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34
citywirEglobal.com
Selectors’ tool kit
It may look like alchemy but Quantis Investment
Management’s Q-Select system combines
subjective and objective facets of fund selection
to achieve a balanced result, says its creator
and the company’s CIO Attila Rébak
W
hen we launched Quantis Investment
Management, we realised we lacked the
economies of scale to practice stock picking on
a global scale. However, a structured fund selection process
can balance subjective and objective factors whether we
build a Chinese equity fund of funds or a Latin American
one. With this in mind we have set up our own process,
called Q-Select, which has similarities with other multi-
managers together with specialities and styles of its own.
The first filter
The first step in our process is to sift for potential funds
that fit into our fund of funds model. Filters used include
tracking error, fund size, consistency, turnover, manager
tenure and standard deviation. The aim is to reduce the
number of candidates to a manageable group of 20-40.
When we have the list of contenders we try to
understand the investment process and the proposition
of the given vehicle. Initially we go through the RFP stage
and roughly complete our scoring template; these points
can then be grouped under the following headings:
People, Process, Performance, Risk monitoring and
management.
This is a common step for many multi-manager
companies, but it is a very subjective one as well.
We are lucky at Quantis because we talk a common
language; there are only minor differences in scores
depending on who completes the scoring system.
We try to meet the decision makers to fully understand
their process, to know what to expect from the fund in
general and in different market situations in particular,
and to go through every ambiguous point with them.
After the meeting we often compare one another’s
scores for the same fund, to reduce the chance of
incorrect or subjective evaluation, and to have a
consensus on the fine-tuned scoring.
Crude alpha forecast
We realised that just a pure score is hardly enough to
structure our fund of funds and determine the weights
of the individual holdings. We turn to the Waring and
Ramkumar article1
to translate our scores into an
expected alpha, which is coupled with a forecasted
tracking-error and the correlation among the alpha of
the given funds, with which we can make a formal
optimisation for our fund of funds.
α = estimated equilibrium level of alpha of the given
manager;
IC = information coefficient of Quantis, which measures
the correlation between the predicted quality of the
managers and the actual quality of the manager;
σ = variability in the information coefficient of the
individual managers. As suggested by the original
paper we use 0.07 for all managers;
z = measures the quality of the manager. We translate
our qualitative score with the help of the standard
normal distribution;
Br = breadth of the individual managers, ie the
number of independent bets the managers take
each year;
ω = tracking error of the manager;
TC = transfer coefficient, how effectively could the
manager translate his view in practice – this very
much depends on the number of benchmark holdings
and the active risk of the manager.
We use the formula proposed by Grinold and Kahn for
long-only managers:
where γ=(53+N)^0.57 and N is the number of securities
in the benchmark portfolio.
The above mentioned translation helps us to establish
a few important links with which we agree:
Firstly, the skill alone (high score) is not enough to add
value; you should factor in risk (tracking error).
Secondly, we can judge whether the fee-structure is
attractive by taking into account the quality and the risk
of the fund. As we forecast before fee alpha with the
translation it is very easy to incorporate the fee structure
of the fund.
Finally, the link is not linear between higher tracking
error and higher (before-fee) alpha. Depending on the
depth of the universe and the turnover of the fund
manager, a lower transfer coefficient can easily eat away
the value added by a higher tracking error. This can be
a concern especially in emerging market countries and
regional funds, and less so for global developed market
or US funds.
As we want to use the tracking error forecast for the
alpha forecast and for risk management as well, we have
to balance the conflicting objectives of the two functions.
For alpha forecast we need a more stable tracking error
as we do not want to trade the underlying funds just
because the forecasted tracking error shrinks from one
A formula
for success
Mgr Mgr
1-γ(N)
Quantis=ICα ±σ z ωΒrIC TCMgr Mgr Mgr Mgr
TC=(—–)[—–———]ωMgr
1 (1+ )ωMgr –1
1– γ(Ν)
Mgr Mgr
1-γ(N)
Quantis=ICα ±σ z ωΒrIC TCMgr Mgr Mgr Mgr
TC=(—–)[—–———]ωMgr
1 (1+ )ωMgr –1
1– γ(Ν)

2. pLAYERS 35Issue 21: February 2012
Selectors’ tool kit
citywirEglobal.com
Got something to say on fund
selection? Call Jesús Segarra Sobral.
jsobral@citywire.co.uk +44 20 7840 2175
month to the next and thus the projected alpha is small
compared with other funds. But to be able to effectively
manage the risk of our fund, we need a tracking error
forecast which effectively takes into account every
piece of information and responds promptly to market
movements. After evaluating many potential candidates
we chose exponential smoothing from weekly returns.
Final alpha forecast
We understand our limitations as well; hence we use
other methods to generate the final alpha forecast for
the given funds. The aim with this method is to avoid
mistakes known in statistics as Type I and Type II errors.
Sometimes the manager cannot add value with a
well-thought-out investment process due to deficiencies
uncovered with our scoring system; or to the contrary
the manager can produce alpha because she/he employs
information efficiently in a framework of an average
investment process.
To achieve this goal (ie balance efficiently Type I and
Type II error) we combine a priori information (return
forecast based on Waring et al. article) with the posterior
information (the return achieved during the past 36
months) in a Bayesian fashion, that gives much more
weight to our qualitative assessment.
Portfolio construction
With the expected alpha and covariance matrix at hand
we can make a formal optimisation for our fund of funds.
There are several advantages to this method:
We can identify funds (portfolio composition) which
truly diversify our risk compared to the benchmark.
With the forecasted risk and expected alpha we can
identify the optimal number of funds in our portfolio.
Holding only a few funds represents a big risk for us,
while a larger number diversifies the expected alpha. A
surprisingly small number of funds (four to six) is enough
to achieve optimal diversification in case of single country
or regional funds. This is also true for global funds, where
the fund selector does not want to bet beyond the fund
selection (sector, country, region).
This quantitative method usually confirms the qualitative
assessment of the underlying funds. For example, our
optimisation for global developed market equity yielded
four funds as the ideal composition, where two of the
underlying managers are true stock pickers, one of them
tries to add value mostly with sector bets, while the last
one’s value-added comes mostly from regional calls.
We can use several well-defined constraints explicitly
during the optimisation process such as minimum weights
for individual holdings and tracking error range.
Making it work for you
As we do not make any asset allocation calls in our
strategy, we do not have to make any adjustment to
the aforementioned process, but for selectors who
want to control the asset allocation as well, our model
can be modified. Assuming that the correlation among
the alpha of different managers in different asset
classes (or regions, countries) is independent,
separating the asset allocation and fund selection
steps solves the problem.
We have been using Q-Select since the beginning
and have had positive feedback. Even though the
investment process is well structured I have to emphasise
the subjective factors, mainly during the selection and
evaluation phase. In the phase of fund selection, it is
essential to filter the right universe as the alpha forecast
depends on the tracking error, which makes sense only
when we compare the funds to the correct benchmark.
While in the evaluation phase we also need some
controlled subjectivity when we score the individual
funds and finalise our qualitative assessment. 
1
M. Barton Waring and Sunder R. Ramkumar: ‘Forecasting Fund
Manager Alphas: The Impossible Just Takes Longer’, Financial
Analysts Journal March/April 2008.
‘We understand our limitations as
well; hence we use other methods
to generate the final alpha forecast
for the given funds’
Attila Rébak
CIO Quantis Investment Management