How Betterment Automates ETF Selection

September 09, 2015

This article is part of a regular series of thought leadership pieces from some of the more influential ETF strategists in the money management industry. Today’s article features Ellie Lan, an analyst on the investment team; and Dan Egan, director of behavioral finance and investing at the New York-based automated investing service Betterment.

When you use algorithms and data to make decisions, you agree to a very basic principle: You clearly and logically lay out the steps you follow, and then use the answers that your analysis produces—whether or not that jibes with a personal opinion or feeling, or worse, incentive provided by someone other than your customers.

At Betterment, this is a philosophy we apply at every level of the company—including how we selected the funds that are used in our portfolio.

As mentioned in our portfolio selection white paper, the following core criteria drove our selection process:

  • Positive expected return, after adjusting for taxes and risk
  • Low cost
  • Highly liquid
  • Passive/index-tracking
  • Low turnover
  • Tax efficiency

We considered the full universe of U.S.-listed exchange-traded funds. To select funds for our portfolios, we created a filtering and ranking algorithm (using the free open-source programming language R) to choose ETFs that met our core criteria and ranked highly on qualities aligned to our investment objectives. We applied these filters to remove asset classes and investment vehicles that did not meet our standards, and then selected from the top remaining candidates.

We assessed the universe of 1,654 ETFs (as of Sept. 4, 2015). This transparent process resulted in 30 different ETFs for customers to invest across 13 asset classes. We outline the type of ETFs we sought as well as the ones we actively avoided. The process reduced average expense ratio for the overall set from 0.64 percent to 0.20 percent for the final set.

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