It is widely acknowledged that text analysis offers a view into a massive world of unstructured data. This data offers a goldmine of tradable information ranging from corporate regulatory filings to central bank communications. But, like other areas of “big data,” this material is virtually useless without narrowing the focus. This talk will examine the ways in which deep domain expertise can help refine text analysis data into a powerful investing tool. This presentation was part of the QuantCon 2015 Conference hosted by Quantopian. Visit us at: www.quantopian.com.

1. Prattle AnalyticsTradable Data From Market Chatter
Using Domain Expertise
To Improve Text Analysis
--Evan A. Schnidman

2. ▶ Everyone seems to love mining unstructured data.
▶ How much time do you spend on it?
▶ Must impose STRUCTURE to make using that data efficient.
The Era of Big Data

3. ▶ Data is not limited to numerical.
▶ Text is a goldmine of information.
▶ Examples in Finance:
-Corporate Communications
-Regulatory Filings
-Central Bank Communications
▶ Deciphering this information
without structure is virtually impossible.
Data is Everywhere

4. ▶ Expertise allows us to impose structure on otherwise messy data.
Domain Expertise

5. ▶ Good Buzzword minus Bad Buzzword == Sentiment
▶ Domain expertise allows for much more refined analysis.
Traditional Sentiment Analysis

6. ▶ Central Bank communications are complex and important
▶ Focus today is Federal Reserve
Example: Central Banks

7. The Briefcase Watch
Traditional Fed Watching
Not Much has Changed

8. Failed Attempts
▶ Experts are biased and fail to be comprehensive
▶ Simple text analysis dictionaries don’t work for
Fed Speak and other complex language
▶ Ex. “modest” v. “moderate”
Necessary Components
▶ Must use expertise to train the system based on
whole communications
▶ Market response matters (Hawkish v. Dovish)
Experts in “Fed Speak”

9. Scaling Data
+ =
Enough documents
can eliminate bias
Expertise allows scaling
based on whole documents End result is whole
communications scored
in orderly fashion

10. Resulting Data:
▶ Comprehensive
▶ Unbiased
▶ Quantitative
▶ Fast
Many Possible Uses
▶ Eliminate post-hoc
hedging on CB policy
▶ Forecast based on
established correlations
▶ Add as a signal in
multifactor model
Qual Turned Quant
Trend matters more than value!

11. ▶ Alpha across asset classes, not just Fixed Income
▶ Mitigates downside risk, especially with Equities.
▶ Beats Buy and Hold and Trend Following
▶ Low correlation to commonly used strategies
▶ Better performance with FOREX because both sides of currency pair trade.
Backtested Data
Graph Courtesy
of Mavenomics

12. ▶ Method translates across wide variety of financially important texts
▶ Regulatory and shareholder documents for individual equities
▶ Other regulatory information (Dodd-Frank, FDA, EPA etc.)
Other Applications

13. ▶ These tools are MORE efficient than what big financial institutions do.
▶ Result is tradable data from any number of market moving texts.
Text is Data
Buzzword Approach Domain Expertise

14. Prattle AnalyticsTradable Data From Market Chatter
QUESTIONS?
Evan A. Schnidman
eas@prattle-analytics.com

15. Prattle AnalyticsTradable Data From Market Chatter
EXTRA SLIDES
Evan A. Schnidman
eas@prattle-analytics.com

16. U.S. Federal Reserve
European Central Bank
Bank of England
Bank of Canada
Bank of Japan
Reserve Bank of Australia
Bank of Korea
Reserve Bank of India
Swedish Riksbank
Reserve Bank of New Zealand
Central Bank of Mexico
Central Bank of Brazil
Central Bank of Russia
South African Reserve Bank
Bank of Israel
Central Bank of Turkey
Central Bank of Taiwan
Swiss National Bank
List of Central Banks

17. Tree Maps
Tree Maps
Courtesy of
Mavenomics

18. Forecasting

19. Backtesting
Independent Backtesting Results
The following results are from a fund that independently tested the Fed Playbook data in January of 2015. This fund primarily
utilized a standard return to volatility futures trading strategy based on a common risk parity model to test the FPSI data from
January 2000 to December 2014. All transactions costs are built into the testing. Their findings indicated the following:
• The FPSI is a superior trade signal to both of the most common trading strategies, “Trend Following” and “Buy and Hold.”
EQUITIES
• Using a simple portfolio of the S&P 500, both Trend Following and Buy and Hold generate returns of roughly 27% over the testing period.
• The FPSI generates risk adjusted returns of 58%, more than double the most commonly used trading strategies.
• FPSI returns were generated with almost perfect long/short balance.
• The FPSI only has a 0.3 correlation to Trend Following and just a 0.1 correlation to Buy and Hold, so the FPSI can be used in
concert with these established strategies to generate even higher returns.
• The FPSI also proved to be a superb indicator of downside risk, even beating Trend Following.
• Optimal holding periods for an equity portfolio traded on FPSI data is 2-3 months.
FOREX
• Examining only the U.S. Dollar and Euro based on just U.S. data indicates that the FPSI outperforms existing currency trading models.
• Trend Following tends to dominate the currency trading space because over the sample period it generated a 55% return.
• Over the same period the FPSI generates over 70% returns.
• The FPSI only has a 0.17 correlation to Trend Following, so these two strategies could be used in concert to generate even higher returns.
• Optimal holding periods for a currency trade based on the FPSI data is 10-15 days.
• These returns are only taking into account Prattle Analytics’ data on the U.S. Federal Reserve, since Prattle also has data on
the European Central Bank (along with more than a dozen other central banks), this information could be used to better
understand the other side of the currency pair trade and generate even greater returns.

20. Prattle AnalyticsTradable Data From Market Chatter
Using Domain Expertise
To Improve Text Analysis
--Evan A. Schnidman
eas@prattle-analytics.com