Exchange ETF U: Watch out for “Gremlins”
Think over 3,000 ETFs is a lot? See even bigger as the industry is booming, with another 431 funds going live last year. It takes a lot of advisers to try to find the signal in the ETF noise, the subject of the ETF U: Sorting Through a Growing ETF Universe panel on Sunday at Exchange: an ETF experiencewhere panelists including Eric Balchunas, Principal ETF Analyst at Bloomberg, Elisabeth Kashner, Director of ETF Research at Factset, and Aniket Ullal, Head of ETF Data and Analytics at CFRA, all shared their thoughts .
In such a vast world of ETFs, parsing data into digestible bites is essential — that’s how Factset approaches its ETF work, according to Kashner, describing each ETF with a fit score, letter grade and a story on their site with metrics like effectiveness, marketability, and fit. She added that misunderstanding and choosing the wrong ETF leads to tracking errors, trading gaps and performance gaps.
“We’ve had years where the difference between the top and bottom of the ETF in a category is 60%. So getting it right has really made a difference,” Kasher said.
Factset also allows advisors to compare strategies with an effectiveness analysis, in which, for example, the Invesco DWA Energy Momentum ETF (PXI) and the Vanguard Energy ETF (VDE) can be compared based on data points such as median tracking difference, risk of fund closure, use of derivatives, and even average spreads. PXI, for example, has an average spread of almost 25 basis points, while VHD only has a spread of three basis points, which makes it a bit cheaper.
For Balchunas, there are five factors to consider in the ETF Due Diligence checklist: exposure, cost, liquidity, risk and rating systems. Balchunas says it’s important to look at ETFs and rate them based on risk factors that in some ways mirror movie ratings, from G to R, but at Bloomberg it’s called the traffic light system ETF rating strategies from red to yellow to green. He listed the risks contributing to these ratings, including leverage, potential future rolling costs, lack of liquidity, and more.
“All commodity ETFs seem so innocent, but those rolling costs will eat you alive. You’ll have made the right bet but won’t make any money,” Balchunas said, comparing at least one strategy to Gremlins, the cute monstrous creatures purchased from the 1984 Film.
What are some of the advantages of such a rating system? It covers all ETFs, Balchunas said, adding speed and an acknowledgment that most people don’t actually read prospectuses. By looking at so many different ETFs, fields in the Bloomberg Intelligence system can allow investors to see metrics such as purity, in which revenue comes from pure play sources, or ESG, measured at Bloomberg Intelligence with ETFs rated with a E, an S or a G
Concluding the talk, Ullal touched on CFRA’s own research process, looking at ETFs in a broader context. Taking into account model portfolios, industry, sector and thematic factors and market commentary, the CFRA then takes a close look at each given company.
“What we’re doing is taking the entire ETF universe globally and categorizing it in a granular fashion,” Ullal said. “Once we’ve done that, we can get into other ETF-specific and ETF-level comparisons.”
The CFRA uses machine learning to help produce the company’s star ratings, a one-to-five star system that is powered by information such as industry performance and spending relative to other ETFs. To build the machine learning model, CFRA analysts collect the ratings of the underlying stocks and add forensic trends in addition to the earnings score and manager track record in addition to the sector trends. The star model is updated monthly, the firm having moderated the ratings, so that they do not fluctuate wildly thanks to a smoothing algorithm.
“Over time, it gets smarter; it learns through the data we’ve already compiled,” Ullal said. “We really try to make sure the data is diverse and really represents what’s happening in the market.”
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