Editor’s
note: The role of tactical asset allocation in managing portfolios can be a
complex business. Here, Alexander Melnikov, director of research at Performance
Analytics, based in Boston,
MA, runs through the issues that
wealth managers should be aware of.

Portfolio returns in excess
of an index can be achieved through active investment management in two ways:
security selection, and active (or tactical) asset allocation. Research shows
that about 90 per cent of risk and return for a typical balanced portfolio (60
per cent stocks, 40 per cent bonds) comes from policy asset allocation (source:
Brinson et.al. [1986, 1991]). Clearly, potential for adding value through
actively managing asset allocation is at least as great as from active security
selection. However, while active security selection is widely practiced,
tactical asset allocation has been largely overlooked or out of favor. Here, we
discuss some of the reasons for this, and describe the process that should be
followed in order to successfully perform TAA.

TAA is an investment strategy
that centers on altering investment proportions to take advantage of
differences in expected performance and risks of broad asset classes (such as
stocks and bonds) or sub-classes (such as US and global equities). Several
requirements to the investment process stem from this definition. First, the
responsibility for TAA must be placed with the person (or group) who’s
responsibilities span across asset classes, and who is authorized to change the
asset mix. Second, it has to be based on accurate, timely asset mix information
(actual and benchmark). Thirdly, the effect of these investment decisions has
to be measured as part of performance evaluation. Lastly, TAA decisions have to
be largely based on systematic quantitative results rather than on judgment.

Because the implications to
performance are so large, many family offices already attempt actively managing
their asset allocation, even if implicitly. If you are a family office or HNW
manager, you may hear this in your investment strategy discussions: “We want to
be positioned defensively due to anemic economic recovery in the US” (or due to “debt crisis in Europe”
or whatever the current concern may be), or “We would like to take advantage of
the rally in equities.” However, doing this implicitly, without the proper
process and structure, is dangerously likely to result in underperformance.

Clearly, successful TAA
requires timely, accurate calls on expected asset class performance. “Active management
is forecasting,” say Richard Grinold and Ronald Kahn in their well-known
book Active Portfolio Management. The authors establish the following
relationship between active return (alpha) and forecasting skill, or
information coefficient (IC): α = σ × IC × Score

Forecasting
skill

The key to achieving good
performance from TAA, therefore, is the skill (IC) of forecasting asset class
returns.

This, of course, is not
easy. Qualitative judgment is likely to be affected by the prevailing sentiment
in the market which will be exactly wrong at market turning points. Many
managers use a set of indicators to help determine future market direction.
This is a step in the right direction, but at any point in time, there usually
is about the same number of indicators that give a positive signal as negative.
How do we know which indicators are currently relevant, and what the proper
weights are to each? In addition, a set of disjointed indicators cannot produce
a history of return forecasts, which is required in order to determine if the
method has any skill. One needs to combine predictor factors into a consistent
statistical model that produces return forecast series, correlating which to
actual returns gives IC.

Numerous published studies
have tested predictability of the stock market via factor models. Generally,
they find little evidence of out-of-sample forecasting ability; small excess
returns that are achieved by some models often don’t justify the costs. It is
common to interpret these results as not supporting the idea of actively
managing asset allocation at all. But there is light at the end of the tunnel!

We attribute the lack of
success of the forecasting models commonly described in academic literature to
two reasons. First, while some of them are quite sophisticated from the
statistical standpoint, they tend to miss important aspects of what works in
investment practice. Second, researchers often limit the set of factors to only
a few variables that are commonly described in macroeconomic literature as
drivers of business cycles.

We found that using much
broader set of variables selected empirically, rather than fitting a
pre-defined economic theory, is necessary to build a model with good
forecasting ability. These variables should include economic, valuation and
market factors employed by investment managers as predictive indicators. An
example of a factor that is not common is the CBOE implied volatility index
(VIX), which is known by practitioners to be inversely related to market
returns.

Thus, we recommend that
investment organizations develop return forecasting models that address these
shortfalls, provided that the organization can devote proper resources to it.
The focus should be on equities as the main source of return variability in a
balanced portfolio.

Alternatively, an investment
manager may wish to partner with a research firm that provides return
forecasting. This solution has clear advantages for sophisticated smaller
managers such as family offices. First, it is cost-effective - creating an
internal research team to spend considerable time (likely years) developing
models would be expensive, and success would not be guaranteed. Secondly,
immediate access to vendor return forecasts can potentially help improve client
portfolio performance much sooner than an internal solution.