Morningstar Magazine - June/July 2017 - 71

EXHIBIT 1

Factor Loading Variables in the Morningstar Global Risk Model

Style
3 Economic Moat
3 Financial Health
3 Ownership Popularity
3 Ownership Risk
3 Valuation
3 Valuation Uncertainty
3 Liquidity
3 Momentum
3 Size
3 Value-Growth
3 Volatility

Sector
3 Basic Materials
3 Consumer Cyclical
3 Consumer Defensive
3 Energy
3 Financial Services
3 Healthcare
3 Industrials
3 Real Estate
3 Technology
3 Telecommunications
3 Utilities

Region
3 Developed Americas
3 Developed Asia Pacific
3 Developed Europe
3 Emerging Americas
3 Emerging Asia Pacific
3 Emerging Europe
3 Emerging Middle East

Currency
3 Australian Dollar
3 British Pound
3 Canadian Dollar
3 Euro
3 Japanese Yen
3 New Zealand Dollar
3 Swiss Franc

Italicized factors are Morningstar proprietary measures.

Source: Morningstar.

exposure of a company to the seven Morningstar
regions. For a given stock, the seven factor
loadings are seven region weights, each
between 0% and 100% and all summing to 100%.
Morningstar estimates these weights using
a technique that takes a 100% allocation
to the region of its country as a starting point
for a regression of the returns of the stock
on the returns on the Morningstar Global Equity
indexes for the seven regions.

Statistical Factor Models

In the time-series regression approach,
we observe the factor realizations and estimate
the factor loadings. In the cross-sectional
aggression approach, we construct the factor
loading from observable data and estimate
the factor realizations. In statistical factor models,
we estimate both the factor loadings and
factor realizations from the returns on the
securities being modeled. This is typically done
with one of two statistical procedures,
one called principal component analysis and
the other called factor analysis.

Currency Factors
The seven currency factors represent the
economic exposure of a company to seven major
currencies, excluding U.S. dollars. Morningstar
Elton and Gruber (1994) provide an excellent
estimates currency exposures by running
overview of the statistical factor. They explain the
regressions of the U.S. dollar returns on each
approach as follows:
stock on the percentage change in the exchange
rate of each currency against the U.S. dollar.
"An alternative to prespecifying indexes [i.e., the
0.5 coefficients
1.0
2.0 time-series
2.5
3.0
3.5
Although0.0the regression
are1.5
regression
approach]
is to4.0try to 4.5
Active
Risk
have the historical return series itself suggest
unbounded, they generally fall between negative
1 and positive 1.
what portfolios of securities would best serve as
indexes. [The above] [e]quation ... and its
For more details on how the Morningstar
explanation suggest the characteristics these
Global Risk Model is constructed and estimated,
indexes should possess. In particular, they should
see Morningstar (2016).
separate the common influences in returns from

the unique influences in returns. After we have
specified the indexes, the unique returns on
securities should be uncorrelated with each other.
In addition, the structure should be parsimonious;
that is, returns can be described in terms
of
Alpha a limited number of indexes. Finally, having the
indexes represent separate influences would
0.6
be desirable."
0.5
Elton and Gruber (1994) discuss four issues
with the statistical factor approach:
30.4
Choice of data
3 Number of factors
30.3
Nonuniqueness of factors
3 Computational problems
0.2
For me, however, the biggest disadvantage
0.1
of statistical factor models is that the statistical
factors may not easily map into economically
0.0
meaningful factors.
Despite these issues, many practitioners use
statistical factor models.
Returns-Based Style Analysis

So far in this series, I have been discussing factor

global.morningstar.com/Morningstarmagazine

71


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