exploiting entity co-referencing in unstructured news

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…

2

Outline


• 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

3

Here is what we have learned so far


• 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

4

This is where we’re going!


• 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

5

Transforming news into predictive analytics


6

Tactical asset allocation - Using news-based predictive analytics


• 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

7

Performance summary (industry) - 10bps round trip


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

8

Cumulative returns & turnover


-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%

9

News impact across company networks


• 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?

10

Trading on linked entities


• 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

11

Trading on supply-chain relationships


• 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%

12

News-linked entities (co-referencing)


• 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

13

Trading on news-linked companies


• 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

14

Trading on news-linked companies - Focusing on source companies


• 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

16

News-Linked Companies & Top Events


• 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)

18

Summary


• 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”

19

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

mailto:[email protected]

http://www.ravenpack.com/

http://www.sentimentnews.com/

mailto:[email protected]

exploiting entity co-referencing in unstructured news

Data & Analytics

americas tel

emeaapac tel

impact of news

news impact

portfolio turnover

transforming news

equity portfolio excess

equity portfolio equal