The Perfect Monster ETF Trade Just Played Out

How does a fund go from $1.2 million to $268 million in assets? Easily.

Reviewed by: Dave Nadig
Edited by: Dave Nadig

Sometimes the best stories in ETF-land are the ones that go unnoticed. Sure, one hiccup occurs somewhere in the system and we see headlines for weeks (I’m looking at you, GDXJ (VanEck Vectors Junior Gold Miners ETF), “Popular Gold Miner ETF To Change Dramatically”). But most of the time, ETFs do incredible, extraordinary things, and nobody notices.

The best recent example of this was a once-little-known fund named the PowerShares S&P Emerging Markets Momentum Portfolio (EEMO). This is a fund that, until May 1, was a poster child for forgotten funds with under $2 million in assets, going days and weeks at a time without trading. Its FactSet “Tradability” score is just 29 out of 100, and its time-weighted average spreads were over 2%.

Yet all this time, EEMO was just waiting to get noticed, and here’s the thing: Like a lot of funds, it’s a perfectly fine ETF. It charges an utterly reasonable 0.29% expense ratio for access to a concentrated portfolio of emerging market stocks that exhibit classic momentum characteristics. This is essentially a hopped-up version of a standard emerging market fund, which has tended to outperform in up markets and underperform in flat or down markets.

Then on May 1, someone put through a $268 million trade on EEMO.

This is a rare case where the chart doesn’t actually tell you much of the story. 

The orange line in the chart above is the indicative value for EEMO on May 1. It’s flat for good reason—most of the securities in the portfolio don’t trade during U.S. hours, so what we’re really seeing here is the previous night’s net asset value (NAV) being adjusted for those few securities that were open, as well as currency moves. The little white and blue dots are trades (white is an uptick, blue is a downtick).


The Big Trade

Until 11:49 a.m. EST, exactly one share had traded hands. Then, boom, 15 million shares. It’s not often you see a tape like this (from the excellent Trillium Surveyor tool):

‘Gundlach Bet’

So, how does this happen? Well, obviously, this isn’t someone just dropping their $268 million buy order on the market. This is a worked trade.

Someone—a large institution obviously—is making the current “Jeff Gundlach Bet” by going all-in on high-growth emerging market companies. They decide they like this particular fund to use, and they call someone—maybe KCG, or Cantor or Susquehanna—and they negotiate the trade. (Note: Bloomberg thinks they know who made the trade, which was the "buy" end of a rotation reallocation.) 

While we don’t know the specifics here, most likely it involved some sort of cost-plus pricing. Since it takes some time to get the basket of securities here, as markets are closed, someone has to “wear” some risk. 

Either the liquidity provider offered the price of $17.14, believing they could buy what they need over night, or they spent the previous night buying securities ahead of this incoming trade, and worked up a price based on their actual costs for the underlying securities.

Regardless, the price is actually unique in that it’s a live, midday price on a bunch of closed securities. That intraday net asset value (iNav) line from the chart hovering around $16.96 is a fiction. While $17.14 seems like a big premium to pay over either the previous day’s NAV of 16.91 or the official NAV by the end of May 1, 16.95, it’s worth noting that it’s right in line with the next day’s NAV of $17.12.


Moving The Underlying Needle A Bit

This suggests to me that perhaps (and this could be a bit of a reach), the liquidity provider filled this order on May 1 at 11:49 a.m., then started building the basket of stocks as best they could to do the big creation with PowerShares. That buying then pushed the underlying stocks up ever so slightly.

This is not an (entirely) crazy idea: EEMO weights roughly 10% in Samsung, for example, which trades about $800 million a day. Showing up with a $25 million buy order on the morning of May 2 has to move the needle at least a little bit.

This is the beautiful thing about the creation/redemption process. The demand for the ETF flowed directly out into the underlying markets, just as if the institutional buyer here had just started buying up emerging market stocks one by one.

Back To EEMO

Once that monster trade hit the tape, it was an object lesson in “algos gone wild.” Dozens of 100-share prints run from half a second after the print, then the one-share trades and the larger volume trades followed. By the end of the day, 15.982 million shares had traded, meaning 288,180 shares traded hands that weren’t part of the monster trade.

That would have made it the biggest day for EEMO, which was launched in 2012, I can find for the past few years. Volume follows volume, and in this case, that volume has stuck around, at least a little. In the seven trading days since the big trade, volume has been consistently in the 50,000-shares-a-day range. That’s hardly hyperliquid, but a far cry from the literal zero-share days that were far more common in the past. Spreads have also come in substantially even for normal-sized lots.

To me, the best part of this story is that basically nobody noticed. EEMO went from a “have not” to a “have” overnight, bringing assets and liquidity where there was none.

And somewhere, a giant institution now has the exposure they want, all through a simple, single trade that perfectly transmitted the buying pressure cleanly into the underlying markets.

At the time of writing, the author owned none of the securities mentioned. You can reach Dave Nadig at [email protected].


Prior to becoming chief investment officer and director of research at ETF Trends, Dave Nadig was managing director of Previously, he was director of ETFs at FactSet Research Systems. Before that, as managing director at BGI, Nadig helped design some of the first ETFs. As co-founder of Cerulli Associates, he conducted some of the earliest research on fee-only financial advisors and the rise of indexing.