Inside the investing kitchen, part 1

Inside the investing kitchen, part 1 Jun 12, 2025 4:50:04 PM The recipe for a better portfolio, and the science behind a safer nest egg. Jamie Lee isn’t a Top Chef, but he knows his way around the kitchen. He dabbles in sous vide with the help of a sous chef (his 6-year-old daughter). He loves smoking salmon low and slow on a pair of pellet grills. And in some ways, his day job on the Betterment Investing team resembles the culinary world as well. He and his teammates work in a test kitchen of sorts, defining and refining the recipes for our low-cost, high-performing, and globally-diversified portfolios. They size up ingredients, pair flavors, and thoughtfully assemble the courses of each “meal.” All in service of customers with varying appetites for risk. It's highly-technical work, but we wouldn't be Betterment if we didn't make our methodologies as accessible as possible. So whether you're kicking the tires on our services, or you're already a customer and simply curious about the mechanics of your money machine, come along for a three-part, behind-the-scenes look at how we cook up a better portfolio. Here in part 1, we'll explore how we allocate your investing at a high level. In part 2 (coming soon), we'll zoom in to our process for selecting specific funds. And in part 3 (also coming soon), we'll show you how we handle thousands of trades each day to keep our customers’ portfolios in tip-top shape. The science behind a safer nest egg Betterment customers rely on Jamie and team to do the heavy lifting of portfolio construction. They distill handfuls of asset classes, a hundred-plus risk levels, and thousands of funds into a simple yet eclectic menu of investment options. And underpinning much of this process is something called Modern Portfolio Theory, a framework developed by the late American economist Harry Markowitz. The theory revolutionized how investors think about risk, and led to Markowitz winning the Nobel Prize in 1990. Diversification lies at the heart of Modern Portfolio Theory. The more of it your investing has, the theory goes, the less risk you're exposed to. But that barely scratches the surface. One of the meatiest parts of building a portfolio (and by extension, diversifying your investing) is how much weight to give each asset class, also known as asset allocation. Broadly speaking, you have stocks and bonds. But you can slice up the pie in several other ways. There’s large cap companies or less established ones. Government debt or the corporate variety. And even more relevant as of late: American markets or international. Jamie came of age in South Korea during the late 90s. Back here in the States, the dot-com bubble was still years away from popping. But in South Korea and Asia more broadly, a financial crisis was well underway. And it changed the trajectory of Jamie’s career. His interest in and application of math shifted from computer science to the study of markets, and ultimately led to a PhD in statistics. For Jamie, the interplay of markets at a global level is fascinating. So it’s only fitting that when optimizing asset allocations for customers, Jamie and team start with the hypothetical "global market portfolio," an imaginary snapshot of all the investable assets in the world. The current value of U.S. stocks, for example, represents about two-thirds the value of all stocks, so it's weighted accordingly in the global market portfolio. These weights are the jumping off point for a key part of the portfolio construction process: projecting future returns. Reverse engineering expected returns “Past performance does not guarantee future results.” We include this type of language in all of our communications at Betterment, but for quantitative researchers, or “quants,” like Jamie, it’s more than a boilerplate. It’s why our forecasts for the expected returns of various asset classes largely aren't based on historical performance. They're forward-looking. "Past data is simply too unreliable," says Jamie. "Look at the biggest companies of the 90s; that list is completely different from today.” So to build our forecasts, commonly referred to in the investing world as Capital Market Assumptions, we pretend for a moment that the global market portfolio is the optimal one. Since we know roughly how each of those asset classes performs relative to one another, we can reverse engineer their expected returns. This robust math is represented by a deceivingly short equation—μ = λ Σ ωmarket—which you can read more about in our full portfolio construction methodology. From there, we simulate thousands of paths for the market, factoring in both our forecasts and those of large asset managers like BlackRock to find the optimal allocation for each path. Then we average those weights to land on a single recommendation. This “Monte Carlo" style of simulations is commonly used in environments filled with variables. Environments like, say, capital markets. The outputs are the asset allocation percentages (refreshed each year) that you see in the holdings portion of your portfolio details Hypothetical portfolio; for illustration only At this point in the journey, however, our Investing team's work is hardly finished. They still need to seek out some of the most cost-effective, and just plain effective, funds that give you the intended exposure to each relevant asset class. For this, we need to head out of the test kitchen and into the market. So don’t forget your tote bag.