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Is Your Strategic-Beta ETF Secretly Picking Stocks?

The answer may surprise you.

A version of this article previously appeared in the July 2022 issue of Morningstar ETFInvestor. Click here to download a complimentary copy.

Attempting to beat the market by picking winning stocks is the exclusive domain of discretionary active fund managers. These investors are willing to incur stock-specific risks because their judgment and experience suggest they have favorable odds of those risks being rewarded.

Betting on individual stocks rarely, if ever, comes up in the analysis of strategic-beta exchange-traded funds, and for good reason. Rules-based investment strategies were initially conceived as a cheaper way to capture the investment styles employed by successful discretionary active managers. They try to provide exposure to compensated risks, such as stocks trading at low price multiples (value) or those with strong momentum, as a way to potentially beat the market while avoiding the need to pick stocks.

But not all strategic-beta ETFs successfully tamp down their exposure to stock-specific risks. Some allow these risks to creep into their portfolios—often unintentionally. That adds an additional layer of uncertainty to their expected performance. In some instances, the payoff may not show up. In others, it may work against investors.

Factors in the Driver’s Seat

Great strategic-beta funds provide meaningful exposure to their targeted factors while taking measures to reduce other risks that are less likely to be rewarded, including risks specific to a given stock. Both conditions are key to their long-term success.

Avantis U.S. Equity ETF AVUS, which earns a Morningstar Analyst Rating of Bronze, is one ETF that effectively manages these two sources of risk. While it doesn’t track an index, its managers follow a firm set of rules and diversify across an extensive set of stocks rather than picking individual names.

The managers start with all stocks listed on U.S. exchanges, including small caps, and initially weight them by market cap. From there, they strategically modify each stock’s weight to emphasize those with lower price/book ratios (value) and greater profitability, two characteristics that have historically been linked to market-beating performance.

Holding almost every publicly traded stock means that AVUS’ portfolio doesn’t place an excessive amount of weight on any one in particular, while strategically reweighting certain names emphasizes exposure to the value and profitability factors. In other words, it effectively allocates most of its active bets and their associated risks to those targeted factors, without incurring much exposure to other risks that may not be compensated.

Comparing AVUS against Vanguard Total Stock Market ETF VTI shows this in more detail. Both portfolios hold thousands of stocks, and neither one places big bets on single names. As of June 30, 2022, the top 10 holdings in each ETF represented 22% of VTI’s portfolio, and 17% of AVUS’, so stock-specific risks are not a major concern.

AVUS’ factor tilts have been observable in its average fundamental characteristics. Its price/book ratio has consistently landed below that of VTI. The ETF’s preference for more-profitable stocks is less obvious because its average profitability, as measured by return on invested capital, is typically on par with VTI's. However, stocks trading at lower multiples tend to be less profitable; it’s one of the reasons for their lower price tags. So, keeping AVUS’ profitability in line with VTI's is evidence that its profitability tilt is still at work.

AVUS’ factor tilts appear to be responsible for its market-relative performance over the past two-plus years since its inception. Stocks trading at lower multiples performed poorly during the coronavirus-driven drawdown in early 2020, and AVUS subsequently underperformed VTI by 2.6 percentage points between Feb. 6 and March 23, 2020. But cheaper stocks were an advantage for AVUS when they rallied in late 2020. Between November 2020 and June 2022, it beat VTI by 5.2 percentage points per year.

Factors Along for the Ride

Problems can arise when strategic-beta funds prioritize the strength of their factors over other risks. Issues usually show up when a strategy places too much emphasis on a small group of stocks with the strongest characteristics. In doing so, these strategies have strong factor characteristics, but they don’t adequately diversify away stock-specific risks.

Neutral-rated VictoryShares USAA MSCI USA Value Momentum ETF ULVM is a multifactor ETF that encounters this sort of problem. It pulls its holdings from the large-cap-focused MSCI World ex USA Index—a smaller pool of stocks than AVUS'. But that isn’t a huge concern since large caps tend to represent most of the portfolio weight in AVUS and VTI, so they should dictate most of each fund’s future performance.

