Ghosts In The Robo Advisor Machine

ETF.com

This is the second blog in a multiple-blog series by ETF.com’s Director of Research Elisabeth Kashner on the new “robo advisory” industry. The first was titled:“Which Robo Advisor For My Teen?”.

 

“Robots capable of manufacturing robots do not exist. That would be the philosopher’s stone, the squaring of the circle.”

Ernst Junger, “The Glass Bees

This is the second blog in a multiple-blog series by ETF.com’s Director of Research Elisabeth Kashner on the new “robo-advisory” industry.

In my last blog, I highlighted a few of the differences among the “robo advisor’s” all-ETF portfolios, making clear that human subjectivity flows through these firms’ asset allocation robots.

Today I’m going to talk about the brains behind the machines. It’s time for a look at the not-so-evil geniuses behind the robots, with a complete overview of each firm’s value proposition and investment philosophy.

Asset allocation is by far the biggest driver of client returns, so we’ll spend most of our time understanding each firm’s investment philosophy. But before we get there, we’ll look at seemingly small details that, for some folks, can make or break an investment decision.

Moderately risk-averse investors in high tax brackets with both taxable and retirement accounts might limit themselves to services that offer municipal bonds as well as asset-location services, so they won’t care about Covestor or WiseBanyan’s philosophies, since these firms don’t currently offer the services they require.

First, the details, starting with costs.

Although all the robo advisors cost far less than the typical human asset manager, they range widely in cost, from the truly free WiseBanyan to 0.50 percent plus trading fees at Future Advisor. That’s $50 for each $10,000 invested, plus trading fees.

Robo fees can come in several forms. There’s the overt management charge, if any, but also, potentially, brokerage fees, custodial fees and the portfolio’s expense ratios. Because most of the robo advisors use the same dirt-cheap Vanguard funds, the range in expense ratios is minimal. It’s all laid out in the table below.

Wealthfront Betterment Future Advisor Covestor WiseBanyan Invessence
Fees First $10K free, 0.25% thereafter First $10K 0.35%, 0.25% up to $100K, then 0.15% 0.5% + Trading fees Trading fees 0 $250/yr under $100K, then 0.25%
ETF Fees 90% 0.11% 0.11% 0.14% 0.10% 0.08% 0.15%
ETF fees 60% 0.13% 0.13% 0.14% 0.08% 0.10% 0.13%
Muni funds version available? Yes Yes No No No Yes
Tax-loss harvesting Daily Yes Yes No No Working on it
Rebalancing Cash-flow based, with threshhold triggers With dividends, also in conjunction with tax-loss harvesting Quarterly Occasional Yes Quarterly at minimum
Asset Location Yes Yes Yes No No No
Account Minimum $5,000 No minimum $10K for premium level $10K No minimum $5K

Some of these firms won’t work for my son, because even though he received a small pile of cash at his bar mitzvah this year, he doesn’t yet have the $10,000 minimum they require. He’s a Little League umpire too, but he’d have to call hundreds of games to pocket $10,000.

 

Then there’s taxes—half of the firms I surveyed offer municipal bonds in the portfolios; the other half do not. Robo investors should take their tax rates and fixed allocation needs into account when considering the all-in cost. Although Future Advisor doesn’t use muni funds, it does offer asset-location services that can help you avoid taxes on your fixed-income funds.

Tax-loss harvesting can play a huge role in cost reduction, though it is not without risks. Wealthfront, Betterment and Future Advisor each offer tax-loss harvesting services. Each claims hefty returns from the practice.

Rebalancing can reduce a portfolio’s risk. Although all the robo advisors offer some type of rebalancing, FutureAdvisor and Invessence’s are triggered by the calendar, while Betterment and Wealthfront rebalance with cash flows, and sometimes in conjunction with tax-loss harvesting.

Jon Stein, Betterment’s chief executive officer, told me that Betterment’s focus on net after-tax returns—asset location, tax loss harvesting and rebalancing—has more impact on long-term investment outcomes than tactical asset allocation.

In other words, Betterment’s focus affects returns more than tweaking a portfolio’s balance between emerging markets and U.S. value stocks could. That’s quite a claim. I’ll take a hard look at that later on in this blog series.

The more similar the portfolios are to each other, the more the service costs, and the more cost-saving services will be a deciding factor. But, as we saw, there’s some surprising variation in these portfolios.

So, let’s set aside the fees-and-service comparisons, and dig in hard to the investment philosophies powering the robots.

Some robo portfolios are built with inflation control in mind; others seek factor exposure; a few try as best they can to simply mimic the market; and one includes the risk/return expectations of its investment committee. Most use optimization techniques, but one explicitly builds separate sleeves for each asset class.

Here’s a top-line breakdown of their approaches:

Wealthfront, Betterment, Covestor, Wise Banyan and Invessence all use some form of optimization. Invessence uses a quadratic optimizer; the rest use a more conventional mean-variance model. Each determines expected returns and volatilities for the asset classes they want to consider, along with correlations among the asset classes, and then uses a complex algorithm to determine the best weightings for each risk level.

