Morningstar Magazine - October/November 2016 - 9
stage. The investor constructs portfolios designed
to achieve her objectives subject to her beliefs
about the future and what this means for
securities. It's these portfolios that ultimately
determine investor outcomes.
In a nutshell, investors study, forecast, and act.
How can we help them do better at one of these
three activities?
What We're Doing
If we want to use data to help investors achieve
better outcomes, then we need to identify
projects that support one of these three stages.
Indeed, the quantitative research group is
in various stages of research and development
on projects in all three of these areas.
Our team launched our first data product in 2012
called the Quantitative Equity Ratings. Morningstar
has a large team of equity analysts located
around the world who cover 1,500 stocks with
deep, fundamental analysis. However, due
to the sheer rigor of the analysis, this methodology
cannot be applied to each of the more than
50,000 stocks based in 86 countries that
are trading on 64 exchanges. Yet, many advisors,
investors, and asset managers desire this
type of coverage. To solve this problem, we created
the Morningstar Quantitative Equity Ratings.
These ratings are designed to algorithmically
replicate the decision-making process that our
analysts undergo when valuing a company.
More specifically, we train a machine-learning
algorithm on the valuation decisions our
analysts make using the fundamental and market
data available in our databases. Over time,
the model learns and improves as it gets more
examples of how analysts value companies.
Today, we produce close to 50,000 Quantitative
Equity Ratings on companies globally. These
ratings reflect our best guess at what a
Morningstar analyst would think about that
company based on its data and our analysts'
revealed preferences for stocks with similar
characteristics. In short, it helps investors with the
first stage: to study and evaluate securities
better. (And so far, our research has shown that
the Quantitative Equity Ratings have worked.
See "Quantitative Equity Ratings Prove Effective"
in the August/September issue.)
More recently, and as you'll read in this issue
("A New Way to Interpret Equity Risk, Page 37),
the quantitative research team invested
a significant amount of time and energy into
creating the Global Risk Model. In June,
we launched the first iteration of this model, which
covers global equities and equity funds. The
Global Risk Model is designed to formalize the
second stage: to form solid, up-to-date expectations for the future risk and return profile
of various investments. At its core, the Global
Risk Model is a forecasting engine. It helps
investors identify and assess the amount of risk
in their portfolio. By tracking every stock's
underlying economic exposure to 36 factors,
including six unique to Morningstar, our
model lets investors quickly understand how their
portfolio could behave in a variety of market
conditions. The factor exposures in our model root
out underlying revenue drivers to find a stock's
true sources of returns. Furthermore, our
model uses the exposures to go beyond standard
models to project a stock or stock portfolio's
vulnerability to extreme market events.
The Global Risk Model provides the foundation
on which many other processes may be
built to address the final stage in the investment
process: portfolio construction. This is an area
where the quantitative research team is
actively working. Armed with expectations for how
each stock and fund should behave allows
us to construct more targeted, specific portfolios
to meet investor objectives that take into account
today's market environment. Many portfolio
optimization services that exist today assume that
funds from certain asset classes (such as
large blend) will all behave in exactly the same
manner. Every large-blend fund is assumed
to relate to every intermediate fixed-income fund
in the same way. But we know that funds in the
same category can behave quite differently.
Some tilt toward the energy sector, others toward
healthcare. Some have momentum biases; others
are low volatility.
may consider: How can I construct a meanvariance optimal portfolio with minimal exposure
to momentum? Can I target liquidity factor
exposure? What portfolio minimizes tracking error?
What portfolio will result in lowest fat tail
risk? How can I build a mean/CVAR optimal
portfolio? All these and more are made possible
when a risk model is in place.
Art and Science
These projects are just three examples of how
the quantitative research team takes data
and applies it real-world problems that investors
face. But there are many other project examples,
including ones that rely on text analytics,
sentiment mining, and machine learning. Each
of them is designed to assist investors in one
of the three stages of the investing process. Our
hope is that by improving the tools that our clients
have available they will be better equipped
to make better investment choices and achieve
better outcomes. Using data to tackle these
problems is not easy, and it's not a clear-cut
science-despite the fact that people often refer
to folks who do this work as data scientists.
But in my experience, our work is as much art
as it is science. Upon being presented with
a problem and a block of data, we must be able to
see the potential in the data block. We have
to imagine the shape it may take. And only when
we have that vision in mind can we apply the
science to transform what is raw and unclean into
something that is useful, interpretable, and
predictive. It is only through a careful application
of this art and science can we ensure that
the gold we mine from data is the one of kings and
queens and not the gold of fools. K
Lee Davidson, CFA, is head of quantitative research
with Morningstar. He is a member of the editorial board
of Morningstar magazine.
Modern portfolio optimization services built
on the foundation of a risk model allow
for this nuance to be incorporated and these
strategy tilts to be recognized. Moreover,
it expands on the set of objectives that investors
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9
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Table of Contents for the Digital Edition of Morningstar Magazine - October/November 2016
Contents
Morningstar Magazine - October/November 2016 - Cover1
Morningstar Magazine - October/November 2016 - Cover2
Morningstar Magazine - October/November 2016 - 1
Morningstar Magazine - October/November 2016 - 2
Morningstar Magazine - October/November 2016 - Contents
Morningstar Magazine - October/November 2016 - 4
Morningstar Magazine - October/November 2016 - 5
Morningstar Magazine - October/November 2016 - 6
Morningstar Magazine - October/November 2016 - 7
Morningstar Magazine - October/November 2016 - 8
Morningstar Magazine - October/November 2016 - 9
Morningstar Magazine - October/November 2016 - 10
Morningstar Magazine - October/November 2016 - 11
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Morningstar Magazine - October/November 2016 - 13
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Morningstar Magazine - October/November 2016 - 18
Morningstar Magazine - October/November 2016 - 19
Morningstar Magazine - October/November 2016 - 20
Morningstar Magazine - October/November 2016 - 21
Morningstar Magazine - October/November 2016 - 22
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Morningstar Magazine - October/November 2016 - 26
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