Presentation by: Peter Hafez Director of Quantitative Research, RavenPack
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
and thousands more…
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Outline
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• Here’s what we have learned so far….and this is where we’re
going!
• Multi-dimensional predictive modelling
• The impact of news across company “networks”
– Case study: trading on economically linked companies
– Case study: trading on news linked companies
• Concluding remarks
• Sentiment & news volume are valuable inputs into an investment decision process
• The “sweet spot” trading horizon is measured in hours, days, and weeks
• Relevance and Novelty filtering is important, especially as you expand source coverage
• Event sentiment is stronger than story level sentiment
• Applying news analytics as an overlay helps address turnover
• Context and signal sophistication is becoming increasingly important
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Here is what we have learned so far
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• Understanding the impact of news across company networks is becoming the new “cutting-edge”
• The same goes for understanding where news is first “broken” and how it’s being disseminated to market participants
• Social media analytics will continue to attract a great share of interest
• Going social increases the importance of a multi-language approach
• The “battle against noise” will continue to intensify
• “Bigger data” will increase the need for predictive analytics
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This is where we’re going!
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• Our most successful clients apply some sort of machine learning or multi-dimensional approach for signal generation
• Context is key when addressing signal decay i.e. extending signals into longer horizons
• To tackle the relatively quick decay of news analytics signals, RavenPack has partnered with the predictive analytics company Arialytics
• The objective is to address the non-quant or longer-term investment community
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Transforming news into predictive analytics
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
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Tactical asset allocation - Using news-based predictive analytics
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• Together, we have created a set of industry-level “predictive analytics” covering the US economy
• Additional studies show great promise for single equities
• In producing these analytics, more than 15,000 fields were considered including both news-based factors and more common factors
• For evaluation purposes, we create a long-only and 130/30 portfolio using equal weight, a monthly investment horizon, and daily re-balancing
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Performance summary (industry) - 10bps round trip
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
Annualized Statistics 130/30
Equity Portfolio
Long-Only
Equity Portfolio
Equal Weight,
Long-Only Equity
Portfolio
S&P500
Excess of Rf Avg. Excess Return 13.9% 12.5% 10.9% 10.7%
Sharpe Ratio 0.90 0.79 0.63 0.62
Excess of
S&P 500 Avg. Excess Return 3.2% 1.7% 0.1% 0.0%
Portfolio Turnover 727% 350% 7.0% NA
Max. Drawdown 14.0% 15.6% 19.6% 19.4%
Max. Drawdown Recovery days 191 193 219 207
2010-02 :: 2013-10
• The long-only and 130/30 portfolios outperform the S&P500 in both absolute and relative performance
• The portfolios offer improved drawdown statistics
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Cumulative returns & turnover
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-10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Feb-2010 Aug-2010 Feb-2011 Aug-2011 Feb-2012 Aug-2012 Feb-2013 Aug-2013
Cu
mu
lati
ve R
etu
rns
Long Only 130/30 Benchmark (Equal Weight)
Annualized
Statistics
130/30
Equity Portfolio
130/30
Equity Portfolio
Long-Only
Equity Portfolio
Long-Only
Equity Portfolio
Excess of Rf Avg. Excess Return 13.9% 13.0% 12.5% 11.8%
Sharpe Ratio 0.90 0.83 0.79 0.76
Portfolio Turnover 727% 380% 350% 165%
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News impact across company networks
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• News on linked companies drives stock prices. That being within a given market, across the competitive landscape, or across the supply chain
• Relationships can be temporary or somewhat permanent in nature e.g. M&A activity vs. partnerships
• The more complex the relationship, the more time analysts require to process new information
• The delay in information processing is hypothesized to be the key driver of return predictability across related companies
• This begs the question: what about “information” around supplier or customer companies?
