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Summary CAIA Level 2 Topic 8 notes
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Topic 8: Due diligence and selecting managers8.1 Active management & new investments
8.1.1 Tactical asset allocation
One thing that all uses of TAA seem to have in common is that it represents a form of active
management of a portfolio. TAA will add value if it can systematically take advantage of tem-
porary market inefficiencies and departures of asset prices from their fundamental values.
Over time, strategic asset allocation is the most important driver of a portfolio’s risk-return
characteristics. TAA can add value if: (1) there are short- to medium-term inefficiencies in
some markets; and (2) a systematic approach can be designed to exploit these inefficiencies
while overcoming the risks and costs that are associated with active portfolio management.
TAA is driven by the balance between its potential benefits and costs. The potential benefit
of TAA is to reach a more preferred allocation (in terms of risk and expected net return) over
a short to medium time horizon. The costs of TAA include transactions costs (commissions
and bid-asked spreads), managerial costs, and, in some cases, potential adverse taxation con-
sequences. The Fundamental Law of Active Management, discussed in the next section, mod-
els the tradeoff between risk and return.
8.1.2 The fundamental law of active management
The Fundamental Law of Active Management (FLOAM), explained by Grinold (1989), ex-
presses the risk-adjusted value added by an active portfolio manager as a function of the
manager’s skill to forecast asset returns and the number of markets to which the manager’s
skill can be applied (breadth).
IR=IC×√BR
The information ratio (IR) is equal to the ratio of the manager’s estimated alpha (i.e., risk-
adjusted expected outperformance) divided by the estimated volatility of that alpha. The in-
formation coefficient (IC) is a measure of the manager’s skill, and represents the correlation
between the manager’s forecast of asset returns and the actual returns to those assets.
The breadth (BR) is the number of independent forecasts (or “bets”) that the manager can
skillfully make during a given period of time (e.g., 1 year).
FLOAM indicates that the value added by active management increases with the ability of the
manager to forecast returns and the number of independent assets, sectors, or markets to
which the forecasting skill can be applied. The amount of value that can be added through
,active asset allocation by a portfolio manager needs to be much higher if that skill is to be
applied to only a handful of independent asset classes (i.e., if BR is small).
The FLOAM, as expressed in Equation 1, assumes that the manager faces no constraints in the
asset allocation decision with regard to an ability to invest in all assets that appear to offer an
attractive alpha. In practice, portfolio managers face a number of constraints, both internal
and external. For instance, the manager is constrained by the limits imposed by strategic asset
allocation. In addition, there could be regulatory constraints on allocations to some asset clas-
ses. Finally, there are implementation costs associated with active management. A modified
version of the FLOAM takes into account that some allocations may have to be substantially
different from the ideal allocation recommended by the manager’s forecasting skill.
IR=IC×√BR×TC
The transfer coefficient (TC): (1) measures the ability of the manager to implement her rec-
ommendations; (2) has an upper limit of one and a lower limit of zero; and (3) indicates the
correlation between the forecasted active returns and the active weights. When TC is equal
to one, it means the manager is able to implement all her recommendations—meaning that
she is able to assign high weights to those assets appearing to offer high alpha. A TC of zero
indicates no correlation between the portfolio weights and the perceived alpha associated
with each weight.
In practice there is a tradeoff between IC and BR. The key driver of the tradeoff is that the
more markets to which the manager tries to apply her skills, the less accurate the forecasts
are likely to become (and the lower the IC). Note that the information coefficient tends to be
much higher when applied to asset classes than when applied to individual securities. The
random returns on individual security prices contain a significant amount of noise, which
makes forecasting models less accurate.
8.1.3 Costs of actively reallocating across alternative investments
The FLOAM model seeks the benefits of alpha through active management, but the model
does not address the potential costs of moving between assets that occurs with active man-
agement.
