Jamie Lee is not a top chef, but he knows the paths around the kitchen. He dabbles in Sue Video with the help of Soo Shef (his six-year-old daughter). He loves to smoke salmon low on a pellet grill.
And, in a way, his work with the Betterment Investing team is similar to the world of culinary. He and his teammates work in some kind of test kitchen, defining and improving recipes for our low-cost, high-performance, and globally expanded portfolio. They pair ingredients sizes, flavors and thinkfully assemble each “meal” course. All of our customers who have a diverse appetite for risk are served.
It’s a very technical task, but we won’t improve unless the methodology is as accessible as possible. So let’s take a behind-the-scenes look at the three-part behind the scenes on whether you’re kicking tires with our services, or just you’re already a customer and are interested in the mechanisms of your money machine, how we cook a better portfolio.
- Here, in Part 1, we explore how to allocate your investment at a high level.
- Part 2 (coming soon) zooms in to the process to select a specific fund.
- Part 3 (coming soon) shows you how to process thousands of transactions every day to keep your customer’s portfolio in tip top shape.
The science behind safer nest eggs
Betterment’s customers rely on Jamie and Team to do heavy portfolio structures. They distill a handful of asset classes, risk levels of over 100, and thousands of funds into a simple yet eclectic investment options menu.
And much of this process is what is called the Modern Portfolio Theory, a framework developed by late American economist Harry Marcowitz. This theory revolutionized the way investors think about risk, leading to Marcowitz, who won the Nobel Prize in 1990.
Diversification is at the heart of modern portfolio theory. The more investments you make and the more theory you go, the less risk you are exposed to.
However, it hardly damages the surface. One of the most physical parts of building a portfolio (and, in addition, diversifying your investments) is how much weight you give to each asset class, also known as asset allocation.
Broadly speaking, you have stocks and bonds. However, you can slice the pie in other ways. There are large cap companies or less established companies. Government debt or corporate diversity. And more relevant at recent times: the American market or international.
Jamie grew older in Korea in the late 90s. Back in the state, the bubbles at dotcom were still a few years away. However, the financial crisis was progressing more widely in South Korea and Asia. And that changed the trajectory of Jamie’s career. His interests and application in mathematics shifted from computer science to market research, eventually earning a PhD in statistics.
For Jamie, interaction with the market on a global level is fascinating. So when optimizing asset allocation to customers, Jamie and his team are good to start with a hypothetical “global market portfolio,” an imaginary snapshot of all investable assets in the world. For example, the current value of US stocks represents about two-thirds of all stocks, and is weighted accordingly in the global market portfolio.
These weights are the jump points for a critical part of the portfolio construction process. This predicts future returns.
Reverse engineering expected revenue
“Past performances do not guarantee future results.”
This type of language is included in all communications with Betterment, but for quantitative researchers like Jamie, or “Quent,” it’s more than a boilerplate. This is why predictions for expected returns for various asset classes are not based primarily on historical performance. They are positive.
“Historical data is simply too reliable,” says Jamie. “Look at the biggest companies of the 90s. That list is completely different from today.”
Therefore, in order to construct forecasts commonly referred to as capital market assumptions in the investment world, we pretend that a global market portfolio is the best. You can reverse engineer the expected returns because you know roughly how each of these asset classes will work with each other. This robust mathematics is expressed in a deceitfully short equation –μ=λσω Market-This can be read in detail in our complete portfolio construction methodology.
From there, we simulate thousands of passes for the market and find the best allocation for each pass, taking into account both forecasting and large asset managers like BlackRock. Then, average these weights to land on a single recommendation. This “Monte Carlo” style simulation is commonly used in variable-filled environments. For example, the environment such as capital markets.
The output is the asset allocation rate (updated annually) that appears in the holdings of the portfolio details
Hypothetical portfolio; illustrations only
However, at this point on the journey, the investment team’s job is barely finished. They should look for some of the most cost-effective and merely effective funds that provide intended exposure to each relevant asset class.
To do this you will need to go to the market from your test kitchen. So don’t forget your tote bag.
