Achieving absolute return targets in multi-asset funds is
very different and, in my opinion, much more difficult to achieve than
relative return targets and requires a completely different approach.
Any honest fund manager will be the first to confess that they do not have a
crystal ball but still somehow has to produce consistent positive results
through a combination of assets where the individual return expectations are
This is an additional requirement for absolute return
mandates, as out-performance (or at very least matching) of benchmarks
within asset classes remains as critical as in relative benchmark
portfolios. The fact is that any specific return above the risk free rate
cannot be guaranteed for any specific time period, and that is assuming the
concept of risk free rate even exists. That is where risk management becomes
vital, as it provides the ability to achieve targets with relative certainty
and confidence over rolling periods by not only targeting specific risk
levels of risk as opposed to return, but doing so in the most optimal way,
thus mitigating the volatility and producing a smoothed return of the
optimal level of risk through diversification.
The process of optimisation that is used by such fund houses as MitonOptimal
is based on Markowitz portfolio theory and requires three inputs: a measure
of return, a measure of risk and a measure of correlation. They use
volatility as measured by standard deviation of returns as a measure for
risk, and variances and co-variances between all pairs of asset classes for
correlations, making the approach a form of Mean Variance Optimisation.
Even though the theory behind this is well known and could (and quite
frankly should) be used and even replicated by everyone, MitonOptimal does
employ a few proprietary alterations to the traditional model, which tailor
make it to fit their own needs and beliefs. This includes exponential time
weighting of risk and correlation measures to adapt to changing market
conditions, as well as not purely using historical returns given their
belief that asset class returns are cyclical, among other things.
All adjustments are based on rigorous testing and re-testing in order to
make it as robust and yet sophisticated as possible to give sensible results
in all possible market conditions. It is also essential for any model to be
adaptive to a change in the tides, and steer portfolios in the right
direction even if there is an unexpected game-changing event, as that is
exactly when investors find the benefit of protection through
diversification most valuable.
The optimisation process is thus aimed at producing a target level for risk
that corresponds to the targeted return at the point where it has the
highest Sharpe ratio, given the model inputs. This would be the “neutral”
portfolio which sets the guidelines for a long term strategic investment
plan to achieve the absolute return target.
Optimising for a static portfolio at this point is not good enough though,
the Strategic plan needs to accommodate for Tactical deviations as markets
move through short/medium term cycles creating trading opportunities.
Multiple combinations of asset classes are tested and optimised and (given
the parameters) together form an efficient frontier of all possible optima;
risk adjusted return levels (highest return at any given risk, or lowest
risk at any given return). Calculating an upper and lower target on the
efficient frontier given minimum return targets and effective risk
allocation thus produces a minimum and maximum variance portfolio.
By this point the fund manager should have minimum and maximum allowances by
asset class as a result of all the different permutations tested, as well as
a neutral for each asset class as per the neutral portfolio, dictating some
If the minimum variance portfolio is viewed as the 0/10 portfolio, the
neutral portfolio is 5/10 and the maximum variance portfolio 10/10, any
given combination of assets can be risk rated on a scale of 0-10. The figure
5 thus represents the Strategic plan, and the average risk rating over long
periods of time should essentially be close to that. But tactical moves
closer to 0 (in ‘risk off’ periods) or 10 (in ‘risk on’ periods) offer major
return enhancing opportunities based on shorter term market conditions and
pricing anomalies moving asset classes away from their perceived intrinsic
values. Return attribution, in decreasing order of importance, is driven by:
1. The Strategic plan
2. Tactical moves up and down the risk spectrum (0-10)
3. Tactically over and under-weighting specific asset classes
4. Tactically over and under-weighting sectors within an asset class
5. The manager/security selection
The optimisation process by no means replaces a fund manager; it is purely
an additional tool for understanding, controlling and measuring risk as well
as providing guidance and controls towards confidently achieving absolute
An added benefit of the process is that it is possible to
attribute returns for any given period to any of the five points mentioned
above. This gives the fund managers the tools to assess where there is
added/detracted value and improve on necessary areas whilst leveraging off
skills where they are adding value.
Thus this is an ongoing process which never stops and is
constantly evolving and is why Absolute Returns Funds should be part of your
portfolio - absolutely!
The above data and
research was compiled from sources believed to be reliable.
However, neither MBMG International Ltd nor its officers can
accept any liability for any errors or omissions in the above
article nor bear any responsibility for any losses achieved as a
result of any actions taken or not taken as a consequence of
reading the above article. For more information please contact
Graham Macdonald on [email protected]