Because mean-variance optimization can sometimes produce highly skewed portfolios, especially when using historical asset class returns as inputs, most of these firms modify their inputs or constrain the outputs.

That’s where things get interesting.

 

Wealthfront modifies historic asset-class returns with current market implied expected returns (Black-Litterman) as well as with the in-house views of Chief Investment Officer Burton Malkiel’s team. In addition, Wealthfront sets minimum and maximum weights for each asset type. The resulting portfolio has an unmistakable Malkiel flavor to it, with an emerging market allocation that reflects his interest in China.

Betterment uses Black-Litterman currently implied market expected returns, but deliberately includes small-cap and value as separate asset classes, adding a classic Fama-French factor tilt. It doesn’t constrain the portfolio weights, but they do account for downside risk. Betterment’s portfolios wind up quite similar to the global market, at least on the equities side.

Covestor deliberately veers away from its optimizer to hedge its portfolios against inflation and to adjust for downside risk. Its wide constraints allow heavy weights to emerging markets.

Wise Banyan constrains its portfolio weights “tighter than most,” back toward market-cap weights, according to Herbert Moore, co-founder and chief investment officer. This might explain why its portfolios allocate generously to U.S. equities, and away from the rest of the global equity market.

Invessence includes the largest number of asset types, adding granularity to the fixed-income side. It bases asset-class returns expectations on up to 80 years of historical ETF or index returns, but uses only nine years of volatility history. Invessence employs gold as an inflation hedge. It also constrains all asset weights except for U.S. equity. Sure enough, the U.S. dominates its equity allocation.

FutureAdvisor doesn’t optimize. Instead, its builds its portfolio in sleeves, creating a glide path much as the target-date mutual funds do. It builds in a “strategic” allocation to REITs as an inflation hedge, adding Fama-French type tilts. They’re not kidding. The firm’s portfolios emphasize small- and midcap stocks, and financials (REITS), with highest-in-class dividend yields and lowest price/book ratios.

Wealthfront Betterment Future Advisor Covestor WiseBanyan Invessence
Portfolio construction Mean-variance optimization Mean-variance optimization Sleeves ' glide path Mean-variance optimization Mean-variance optimization Quadratic optimizer
Number of funds 8 (7 in taxable accounts) 12 12 7 10 21
Constraints Yes No N/A Yes Yes Yes
Tilts No Small cap/value ' downside protection Small cap/value ' inflation hedge Growth, inflation hedge ' downside protection No No
Capital markets assumptions Historical returns, market cap weights, investment committee view Historical returns, market-cap weights N/A Historical returns Historical returns, market-cap weights, investment committee view Historical returns
Intent to mimic the markets No Yes No Yes No No

Let’s look at how these philosophies and use of constraints determine each firm’s weighting to Vanguard FTSE Emerging Markets (VWO | C-90) within their 90 percent equity portfolios.

 

VWO Weight
Wealthfront 28.0%
Covestor 21.0%
Future Advisor 17.0%
Invessence 15.7%
Betterment 10.5%
WiseBanyan 6.8%

 

 

 

 

 

Wealthfront’s wide constraints allow Malkiel’s enthusiasm for emerging markets a wide berth. Malkiel explained to me:“We believe ex-ante that emerging markets are likely to return more, on a risk-adjusted basis, than the U.S. market.”

To support his view, he cited a CAPE ratio of less than 15 for the emerging markets vs. 26 for the U.S.

Covestor reached a similar conclusion, by a different process:using the last 10 years of historical returns and volatility to generate future expectations. In addition, its wide constraints allow emerging markets to dominate the equity allocation.

At the other end of the spectrum, Betterment’s CEO Jon Stein told ETF.com:“The difference between a 10 percent and 12 percent emerging markets allocation doesn’t really matter. Diversification among asset classes—stocks and bonds—matters much more.” Betterment’s use of Black-Litterman pushes its weights closer to global market weightings.

Lastly, Wise Banyan’s tight constraints strictly limit its allocation to VWO.

Did you notice that every single robo advisor picked VWO, rather than the iShares Core MSCI Emerging Markets ETF (IEMG | B-99),the Schwab Emerging Markets Equity ETF (SCHE | B-89) or the iShares MSCI Emerging Markets ETF (EEM | B-99) for their emerging markets exposure? How did that happen?

In blogs four and five, I’ll examine each firm’s fund selection process.

The fourfold range of VWO weights between WiseBanyan and Wealthfront clearly shows that the constraints and philosophies do drive asset allocations. This can make an enormous difference in portfolio returns over the long run. For the 10 years through June 30, 2014, the MSCI Emerging Markets index returned 11.9 percent annually, while the Russell 3000 returned just 9.3 percent. Over 10 years, that adds up to a 66 percent disparity in returns.

Of course, there’s no knowing what the future holds. Malkiel could be right, or very wrong. But one thing’s for sure:Even for robots, it pays to do your homework. Stay tuned for the third blog in this series:a full-throttle assessment of each robo advisor’s risk levels and portfolio tilts.


Contact Elisabeth Kashner, CFA, at ekashner@etf.com.

 

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