A common reason to wish to exit a fund is that it has performed poorly, potentially having
generated losses in recent years. The foregone loss carryforward of a fund with incentive fees
is an opportunity cost potentially borne by every investor in a fund with an asymmetric incen-
tive fee structure that arises from the inability to recapture incentive fees.
Foregone loss carryforward arises when an existing investor loses the fee benefits of owning
a fund below its high-water mark. The cost to the investor results from a managerial decision
,to liquidate a fund with a net asset value (NAV) below its high-water mark. Because a manager
collects performance fees only when NAV is above the most recent high-water mark at the
end of the relevant accounting period, a manager who is underwater
The cost of loss carryforward should be taken into account when the decision is being made
to replace a poorly performing manager with another manager. Going forward with the ex-
isting manager means that any positive returns realized from the poorly performing manager
will be gross of performance fees until the fund returns to its high-water mark. While any
return earned on an investment with a new manager will be subject to (i.e., net of) perfor-
mance fees.
After fee Return on Old Fund: r = 33.33%
After fee Return on New Fund: (33.33% + α)(1 − 20%)
33.33%=(33.33%+α)(1−20%)α=(.3333/.8)−.3333=41.67%−33.33%=8.34%
So the new fund’s total return would have to be 41.67%, an alpha of 8.34% relative to the old
fund, to break even with the return on the old fund after fees.
While the loss carryforward represents a potential cost for replacing a manager that has re-
cently experienced some losses, there are three primary reasons that an investor may still
wish to replace a manager with a carryforward loss. First, the investor may be concerned that
the old manager does not have an adequate incentive to generate performance until the high-
water mark is reached. The recent poor performance may continue for some time while the
manager puts greater effort into other professional opportunities or clients, or does not offer
sufficient compensation to retain and attract quality traders and other employees.
Second, other investors may withdraw their funds, making the investor’s relative position in
the fund too large or jeopardizing the viability of the fund.
There are two primary types of other costs associated with replacing managers other than
forgone loss carryforwards or concerns with the management of funds that are in decline.
These include: forgone earnings on dormant cash, and administrative costs of closing out one
position and opening another.
The other type of cost is related to transaction and administrative fees. Closing out old posi-
tions and opening new positions entails administrative fees and due diligence costs.
8.1.4 Keys to a successful tactical asset allocation process
, A key component of a successful TAA process is the development of sound models that can
consistently forecast returns across asset classes.
The effectiveness of a TAA strategy is largely dependent on constructing a good model of
return prediction. Absent an ability to forecast expected returns and/or risks, there would
seem to be few reasons to embark on TAA given the costs discussed in previous sections.
The first step in developing a TAA strategy is to forecast excess returns by constructing fun-
damental and technical models that can predict asset class returns, using a set of explanatory
variables for fundamental models and signals for technical models.
Evidence indicates that return models may have varying predictive strengths during different
economic regimes or points in economic cycles. Note from the FLOAM that the potential value
added through a TAA process is higher when the models’ errors are not correlated with each
other (so that the aggregated risk from errors is reduced). Therefore, multiple forecasting
models should be used to maximize the value added by the TAA strategy.
A good forecasting model must include economically meaningful signals and have a research
process that correctly identifies those signals. In addition, the model must have performed
well in the past using out-of-sample data.
3 important characteristics of sound model development:
- Use of economically meaningful signals. Economically meaningful signals are those
signals with rational, intuitive explanations for their expected predictive power.
- Absence of data mining. The manager should be able to confirm that the predictive
results are not due to data mining. Data mining in this context occurs if an analyst or
investment manager tries a large variety of models, explanatory variables, and return
models to see which models and variables best explain historic data.
- Avoidance of overfitting. Models that have a large number of explanatory variables
can produce impressive explanatory power (e.g., R-squareds), especially when there
is limited data.
An unconditional empirical analysis approach to asset allocation uses the historic means and
volatilities (and correlations) within an SAA approach to form asset weights without regard to
the current condition of the economy and markets. Note that the results of long-term histor-
ical analyses change slowly, so SAA weights also tend to change very slowly through time.
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