CTA-Portfolio Construction

CTA-Portfolio Construction
How to Avoid Concentrations and Achieve Stable Performance
Rodex Risk Advisers LLC
Dr. Claus Huber, CEFA, CFA, FRM
May 2014
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Agenda
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7)
8)
Setting the Stage
Detecting Manager Similarities: Self-Organising Maps
Criteria for Selecting Managers
Do Self-Organising Maps Add Value for Manager Selection?
Results: Gross vs. Net Returns
Portfolio Construction
Summary
References
1) Setting the Stage
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Our goal is to build a portfolio of hedge fund managers that performs stable in all
market phases
Purely quantitative approach
Universe: dbSelect platform. Daily data from 2002-2013
Monthly rebalancing
Focus is on avoiding concentrations in the portfolio
Not all concentrations are obvious: for example, one FX manager [M2] and one
equity relative value manager [M7] could have the same [indirect] risk exposures
that get hit.
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Example: in April 2013, the Bank of Japan announced new QE measures, helping the Yen to weaken
[hitting the FX manager M2] and the typical mean reversion tendency of the spread between SPX
and Nasdaq to widen even more [hitting the equity RV manager M7]
How can those indirect risk exposures be identified?
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Correlations also take the upside into account: not suited
We focus only on the downside behaviour by looking at the 5 most severe weekly drawdowns of the
last 52 weeks
2) Self Organising Maps
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SOM were developed in the 1980s by
Teuvo Kohonen
SOM project similar objects on a map
Similar objects are being projected
closely together
Example: SOM with 16 units
Managers with similar risk profiles
appear on the same unit: M2 and M7 on
the same unit
40 of 90 managers appear on the same
unit: similar risk profiles
SOM can be used to identify similarities
in drawdown behaviour: managers with
similar DD behaviour appear on near-by
units
For portfolio construction, we want
managers evenly distributed across the
SOM
M2, M7
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3) Criteria for Selecting
Managers?
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Several performance evaluation criteria are used in the academic literature
For example:
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Sharpe Ratio
Alpha, t(alpha)
Modified Sharpe Ratio [Eling / Schuhmacher (2007)]
Omega [Shadwick / Keating (2002)]
D-Ratio [Koh et al. (2002)
Etc.
Model-free performance statistics, e.g., Sharpe Ratio and Downside-Risk variants,
are more sensitive to detecting performance persistence than risk factor-model
based statistics [Pätäri / Tolvanen (2009)]
Typical risk factor models deploy 5 to 9 risk factors [Fung / Hsieh 1997, 2001]
Some studies find a material impact of different selection criteria on the results
[e.g., Nguyen-Thi-Thanh (2007)]
We also find material performance differences depending on the selection
criterion utilised
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Selection Criteria
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4 different selection criteria deployed:
Alpha, t(alpha), SR are mentioned in the literature to deliver performance persistence
12W is a very simple criterion and benchmark: selects the managers with the
strongest performance over the last 12 weeks
Results of 12W are generally weak [exception 2004] and therefore no longer
considered
style
focus
Alpha & HiVol
aggressive
idiosyncrasy
Information Ratio, t(alpha) conservative idiosyncrasy & stability
Share Ratio
conservative
stability
12W
aggressive
past performance
C:\Users\Claus\TradeCap AG\Models\Empirical\Kohonen\Overview\Overview Versions iv.xlsx
CTA Selection Models (2)
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Annual Returns [5 managers each]:
Som?
Yes
Yes
No
Criterion Sharpe Ratio
Alpha
t(alpha)
avg. return
9.4%
7.0%
8.3%
vol
12.2%
10.1%
8.7%
SR
0.77
0.69
0.96
Model
V32 A idx.L.SR V31 A idx.L.alpha V180 A idx.L.alpha_BM
31/12/2004
-0.4%
-3.3%
2.8%
31/12/2005
35.9%
14.9%
21.8%
31/12/2006
12.3%
6.7%
12.6%
31/12/2007
16.1%
26.4%
9.3%
31/12/2008
7.0%
31.7%
13.8%
31/12/2009
14.8%
-16.0%
12.7%
31/12/2010
13.1%
13.0%
4.1%
31/12/2011
7.9%
4.8%
3.0%
31/12/2012
-19.1%
-2.2%
-1.1%
31/12/2013
6.3%
-6.2%
4.1%
C:\Claus Huber\TradeCap\Overview\Overview Versions iv.xlsx
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CTA Selection Models (3)
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Building a CTA Portfolio:
Steps of the Process
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b)
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Our selection criteria are:
Sharpe Ratio
Alpha
Information Ratio
Return of the last 12 weeks
• The steps of our selection process are:
1) Identify drawdown similarities of managers: run SOM
2) Candidates for inclusion in portfolio need to load on different units of the SOM
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This ensures diversity in terms of drawdown behaviour
3) Calculate selection criteria, e.g., Sharpe Ratio, alpha, t(alpha) etc.
4) Select managers based on those criteria
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The number of maximum exits is determined based on the number of portfolio constituents
Example: 5 managers in the portfolio: 2 exits / entries per month
5) Repeat steps 1 – 4 on a monthly basis
4) Do SOM Add Value for CTA
Selection?
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Generally, results improve when SOM are applied
This holds true for different numbers of
managers, # exits, selection criteria, etc.
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Example: Selection criterion = Sharpe Ratio
Performance not better every year, but over 2 to
3 years, SOM help to avoid larger losses, e.g.,
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2008 [+7.0% vs. -10.7%]
2009 [+14.8 vs. -3.8%]
2013 [+6.3% vs. -20.9%]
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There are always phases when SOM also worsen
performance, e.g.,
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2012 [-19.1% vs. -8.5%]
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For equity lines, see next slide
All returns are excess returns over 1M-T-Bills, net
of all fees
C:\Users\Claus\TradeCap AG\Models\Empirical\Kohonen\V32\Output Perf, V32 A, levered.xlsx
vol
avg. ret. P.a.