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Trading on linked entities
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• We rank customer and supplier companies according to their previous 1-month stock returns (i.e. “information”)
• We dynamically decide on the “direction of information” on a per sector basis (customer information can drive supplier stock prices and vice versa)
• Supplier or customer companies are sorted by their news volume abnormality depending on the current “direction of information”
• Long-short portfolios are created by going long (short) companies with high (low) momentum but low (high) news volume abnormality
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Trading on supply-chain relationships
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• Information around economically linked companies impact stock prices across the supply chain
• Including abnormal news volume, improves on absolute performance of the 1-month long-short strategy
• Sharpe Ratio of 1.5 with annualized returns of 15.4%
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News-linked entities (co-referencing)
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• To address static relationship and non-disclosure concerns, we propose using news co-referencing as a real-time measure of relatedness
• RavenPack Relationship Analytics include information about the companies involved, the top event driving a relationship, and their joint co-reference score
• Potential applications of news co-referencing
‣ New relationships: detect new relationships as they occur in real-time
‣ Existing relationships: understand how existing relationships change over time, i.e. known competitor, customer or supplier relationships
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Trading on news-linked companies
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• We rank source companies according to their 1-week price returns (i.e. “information”)
• Companies are then sorted based on their co-reference score
• Two different approaches: (1) trade the source company, or (2) trade the related company
• We equally weight the return contribution of each relationship i.e. companies with more relationships will hold greater overall weight
• We enter our portfolios at the close price on time t+1, and exits at t+6
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Trading on news-linked companies - Focusing on source companies
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• Only companies with “current” relationships are being considered
• At least two co-references are required, and a relationship is required to account for at least 5% of all co-references for the company
• Companies with more relationships hold greater portfolio weight
Long: losers with low co-reference score Short: winners with high co-reference score
Long: winners with low co-reference score Short: losers with high co-reference score
15 ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
Trading on news-linked companies - Focusing on source companies
IR: 0.72
IR: 0.66
Jan 2010 – April 2014 Jan 2003 – Dec 2007
IR: 0.90
IR: 1.26
IR: 0.77
IR: 0.51
Non-crisis periods outperform crisis periods (IR of 1.08 vs. 0.66)
IR: 0.77
IR: 0.84 IR: 0.38
IR: 1.21
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News-Linked Companies & Top Events
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• When trading related companies, we find little to no value conditioning on the Co-Reference Score
• Price impact depends on the type of relationship between two companies
• Relationships can be inferred from:
‣ Information on the supply-chain or competitor landscape
‣ Top events detected in the news
• We take the latter approach by focusing on the Top 3 events (business-contracts, partnerships, and product-releases)
17 ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
Trading on News-Linked Companies - Focusing on Source & Related Companies (Top Events)
IR: 0.78
IR: 1.02
IR: 0.64
Jan 2010 – Mar 2014
Trading both source and related companies provides diversification benefits
Jan 2003 – Dec 2007
IR: 0.82
IR: 1.44
IR: 1.02
IR: 1.31
Stable performance across crisis and non-crisis periods (IR of 1.11 vs. 1.10)
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Summary
ravenpack.com | [email protected] | AMERICAS Tel: (646) 277-7339 | EMEA-APAC Tel: +44 (0) 782 783 8282
• Combining news analytics with more traditional factors can help you extend the value into longer investment horizons
• Including news analytics as part of a multi-dimension return prediction process not only improves absolute/relative returns, it also improves downside risk
• Identifying linked companies help you make informed decisions on existing or new relationships between companies
• Tracking media attention can help you to better understand information “spill-over” across the supply-chain
• Tracking news co-referencing and “Top events” help you to identify stocks that are more likely to “revert”
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Contact: Peter Hafez Head of Quantitative Research [email protected] Website: www.RavenPack.com Blog: www.SentimentNews.com Web: www.ravenpack .com Blog: www.SentimentNews.com
Questions?
Hugh Taggart Head of Sales & Business Development [email protected] Tel: +44 782-783-8282
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