The breadth of ULVM’s portfolio is where it appears to get into trouble. It targets stocks with a strong combination of value and momentum characteristics, but it holds only the top 25% of the MSCI USA Index by those combined measures. That leads to a narrower portfolio of stocks (about 200) than its parent universe or AVUS.

Focusing on a small subset of stocks with the most attractive value and momentum characteristics can be both a blessing and curse. It potentially increases ULVM’s value and momentum exposure, which could lead to better long-term performance. But it also amplifies the influence of stock-specific risks, or risks that may not be rewarded over the long run.

Compared with AVUS, ULVM’s factor exposures are trickier to quantify using average portfolio characteristics because there isn’t a great metric to assess an ETF’s average momentum. Statistical tools like a regression analysis indicate that it had significant exposure to stocks with lower valuations and strong positive momentum.

While its factor exposures stack up to expectations, its performance raises some questions. It trailed AVUS by 3.7 percentage points between October 2019 and June 2022, the 33 months where their live track records overlapped. It also underperformed VTI by 2.4 percentage points per year over the same period.

What’s more important is the reason ULVM trailed the market. The results from a statistical attribution are presented in Exhibit 1. It breaks down the total return of AVUS and ULVM into the contributions from the market, four factors (small size, value, momentum, and quality/profitability), and other sources of risk that are collectively referred to as alpha. The attribution is an approximation and prone to some small errors, but it’s still useful for identifying the key contributors to each ETF’s performance.

Table comparing performance attributions of AVUS and ULVM.

AVUS and ULVM had similar exposure to the market’s risk, so they both captured about 20 percentage points of annualized return between October 2019 and March 2022. Value was the only other factor that contributed a substantial amount to each fund’s performance. It added an additional 0.83 percentage points to AVUS’ total return, while ULVM’s advantage was nearly twice that. That makes sense because both ETFs intentionally targeted cheaper stocks, which performed well over these two-plus years. Profitable stocks didn’t provide much advantage for AVUS and momentum didn’t help ULVM over this particular period.

Alpha, or other sources of risk, was the only other component that noticeably contributed to performance. It didn’t have much impact on AVUS, subtracting 0.27 percentage points from its annualized total return. It had a stronger influence on ULVM, damaging its performance by about 5 percentage points per year.

The term alpha is difficult to account for because it captures a wide range of other risks not addressed in the model. There’s no clear connection between cause and effect, but one major difference between the two strategies stands out. AVUS holds just about every stock in the market and deviates from the market by only modest differences in each stock’s weight. ULVM holds a more compact portfolio of roughly 200 stocks that looks considerably different from the market. As a result, ULVM’s performance relies more on those 200 names. Said another way, stock selection played an important role in ULVM’s performance, but there’s no manager providing any form of judgment or discretion over the individual names that it holds or their respective weights.

All signs appear to point at stock selection as the main cause of ULVM’s lackluster showing, and it sheds some light on how to think about strategic-beta ETFs. Using factor characteristics to build a compact portfolio of stocks or bonds can be problematic, even if that approach works to their advantage. The direction and magnitude of their performance can be difficult to forecast, and it may lead to some surprising, if not unpredictable, performance patterns.

Complicated attribution tools are useful for identifying this problem, but they’re time-consuming to build and not always practical. Fortunately, there are some simpler signs to look for. Strategic-beta ETFs that hold a relatively small subset of stocks from their parent universe are more likely to be influenced by stock-specific risks. At the same time, those that provide consistent exposure to their targeted factors and hold hundreds, if not thousands, of stocks are more likely to derive their edge from their factors, with little impact from stock selection. Exceptions exist, or course, and these guidelines won’t catch everything. But looking for strategies with more holdings and greater diversification should improve the odds of selecting a strategic-beta ETF with fewer performance surprises in the future.

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