Finance & Investing

Vulnerable Prey: How Short-Sellers Outsmart Mutual Funds

Mutual funds regularly underperform in the markets. Here’s why.

June 07, 2016

A bird of prey

Are nimble brokers preying upon slow mutual funds? | Reuters/Baz Ratner

For years now, actively managed mutual funds have been dogged by an embarrassing conundrum: For all their sophistication and expertise, mutual fund portfolio managers have consistently underperformed market averages.

It takes unusual talent to do worse than throwing darts at a list of stocks, but mutual funds seem to have mastered the art. Over several decades, the evidence shows that mutual funds as a group have an uncanny ability to consistently buy when prices are too high and sell when prices are too low.

The problem, as it turns out, isn’t necessarily that portfolio managers lack stock picking expertise or financial competence. A big part of the problem is that mutual funds are clumsy traders. When they trade, they often do so in concert, buying (or selling) the same stocks on the same day. In other words, they behave like a herd of lumbering elephants: You can detect them a mile away. This allows more nimble rivals to outmaneuver them and even take advantage of the disruptions they cause.

In a new study I co-authored with two colleagues at Indiana University, we documented exactly how some short-sellers appear to have profited by moving in the opposite direction to mutual fund trades. When mutual funds ramp up their buying of a particular stock, short-sellers are likely to double-down on shorting that same stock. When mutual funds sell heavily, the shorts throttle back.

In a nutshell, short-sellers are correctly anticipating that big moves by one or more mutual funds will cause temporary price distortions. When mutual funds ramp up buying in a stock, they temporarily drive the price higher, to above normal levels. The short-sellers, anticipating that the price will soon slip back, respond by ratcheting up their shorting activities. When the stock price drifts back down, as it often does, the shorts reap a nice profit.

This isn’t about “flash boys” or high-frequency traders, who use sheer speed to front-run trades by big institutions. That kind of trading often amounts to gaming the system and can be quite predatory.

What we are documenting here is quite different because the short-sellers are helping to absorb the price distortions caused by mutual funds. This is actually healthy in some ways for the overall market. Unfortunately, it’s not good news for these funds’ investors because mutual funds’ vulnerability reduces their profit.

What’s remarkable is how effective the shorts are in detecting and capitalizing on mutual fund moves. Betting against mutual funds, it turns out, can be a very profitable strategy.

Betting against mutual funds, it turns out, can be a very profitable strategy.
Charles Lee

Our study required some detective work. We examined vast amounts of trading data for mutual fund clients of Ancerno Ltd., a transaction-cost consulting firm. Ancerno serves many of the nation’s biggest mutual funds, and the trading record of these funds gives us a way to track the direction of the elephant herd. We then compared those trades with daily short-selling data from the New York Stock Exchange.

The first thing we found was that short-sellers consistently moved in the opposite direction of mutual funds. On 67% of the days when mutual funds made heavy moves to either buy or sell, short-selling activity moved in the other direction.

The second thing we found is that the short-selling strategy is generally profitable.

Consider the case of Estée Lauder, the big cosmetics company.

On Aug. 16, 2005, Estée Lauder announced great quarterly earnings and the rollout of a new brand called Grassroots. The market reaction was strongly positive, as its stock price jumped from $18.70 to $20.92 (a gain of almost 12%) by Aug. 18.

Our data show that mutual funds played a key role in the price move. Starting on Aug. 16 and extending to the next four days, net buying by Ancerno’s mutual fund clients alone accounted for more than 15% of daily trading volume in the stock. Ancerno clients are only a fraction of all mutual funds, and it is likely that other funds were also buying. Our estimate is that total buying across all mutual funds was at least 30% of daily trading for each of these five days.

Almost simultaneously, during the same five-day period, short-selling in Estée Lauder shares spiked 37%. Indeed, over these five days, trades initiated by short-sellers in Estée Lauder accounted for one out of every five recorded transactions.

In hindsight, these short-sellers were smart. It turned out that Estée Lauder’s closing price of $20.92 on Aug. 18 was to become its high-water mark for the year. By the end of August, the company’s stock price had dropped below $18, and by the end of 2005, it was down to $16.74 (a 20% decline).

This was hardly an isolated instance. Looking at the trading data in 3,800 companies from 2005 to 2007, we found that a strategy of going with the short-sellers when mutual fund trades are in the opposite direction yielded an average risk-adjusted return of 1.41% over the next three months. What is the return to short-sellers when mutual funds are not trading in the opposite direction? A paltry 0.04% over three months. In other words, the informational advantage of the short-sellers seems to be coming almost entirely from their response to mutual fund trades.

How do the short-sellers pick up so quickly on mutual fund moves? Some of these moves are predictable and easy to spot. Once a group of mutual funds start actively buying or selling a stock, they usually continue to do so for several days or even weeks. So some of the daily trading pattern of the mutual funds could be deduced from their past behavior.

But some of the short-sellers’ intuitions are more startling. It turned out they were also extremely quick to mirror “unexpected” mutual fund moves, meaning those moves that weren’t part of an ongoing pattern. We aren’t entirely sure how the shorts are able to intuit these moves, but they are clearly picking up on some important indirect signals.

We are not the first researchers to show short-sellers are smart, but we are the first to link this phenomenon to mutual fund trades. My favorite analogy is to hunt for tuna in deep-sea waters by watching for frenzied activity by seagulls. Why? Because when tuna are attacking a school of smaller fish, the seagulls gather above and swoop in to catch the fish parts that tuna leave behind. We cannot see the tuna (the mutual funds), but the behavior of the seagulls (the short-sellers) gives them away.

Ironically, mutual funds could probably defend themselves better if they had more authority to conduct their own short-selling. That would give them the ability to tamp down the price distortions caused by their own buying. But it would also require the authority to use leverage, which most mutual funds don’t have. Hedge funds, which do have that authority, are less vulnerable.

What are ordinary investors to do?

Investors are already pouring billions of dollars into more robotic exchange-traded funds, which simply mirror broader stock indexes. ETFs are certainly less vulnerable to the problem we have identified, but they don’t allow a person to bet on money managers whom they believe have outsized talent or unique strategies.

Some investors may well want to shift to do-it-yourself investing, which is more realistic than it once was. Affordable software programs make it much more practical to construct “do-it-yourself” portfolios that are based on particular strategies and goals. Indeed, the growing array of “robo-investing” firms employ precisely that approach in building portfolios without relying on traditional mutual funds.

But do-it-yourself strategies require time and energy, and robo-investing isn’t for everybody.

For the moment, it’s simply important that investors recognize that mutual funds have hidden costs and that those costs are at least part of the reason these funds have generally underperformed the market averages. The better we understand the problem, the faster we can come up with solutions.

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