Morningstar Magazine - October/November 2016 - 8

Dispatches Extracting Value From Data It's a resource we use to tackle investors' toughest problems. Phillips Curve 10 Civil Wars 12 Where Does Active Management Fare Best? L E T T E R F R O M T H E B OA R D Lee Davidson Ivory Towers The Old Squirrel 17 Trump and Clinton 20 Retirees Need Help Converting Savings into Income Policy HelloWallet 22 Saving and Student Debt Don't Mix 24 Save the Shop Talk 27 Americas Greater Europe Australia & New Zealand Asia Behavior Gap I view data as an abundant natural resource. It occurs naturally, it can be difficult to move, it requires nurturing, and it must be coerced out of its natural habitat and transformed to be of much use. Unlike most resources, however, data grows exponentially-we are not going to run out of it anytime soon. If I know one thing for certain, it is that next year the world will have more data than it does today. And in 10 years, the world will have 1,000 times more data than it does today. Every time a website is visited, a product is purchased, or a stock is sold, data is generated and usually stored. The generation and storage of data is a natural outgrowth of the software and social systems within which we live. Global Briefs 33 35 35 Like most natural resources, data can be used for many different purposes. Of course, we could ignore it and use it for nothing. But data also can be extracted from its ecosystem and put to some other productive use. We can use data to form an investment thesis or to construct better portfolios. Data can also fuel smarter, more agile software development. Data can be used to identify better management practices or help to define corporate strategy. Data sets can be molded, folded, and alloyed to other datasets to create stronger, more reliable insights. It can be harnessed to predict the future. The point of all these efforts is simple: People want to use data to make better decisions in order to achieve better results. This is what we do a lot at Morningstar. The quantitative research team, which I lead, was assembled three and a half years ago for 8 Morningstar October/November 2016 exactly this purpose. Our mission is to use data to help investors make better choices so that they can meet their investment goals and achieve better outcomes, whether it's saving enough money for retirement or funding a college education. At Morningstar, we are awash with data. And we know investors face tremendously complex and challenging problems. What is going to happen to my portfolio next year? How much risk do I face? How similar is this company to another one? What does a Morningstar analyst think about this stock? How do Morningstar's ratings perform? Why do some funds succeed and others fail? Our job is to use what data we have to answer these types of questions and make investors' decisions less daunting and difficult. But where do we start? Three Stages of Investing When our team thinks about the investing process, we break it down into three stages. When we choose a project to work on, we want to make sure that it will address one of these stages. First, every investor usually begins by digesting raw information in an effort to study the markets. Maybe the investor conducts a discounted cash flow analysis of a particular stock. Maybe she compares the fees on two mutual funds in the same category. Maybe she studies the different macroeconomic drivers of China or the eurozone. After an investor has conducted her analysis, she implicitly or explicitly forms an opinion about what this means for the future-either the future risk or future return of the security or set of securities she is considering. This is the second stage. After digesting raw information, the investor then forms expectations about different risk or return expectations based on the information she consumed-whether that's fees, past returns, future cash flow projects, or econometric models. With her opinions formed, the investor then acts on this information. This is the third and final

Table of Contents for the Digital Edition of Morningstar Magazine - October/November 2016