There exist thousands of academic research papers written on trading strategies. Learn what these academics found out and how we can use their knowledge in the trading world. The webinar covers: - Overview of research in a field of quantitative trading - Taxonomy of quantitative trading strategies - Where to look for unique alpha - Examples of lesser-known trading strategies - Common issues in quant research Learn more about our EPAT™ course here: https://www.quantinsti.com/epat/ Most Useful links Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B Visit us at: https://www.quantinsti.com/ Like us on Facebook: https://www.facebook.com/quantinsti/ Follow us on Twitter: https://twitter.com/QuantInsti Access the webinar recording here: http://ow.ly/1YwO30dz5FD Know more about EPAT™ by QuantInsti™ at http://www.quantinsti.com/epat/

1. July 11th, 2017
Radovan Vojtko, CEO - Quantpedia
A Pioneer Algo Trading Training Institute Encyclopedia of Quant Trading Strategies

2. MSc in Telecommunication from Slovak University of Technology. Former Portfolio Manager at
Tatra Asset Management (the biggest asset management company in Slovak Republic),
managed over 300+mln EUR in several quantitative funds (focused on multi-asset CTA/trend-
following strategies, market timing and volatility trading).
ABOUT THE SPEAKER
2
Radovan Vojtko
CEO of Quantpedia.com since 2015

3. Who are we:
 Small company, based in Slovak Republic (EU), engaged in a field of quant
trading research ...
Our mission:
 We are building a database of quant trading strategies, our goal is to give
institutional clients opportunity to find new ideas for trading. Database
currently contains more than 350 strategies.
QUANTPEDIA
THE ENCYCLOPEDIA OF QUANTITATIVE TRADING STRATEGIES
3

4. QUANTPEDIA – HOW WE DO IT
4

5.  Academic – 10s of thousands of financial papers are published each year
 Sell side research – part of the research is not publicly available (sent by
brokers/banks to their clients), however big part is freely available
 Special subsection - research published by notable Asset Management companies to
show their expertise to their clients (notable examples AQR, Robeco, Cambria,
Resolve etc.)
 Buy side
RESEARCH IN A FIELD OF QUANT TRADING
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6. ASSET CLASS FOCUS OF QUANT RESEARCHERS
6
 Research on equities is totally dominant
 Cheap/free data on equities, popular asset class among public
 Commodities, currencies and bonds are less explored
Data from Quantpedia.com

7. TIMEFRAME FOCUS OF QUANT RESEARCHERS
7
Data from Quantpedia.com
 Quant trading strategy is a very vague term, historically labelled all
strategies from HFT to systematic long term strategies (systematic value
in equities as an example)
 Strategies with monthly rebalancing are the most popular (reflects
popularity of equity strategies), HFT is under-represented

8.  Again, topics/keywords reflects
popularity of equity strategies
(stock picking, equity long short,
momentum, market timing, value,
earnings effects)
 Trend following, FX strategies and
arbitrage/relative trading
strategies are also popular among
researchers
TOPIC FOCUS OF QUANT RESEARCHERS
8
Top 20 keywords, data from Quantpedia.com

9.  A lot of various classifications, quant strategies dynamically evolve and
whole quant-space is in a constant motion.
 We will present our view of the field
 Main dimensions – Timeframe, Keywords and Asset Class focus
MAIN CATEGORIES OF QUANT STRATEGIES
9

10. 10
MULTI-ASSET CLASSIFICATION

11. BLIND SPOTS
11

12. 12
EQUITY CLASSIFICATION

13. BLIND SPOTS
13

14. LESSER-KNOWN STRATEGIES
14
 PAIRS TRADING WITH COMMODITIES
 PRE-EARNINGS ANNOUNCEMENT DRIFT

15. PAIRS TRADING WITH COMMODITIES
15
 Bianchi, Drew, Zhu: Pairs trading profits in commodity futures markets
 http://eprints.qut.edu.au/32427/1/c32427.pdf
 Trading rules:
 Commodity futures are divided into 5 groups with similar characteristics (energy, base
metals etc.).
 A cumulative total return index is then created for each commodity.
 Pairs are formed inside each commodity group over a twelve-month period (formation
period).
 The top 5 closest pairs are found by looking for the pairs of futures which minimize the
sum of the squared deviations between the two normalized price series.
 The top 5 pairs with the smallest historical distance measure are then traded in the
trading period (the rest of the sample).
 A long-short position is opened when a pair of prices has diverged by more than two
standard deviations and the position is closed when prices have reverted.

