Morningstar - Q1 2020 - 12

Dispatches

EXHIBIT 1

EXHIBIT 2

Performance's Performance Past alpha
is a rough guide to future returns.

Ownership Lens Owns It Portfolios
are a better predictor of returns than
performance.

Alpha Quintile

Ownership Lens Quintile

Annualized Return (%)

Annualized Return (%)

Lowest

6.61

Lowest

5.64

Second Lowest

7.36

Second Lowest

7.21

Middle

7.74

Middle

7.53

Second Highest

7.89

Second Highest

7.67

Highest

7.37

Highest

8.94

Source: Morningstar. Returns of global equity funds from 2003-19.

Source: Morningstar. Returns of global equity funds from 2003-19.

Morningstar creates quintile buckets, rather than
deciles, so there isn't as much separation
from top to bottom. Nevertheless, its conclusion
is striking. The subsequent alphas for the
top-quintile funds (again, as calculated by the
Carhart model) beat those of the bottomquintile funds by an annualized 330 basis points,
a larger gap than is created by sorting funds into
expense quintiles. If there is a stronger
predictor for future equity-fund performance,
after adjusting for style effects, I have yet
to encounter it.

None of this is remarkable; you knew that
yesterday's performance is only a very
rough guide for tomorrow's. The Ownership Lens
results ( EXHIB IT 2 ), however, come as
a surprise.

First, let's see what happens when
one chooses funds by returns alone. As with
"Judging Fund Managers," Morningstar's paper
measured past performance by using
the Carhart four-factor model, which not only
risk-adjusts the numbers but also considers
three style effects (size, value/growth,
and momentum). The paper sorted funds
into quintiles based on those alphas,
then calculated the returns for each quintile
going forward.
The good news is that the funds with the
lowest past alphas posted the lowest
future returns and that the results steadily
improved over the next three alpha rungs
( EX H I B I T 1 ). The bad news is that the top alpha
group was merely middling, making the
return spread between the top and bottom
groups only 76 basis points. Most of that
disparity, one suspects, owes solely to differences
in expense ratios.

12

Morningstar Q1 2020

Once again, the funds with the lowest
scores appear at the chart's top and those with
the highest at the bottom. Only this time,
rather than coming from past performance, the
quintiles were based on portfolio analysis,
with the funds that shared holdings with the least
successful competitors placed in the lowest
quintile and those that shared holdings with the
most successful competitors landing in the
top quintile.
Look at that gap! A whopping 330 basis points
separate the future total returns between
the fund quintile with the highest Ownership Lens
score and that with the lowest. In addition,
the returns march in lock step; as each Ownership
Lens quintile increases, so do the subsequent
returns. Let's recall, these figures come
from a huge sample size, as each quintile currently
possesses 20,000 funds.
When faced with such overwhelming evidence, all
I can do is bow in admiration.
Practical Considerations

Two caveats must be offered. One is that,
with both papers, the methodology uses
information that would be unavailable to investors
were they to attempt to realize those results.

The Ownership Lens scores are assigned
on a fund's portfolio date, but those portfolios are
not publicly filed until after the fact. One
could not invest exactly as the papers suggest.
Acknowledging that issue, both publications
checked their findings by lagging the
portfolios, so that a real-life investor could fully
implement the methodology. The original
article tested a three-month lag, while
Morningstar's paper assessed three-, six-, and
12-month versions. No major effect. The
conclusions weakened as the lag time increased,
but even when using the 12-month period,
the pattern remained strong and inescapable.
The second caveat cannot be dismissed
quite so quickly. It is that those results are strictly
theoretical. Academics study factors, not
investment strategies. The portfolios used for their
research are both huge and frequently traded,
to most accurately capture a factor's effect.
While instructive, the academic approach is deeply
impractical. Nobody will buy equal stakes
in all equity funds that have high Ownership
Lens rankings, rebalance monthly, and
then reconstitute quarterly, when funds file their
latest portfolio positions.
It's great to have a mutual fund calculation that
appears to be powerfully predictive, but the
statistic must nevertheless be implemented. How
to accomplish that task?
Taking more practical approaches might reduce,
or even eliminate, the statistic's power. By
evaluating a group of funds, rather than a single
fund, the measure reduces statistical "noise."
Holding only a few funds that have high
Ownership Lens scores, rather than a large pool,
creates the very statistical "noise" that
the measure attempts to avoid. That is,
investors following the Ownership Lens strategy
might fail even if the measure generally
succeeds, should those buyers have purchased
duds. Professors who research entire
universes need not worry about such flukes, but
shareholders must.
What's more, what prospers for three months may
not do so for three years. The professors and
Morningstar - Q1 2020

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