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 global.morningstar.com/Morningstarmagazine 9 http://global.morningstar.com/Morningstarmagazine

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
Morningstar Magazine - October/November 2016 - 12
Morningstar Magazine - October/November 2016 - 13
Morningstar Magazine - October/November 2016 - 14
Morningstar Magazine - October/November 2016 - 15
Morningstar Magazine - October/November 2016 - 16
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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 - 25
Morningstar Magazine - October/November 2016 - 26
Morningstar Magazine - October/November 2016 - 27
Morningstar Magazine - October/November 2016 - 28
Morningstar Magazine - October/November 2016 - 29
Morningstar Magazine - October/November 2016 - 30
Morningstar Magazine - October/November 2016 - 31
Morningstar Magazine - October/November 2016 - 32
Morningstar Magazine - October/November 2016 - 33
Morningstar Magazine - October/November 2016 - 34
Morningstar Magazine - October/November 2016 - 35
Morningstar Magazine - October/November 2016 - 36
Morningstar Magazine - October/November 2016 - 37
Morningstar Magazine - October/November 2016 - 38
Morningstar Magazine - October/November 2016 - 39
Morningstar Magazine - October/November 2016 - 40
Morningstar Magazine - October/November 2016 - 41
Morningstar Magazine - October/November 2016 - 42
Morningstar Magazine - October/November 2016 - 43
Morningstar Magazine - October/November 2016 - 44
Morningstar Magazine - October/November 2016 - 45
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Morningstar Magazine - October/November 2016 - 63
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Morningstar Magazine - October/November 2016 - 65
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Morningstar Magazine - October/November 2016 - 89
Morningstar Magazine - October/November 2016 - 90
Morningstar Magazine - October/November 2016 - 91
Morningstar Magazine - October/November 2016 - 92
Morningstar Magazine - October/November 2016 - 93
Morningstar Magazine - October/November 2016 - 94
Morningstar Magazine - October/November 2016 - 95
Morningstar Magazine - October/November 2016 - 96
Morningstar Magazine - October/November 2016 - Cover3
Morningstar Magazine - October/November 2016 - Cover4
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