SR
31/12/2004
31/12/2005
31/12/2006
31/12/2007
31/12/2008
31/12/2009
31/12/2010
31/12/2011
31/12/2012
31/12/2013
SOM
no SOM
12.2%
9.6%
9.4%
-3.2%
0.77
-0.34
idx.L.SR idx.L.SR_BM
-0.4%
12.8%
35.9%
10.5%
12.3%
2.1%
16.1%
8.2%
7.0%
-10.7%
14.8%
-3.8%
13.1%
-13.1%
7.9%
-8.8%
-19.1%
-8.5%
6.3%
-20.9%
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Do SOM Add Value for CTA
Selection?
5) Results: Gross vs. Net
Returns (1)
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All returns are net of all fees [management & performance fees]: 1% mgt fee, 1% FoF
fee, 0.8% DB fee, 19% performance fee [leverage = 1]
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For leverage = L, all fees are
levered linearly as well
5 managers, selected by
Sharpe Ratio, SOM
Table shows the returns of the
same model:
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Net: net of all fees
Gross: gross excess returns before
all fees
Differences between net and
gross up to 30% p.a.!!!
The smallest difference is still
10% p.a.
Average leverage 3.1
Equity lines see next slide
C:\Users\Claus\TradeCap AG\Models\Empirical\Kohonen\V32\Output
Perf, V32 A, levered.xlsx
net
gross
Diff net-gross
SOM
SOM
vol
12.2%
13.9%
n/a
avg. ret. p.a. 9.4%
27.3%
-17.9%
SR
0.77
1.96
n/a
idx.L.SR idx.L.gross.SR
2004
-0.4%
12.3%
-12.7%
2005
35.9%
66.0%
-30.0%
2006
12.3%
26.9%
-14.6%
2007
16.1%
35.5%
-19.4%
2008
7.0%
23.8%
-16.8%
2009
14.8%
34.5%
-19.7%
2010
13.1%
37.1%
-24.0%
2011
7.9%
21.1%
-13.2%
2012
-19.1%
-9.0%
-10.1%
2013
6.3%
24.8%
-18.4%
Results: Gross vs. Net
Returns (2)
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6) Portfolio Construction:
Results (1)
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Model 1 varies its aggressiveness
and the number of managers
depending on the market
environment
Model 2 is conservative
comprising only 5 managers
Both are comparable with
regards to their Sharpe Ratios
Both deliver decent returns also
in times of a negatively
performing CTA universe [20092013]
Equity lines see next slide
All returns are excess returns
over 1M-T-Bills, net of all fees
# managers
vol
avg. ret.
SR
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Model 1
5-15
10.4%
9.5%
0.92
-3.5%
25.9%
14.4%
20.9%
18.1%
5.1%
9.3%
5.5%
-5.5%
4.6%
Model 2
5
8.7%
8.3%
0.96
2.8%
21.8%
12.6%
9.3%
13.8%
12.7%
4.1%
3.0%
-1.1%
4.1% 14
Portfolio Construction:
Results (2)
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7) Summary
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We have analysed the dbSelect platform extensively
SOM can help to identify similar drawdown behaviours of managers and make
portfolios more robust
Fee load is significant and can severely reduce returns
One possible way to address fee load could be to prefer high-volatility managers:
they provide “cheap” leverage
Different selection criteria perform differently in varying market environments
For example, high volatility managers to be preferred when the whole CTA
universe performs well
In times of a badly performing universe:
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selection criteria that emphasise idiosyncrasy [e.g., alpha, information ratio] produce the more
stable portfolios
It pays off to reduce the number of managers and become more concentrated
Utilising smart portfolio construction techniques, CTAs can deliver positive returns
even in times of a negatively performing CTA universe
Similarity analysis is not restricted to CTAs:
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Can also be applied to, for example, equity or hedge fund portfolios
Or to identify similarities in a loan portfolio
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Contact
Rodex Risk Advisers LLC
Dr. Claus Huber, CEFA, CFA, FRM
Office:
Breitenstrasse 15
CH-8852 Altendorf SZ
Switzerland
Tel.:
+41 43 539 76 22
+41 76 238 00 79
Email:
[email protected]
(Office)
(Mobile)
8) References
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Eling, M., Schuhmacher, F. (2007): Does the Choice of Performance Measure
Influence the Evaluation of Hedge Funds? Journal of Banking & Finance, vol. 31,
no. 9, 2632-2647.
Fung, W., Hsieh, D. (1997): Empirical Characteristics of Dynamic Trading Strategies:
The Case of Hedge Funds, Review of Financial Studies, vol. 10, 275-302.
Fung, W., Hsieh, D. (2001): The Risk in Hedge Fund Strategies: Theory and Evidence
from Trend Followers, Review of Financial Studies, vol. 14, 313-341.
Koh, F., Lee, D., Phoon, K. (2002): An Evaluation of Hedge Funds: Risk, Return and
Pitfalls, in: Singapore Economic Review, vol. 47, no. 1, 153- 171.
Nguyen-Thi-Thanh, H. (2007): On the Consistency of Performance Measures for
Hedge Funds, Working Paper.
Pätäri, E., Tolvanen, J. (2009): Chasing performance persistence of hedge funds –
Comparative analysis of evaluation techniques, in: Journal of Derivatives & Hedge
Funds, Vol. 15, 223–240.
Shadwick, W., Keating, C. (2002): A Universal Performance Measure, Journal of
Performance Measurement, in: Journal of Performance Measurement, vol. 6, no.
3, 59–84.
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