CIO Magazine Innovation and IT Strategy blog

Ms. Elana Varon who writes the CIO Magazine's Innovation and IT Strategy blog quoted me today in saying that some quantitative investment models are over-engineered. This old article of mine is an elaboration of my view on this.

The Perils of Momentum Strategies

Not so long ago I was an agnostic with respect to choosing between mean-reverting and momentum models: I felt that depending on the particular model or environment, each can be profitable. Lately, however, I am increasingly skeptical about the long-term profitability of momentum models. The main reason is the increasing competition among traders, algorithmic or otherwise.

As I mentioned in my previous post, when more and more traders decide to adopt mean-reverting strategies, all they do is to eliminate the trading opportunity. The market becomes efficient, and nobody makes any money, but nobody loses either. In contrast, when more and more traders decide to adopt momentum strategies, the momentum will be established sooner and sooner. For e.g. in the case of event-driven strategies which are mostly momentum-based, the new equilibrium price will have been established almost instantaneously after the event is publicly disclosed. Under this circumstance, any momentum trades that are entered just a little bit late will not only suffer zero profit, but will likely suffer losses as mean-reversion almost inevitably takes over. But how soon do we need to enter in order to avoid this fate? (It can't be too soon either because often a trend need to be established first in order to trigger an entry signal.) It is unfortunately a moving target as competition increases: 1 day earlier might work now, but may not be sufficient a few months from now. (The exit trade also suffers the same problem, as we don't know how long the momentum will last.) It is a dangerous game to play.

Indeed, time is often a friend of the mean-reversion trader: the longer s/he waits, perhaps the more profitable the trading opportunity. And if s/he enters too early and suffers a loss, s/he can always double the position. As I explained in a previous article, stop-loss should generally not be applied to mean-reverting trades on a short time-scale. So even if the trader does not double-up the position, an eventual re-couping of the loss is more than likely. On the other hand, time is an enemy of the momentum trader: if s/he loses the first-mover advantage and suffers heavy loss, I argued in that article that a stop-loss is advised, and thus the loss is forever locked-in.

Given this asymmetry, it is no wonder that algorithmic traders have been warning me long ago that it is hard to find a profitable momentum trade. And I was silly enough not to pay heed to them until now.

Further debate on factor models

A reader from a hedge fund (who wishes to remain anonymous) sends me some thoughtful comments about factor models. He has graciously allowed me to reprint them here:

"With regards to your blog entry, 'The Robin Hood regime': this weekend I was actually also thinking about the philosophy behind factor models which you allude to in the post. I am wondering if you have any other thoughts as to what service factor models provide? Relegating them to 'just arrogant bets on the correctness of the managers' convictions' isn’t completely intellectually satisfying to me.

I look at factors as such: the returns I get for exposure to various factors can come either because the market is inefficient and systematically misprices those factors (alpha), and/or because I am providing some service via the exposure (and collecting some kind of risk premium associated with that service). My question #1 to you is, are you convinced that all of the returns to factor models are indeed simply from risk premiums and not alpha? If alpha exists, it’s less clear that a service needs to be provided to the market, at least to me.

However, let’s assume (as I believe your boss did) that in the long run, the market is efficient. Then, you will be compensated for factor exposure only by bearing some risk or providing some service. In my mind, some particular conviction of a manager doesn’t necessarily qualify for a risk factor in and of itself - I think we agree on that point. But are there possible fundamental, valuation-based explanations behind these factors? Perhaps low VALUE companies are generally those companies with bad recent performance but which are expected to turnaround / mean-revert (as you somewhat suggest in your post) and the risk you bear when buying a low P/E company is “turnaround risk”. Or perhaps high MOMENTUM companies are companies riding an industry trend and you are bearing “trend continuation risk”. So, my question #2 to you is, are you convinced that there are no such explanations?

If factor models do indeed work, it seems to me that there must either be real risks behind the factors, or alpha, or both."

And here is my response:

"I believe the service that some value factors provide is the efficient allocation of capital to those companies that deserve them, just like any value investors do. In this case, the factors hope to identify these companies faster than humans can, and therefore bring capital to them sooner. I have no argument with these factors as they also provide liquidity, albeit on a longer time-scale. However, with regard to various momentum factors, they are in fact just betting on certain behavioral characteristics of investors, or on the slow dissemination of news, etc. You can argue that they provide a service by improving the efficiency with which information about companies disseminate. But the problem is that once everybody are using these momentum factors, the market becomes efficient and any further bets generate losses.

So I am quite willing to accept that many of these (momentum) factors represent alpha, but these factors are generating more losses as more investors employ them. I am also willing to accept that many of the (value) factors represent risk premia. As more investors employ these, the profit goes to zero, but fortunately not negative as the risk also disappears."

The "failed" factors have reverted

As I said in my CNBC interview, investors just got to be patient with the factor models. Sure enough, we are seeing reports that the large drawdown suffered by these models has already reverted as of Friday.