring – detect potential market abuse
    • Post trade analysis – why did the algo/strategy not work?
    • Stock screening tool (good/bad news stocks)
    • Fundamental research – measure company sentiment, peer analysis, aggregate for market/sector outlook
    • Trader support – confirming/contrarian trading signals, volatility signals
  • 6. HOW TO MAKE MONEY WITH THOMSON REUTERS NEWS ANALYTICS Buy on good news Sell on bad news Outperform S&P500 by 5000 basis points over a 60 day period! S&P1500 stocks in 2008; Daily items >50; Pos vs Neg >50%
  • 7. THOMSON REUTERS NEWS ANALYTICS EQUITIES SAMPLE OUTPUT Relevance: 0 - 1.0 Prevailing Sentiment: 1, 0, -1 Positive, Neutral, Negative: Probabilities which sum to 1.00, providing more granular sentiment Novelty represented by Linked Counts: 12h, 24h; 3d, 5d, 7d Item Type: Alert, Article, Updates, Corrections Headline: Alert or Headline text Topic Codes: What the story is about; RCH=Research; RES=Results; RESF=Results Forecast; MRG=Merger & Acquisitions . . . Other metadata: Index IDs, linked references, story chains, item counts
  • 8.
    • Daily News & Price data in the same view (Jan-June 2007)
    • Daily Net Positive Sentiment [orange] : Daily sum of each item's Relevance*(Positive - Negative Sentiment)
    • Average Daily Price [blue]
    • Y-axis normalised to go from 0-100% of the respective values
    • Event above shows direct correlation between dip in News Sentiment and Price on a single day
    • Series of Events above show close correlation between upturns in News Sentiment and Price over a
    • sustained period of a few days (multiple short term signals lead to longer term movement)
    IMPACT OF DAILY NEWS SENTIMENT ON PRICE Dip in net positive sentiment and price Rise & fall in net positive sentiment lead to similar movements in price Upturns in Net Positive Sentiment correlate to upward price momentum over period of a few days
    • What happened here to drive the price down at the end of February?
    • Conclusions drawn:
    • Daily or intra-day sentiment can be a powerful indicator for stock price movements
      • Real-time for very rapid decision-making – market making, high frequency
      • Daily sentiment impact into following day/week’s price movement
      • Multi-day signals for longer-term movements
      • Weight and filter by relevance and novelty
    1 4 3 2 7 5 6 8 9 10 B A
  • 10. IMPACT OF CUMULATIVE NEWS SENTIMENT ON PRICE Overall positive correlation between Price and Cumulative Sentiment
    • Cumulative Sentiment can be powerful measure to predict medium to long-term movements
    • Variations:
      • X day moving averages
      • Relevance filtering and weighting
      • De-duplication
      • Multiple content sources
    • Same downturn as seen previously, but visually a contrarian signal. Why???
    • Drop in IBM’s share price between 2/21/07 and 3/5/07
    • Corresponding drop in IBM benchmark - Special Technology Sector Spider XLK index
    • Broader market factors were influencing the price during this time
  • 12. 1. Can News explain this downturn in Price? 2. Highlight this significant cluster of negative news stories which are only slightly relevant to IBM PRICE TREND AND INDIVIDUAL NEWS 3. News is related to general worries on the China economy in February 2007
    • Human decision support:
    • Analyze price movements
    • Drill into news stories by type, source, sentiment, relevance, topic, other criteria
  • 13. General Observation: spikes in Quantity of News (tall bars in the top view) are co-incident with spikes in trading volume (third pane), especially when negative (second pane). NEWS VOLUME AND TRADING VOLUME
  • 14. S&P500 ENERGY Net number of positive items August 2007
    • Don’t underestimate the complexity
      • Roll up sleeves to understand the data and the output
      • Not all content is created equal
      • Requires comprehensive aliasing capabilities
      • Measuring entity level sentiment is critical
      • Detailed sentiment is important
      • More metadata is better
      • Fault tolerant, fully resilient environment is a must
      • Speed is important
    • Choose a partner you can trust
  • 16. Questions?