16. PAIRS TRADING WITH COMMODITIES
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 Period of rebalancing – daily
 Number of traded instruments – 10
 Backtest period – 1990-2008
 Indicative performance – 1.44% monthly for long short portfolio (~19%
yearly, table III from source paper)
 Estimated volatility – 11.8% yearly, estimated from t-statistic

17.  Ertan, Karolyi, Kelly, Stoumbos: Pre-Earnings Announcement Over-
Extrapolation
 https://mendoza.nd.edu/assets/193974/2016_spring_finance_seminar
_series_peter_kelly_paper_updated_3_22_2016.pdf
 Trading rules:
 Investment universe – US stocks from NYSE, AMEX, NASDAQ
 Stocks are divided into deciles based on the past earnings-announcement
performance (sorted based on the weighted-average of the past 8 quarterly
earnings announcement returns)
 At the beginning of each trading day, the investor considers firms in the pre-
earnings announcement window – 5 days before the earnings announcement date.
 Then he goes long the firms within the top decile and short the firms within the
bottom decile of the extrapolated return measure and holds for five days.
PRE-EARNINGS ANNOUNCEMENT DRIFT
17

18.  Period of rebalancing – daily
 Number of traded instruments – ~90 (based on the estimated sample
size)
 Backtest period – 1991-2014
 Indicative performance – 0.16% daily for long-short portfolio (~39%
yearly, data from table 7)
 Estimated volatility – ~45% yearly, estimated from t-statistic
PRE-EARNINGS ANNOUNCEMENT DRIFT
18

19.  Alpha decay –
 In-sample vs. Out-of-sample performance
 Post-publication performance decay
 P-hacking & Replication problems
 Momentum in Anomalies
COMMON ISSUES IN QUANT RESEARCH
19

20.  McLean, Pontiff - Does Academic Research Destroy Stock Return
Predictability? (2015)
 Out-of-sample and Post-publication return-predictability of 97 variables/equity
factors, 5year horizon
 Glass is half empty – return is 26% lower Out-of-sample and 58% lower
Post-publication
 Glass is half full – 74% of alpha remains Out-of-sample, 42% remains
Post-publication
ALPHA DECAY
20

21.  Hsu, Myers, Whitby: Timing Poorly: A Guide to Generating Poor Returns
While Investing in Successful Strategies (2016)
 Value anomaly preserves its return because most of the people who try
to invest into "value" have bad timing. In reality, investors help to
increase return of "value factor" on the contrary.
PERSISTENCE OF ANOMALIES
21

22.  Data dredging (also data fishing, data snooping, and p-hacking) - is the
use of data mining to uncover patterns in data that can be presented as
statistically significant, without first devising a specific hypothesis as to
the underlying causality ...
 Hou, Xue, Zhang: Replicating anomalies (2017) –
 Abstract: „ ... data library that contains 447 anomaly variables. With
microcaps alleviated via New York Stock Exchange breakpoints and
value-weighted returns, 286 anomalies (64%) are insignificant at the
conventional 5% level. Imposing the cutoff t-value of three raises the
number of insignificance to 380 (85%). „
P-HACKING
22

23. P-HACKING
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 Harvey, Liu, Zhu - …and the Cross-Section of Expected Returns (2013) –
suggests continuously increasing t-stat hurdle based on number of
published factors

24. MOMENTUM IN ANOMALIES – ... LET HARD DATA
PICK OUT WINNERS ...
24
 Huang, Huang: Real-Time Profitability of Published Anomalies (2014)
 Abstract: „... each year recursively pick the best past-performer among
such anomalies over a given training period...“

25.  Can we find profitable strategies in academic research? Yes.
 But:
 Look for „blind-spots“ (assets, timeframes and instruments that are less-covered by
research).
 Performance of anomalies decrease out-of-sample and post-publication (expect at
least 30-60%).
 Beware of liquidity & transaction costs problems, no micro-caps (no small-caps ?), test
value-weighted portfolios.
 Increase t-stat hurdle for multiple tests.
 Let the data pick out winners – momentum in anomalies/factors.
SHORT CONCLUSION
25

26. TAKE THE NEXT STEP WITH EPAT™
26
Over 10000 professionals from 100+ countries
have benefited from QuantInsti’s educational initiatives.
If you want to be a successful Algorithmic Trader,
then enroll for EPAT™ now!
For more information visit us on:
https://www.quantinsti.com/epat/
or Call us on
+91-22-6169-1400 / +91-9920-44-88-77
Next Batch Starts from July 29, 2017!

27. Success Stories
27
Before EPAT™
Fixed income Trader
Becerra Bursatil S.A
"I am very happy with the support provided by the administration team.
Faculty is greatly committed at resolving queries. Having worked at one of the
leading brokerage houses, I would certainly want to get into algorithmic
trading and this is where QuantInsti’s EPAT course will help me."
Marco Nicolás Dibo, Argentina
After EPAT™
Vice President
Argentina Valores S.A

28. Success Stories
28
V. Sankar Narayanan, India
After EPAT™
Quant Analyst
ProAlpha Systematic
Capital Advisors
"I am glad that I got enrolled with EPATTM by QuantInsti™, the dilemma of getting into quant
trading after 20+ years of being a database expert kept me from getting trained in algo but
QuantInatiTM’s EPATTM programme gave me the much needed confidence and I was placed
within few weeks after completing my course."
Before EPAT™
Database Manager
Axis Bank

29. Grab the
opportunity before
the next batch
starts July 29,
2017!
29
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31. Questions?

32. TAKE THE NEXT STEP WITH EPAT™
32
Over 10000 professionals from 100+ countries
have benefited from QuantInsti’s educational initiatives.
If you want to be a successful Algorithmic Trader,
then enroll for EPAT™ now!
For more information visit us on:
https://www.quantinsti.com/epat/
or Call us on
+91-22-6169-1400 / +91-9920-44-88-77
Next Batch Starts from July 29, 2017!