Outsmarting the Market
Today is blue Monday (a.k.a. the saddest day of the year), so I thought I would distract everyone and get their blood pumping a bit by sharing some thoughts on a controversial subject.
There has been a meme going around the industry recently about the need to be more active in asset allocations. While the problems with passive allocations are easy to display (and there are many), I think the difficulty of the alternative is WAY understated. Barclay‘s is an organization that spends around a billion dollars a year trying to outperform the markets. For their efforts they have managed to add alpha of only around 1.64%. And this is with the benefit of only having to deal with 2500 clients whose average assets are in the hundreds of millions each.
As client size goes down, the cost of servicing those clients goes up which will eat into that 1.64% of alpha. Also, pursuing alpha tends to be a winner take all strategy, where those at the top tend to reap the vast majority of the gains at the expense of all the other players. So you need to be playing at a Michael Jordan level to compete effectively in this arena.
Lastly, the effectiveness of a signal is always at risk, so you must limit yourself to tilts unless you want to expose yourself to a potential blowup like Amaranth or LTCM. And you must always be in search of new signals to replace those which have blown up.
A quick disclosure about myself. I invest my clients in a relatively passive way but I am not a “true believer” who believes it is impossible to exploit any mispricings in the market. Instead I just believe the hurdle to win reliably in active investing is at Olympic levels.
The anatomy of a mispricing
Unfortunately, arguments pro and con towards active investing tend to be very abstract which is not where most people do their best thinking. So I thought I might walk through an example to help people understand some of the difficulties.
Imagine that you discover that the market does not reliably adjust all stock prices when they go ex-dividend. In other words, some percentage of stocks (not necessarily always the same ones) don’t drop in price by their dividend when they go ex-dividend. Clearly this is an exploitable mispricing. All you need to do is buy a basket of stocks right before their ex-dividend date and sell them right after and you should make a profit.
What should also be very evident is that this exploit will have limits. The more money you pour into the method, the greater an effect it will have on the prices of the stocks being traded until eventually the stocks are dropping by their dividend amounts and no further gains can be garnered by this mispricing.
Two additional complications enter the picture. The first is that other players are watching your actions and as it becomes apparent what you are doing, others will follow suit quickening the demise of the mispricing.
The second problem arises in determining when that demise has occurred. Imagine that the mispricing occurs in a random 10% of stocks but with a standard deviation of 20% meaning that sometimes the basket of stocks actually exhibit the reverse behavior of dropping by more than the dividend.
With this additional twist, how do we decide when the mispricing is gone? A single bad round is not indicative. Instead with each failed round we only become more sure that it has died, but it will take a few rounds of failure to reach a reasonable amount of assuredness. And to make things worse, as others discover this “mispricing” and pile on they will exacerbate the potential downside (making it worse than what you probably originally modeled).
So what can we learn from this example. To benefit from a mispricing you must understand its capacity (how much money can be run through it and how much can be made). And you must understand when to get out toretain most of the alpha. It is clear there is a HUGE first mover advantage. In other words, those who discover the mispricing have a huge leg up on those who follow afterwards. Being first, means having the smartest people with the best information which generally means having the most money. So it becomes of contest of giants with ever slimmer gains with ever growing expenses. To me, this is not a game for those handling clients each with a few million here or there.
Dimes in front of Steamrollers
There are lots of ways to make bets which have a high percentage chance of a small upside and very small chance of a large downside. By playing these odds in a way that is not visible to investors, a money manager can seem to “beat” their index consistently. When the day of reckoning finally comes to rest, excuses are made about how it could not have been foreseen, the fund is closed, and a new one with a clean slate is opened soon after. This behavior is most problematic in the hedge fund arena where derivative use is rampant and hiding these kinds of risks from investors is trivial.
I hope these examples shed more light than confusion. Let me know!