Morningstar Magazine - October/November 2016 - 61
The Rise and Fall of New Funds
Investors routinely pour cash into recently
launched funds. Is that a mistake?
INVESTMENT RESEARCH: FUNDS
Lee Davidson, Madison Sargis, and Tim Strauts
The following article was part of a larger
research study on the subject. To get a copy
of the full white paper, email the authors
at lee.davidson@morningstar.com
It is easy to dismiss new funds as an immature,
unproven, and unimportant segment of the
asset-management industry. But the truth of the
matter is that the asset-management industry
is dominated by new funds. Globally, we find that
new funds account for the preponderance of
new asset flows. In 2015, global fund flows reached
approximately $516.4 billion across the three asset
classes in our study-equity, fixed income,
and allocation. Of the total, new funds with less
than 12 months of track record accounted for $379
billion, or 73% of all flows. Even in years when
the industry experiences net outflows, new funds
continue to garner assets. In 2014, new funds
grabbed $316 billion in net inflows compared
with negative $526 billion in net outflows for funds
with greater than 12 months of track record.
We hope, therefore, that it does not come as a
surprise why the patterns of flows into new
funds should be of interest to us as researchers.
It is simply too big and too important to ignore.
Furthermore, we are particularly concerned about
investor behavior when investing in these
unproven products. Do we find that investors
prefer what's good for them?
In this paper, we explore the relationship
between observed investor preferences for and
eventual investor outcomes in newly launched
funds. To conduct this exploration, we built
two models that unpack the historical correlations
between both forward risk-adjusted returns
and forward cumulative fund flows with observed
fund characteristics, economic regimes, and
category environs. In isolation, each model offers
us a multivariate view of the factors that
drive future flows and risk-adjusted returns for
newly launched funds. By comparing the outputs
from the two models, we can also identify
potential conflicts of interest for asset managers.
A variable that has tended to result in higher flows
but lower returns, for example, socially responsible investing, presents a difficult choice for asset
managers over whose interests to prioritize.
This tension between what investors have
preferred and the outcomes investors are seeking
also highlights poor choices on the part of the
investing populace. We find that investors
have been inconsistent with how they allocate
their assets among newly launched funds.
For example, we find that investors prefer cheaper
funds that, not surprisingly, tend to result in
better outcomes. But we also find that investors
prefer funds that invest in more-popular, familiar
stocks that have done well recently when
the opposite choice would have resulted in better
risk-adjusted returns.
Newly launched funds are of particular interest
to us as they often lack information that we
know investors use to make decisions. From prior
work, we have observed that investors exhibit
strong preferences for funds with strong
performance track records. While this is perhaps
not surprising, it does raise the question
of what information investors use to make an
investment decision when a performance track
record is unavailable. Despite the lack of
information on newly launched funds, we have
already presented evidence that new funds
can garner immense assets almost immediately.
Given the lack of relevant data early on in the
life cycle of a fund, variable selection and variable
definition were critical choices in our study.
Variables included in the model had to be both
widely available in our sample and hypothesized to
be relevant drivers. We included 23 variables
in the fixed-income and allocation models. In the
equity model, we were able to expand this number
to 39 because of the quality of Morningstar's
equity data and portfolio holdings database. Many
variables included in the model are to be
expected: fees, firm-level characteristics, index
fund, socially responsible, and portfolio disclosure.
However, some variables are less common.
Category-level data points such as market concentration and firm market share by category
were constructed specifically to examine the role
of category structure on newly launched fund
outcomes. Furthermore, we also expanded the use
of our ownership data by building data points
to examine whether a fund manager behaves in
a similar fashion to other types of managers.
An example of this is the Manager CFA Ownership
data point, which captures the extent to which
EXHIBIT 1
Eating Their Own Cooking Managers
of new funds that invested alongside
shareholders posted better returns-
and attracted more cash.
p Equity p Fixed Income p Allocation
%
10
8
6
4
2
Flows Model
Star Rating Model
global.morningstar.com/Morningstarmagazine
0
61
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