The Robin Hood regime

It has become apparent to me in the last month that there has been a massive transfer of wealth from the gigantic hedge funds running factor models to many day-traders with accounts less than $10M. I call this the Robin Hood regime (regime being a common technical term referring to a particular trading environment, as in "this is a mean-reverting regime"). Many, many day-traders that I heard from have had one of their best months in a long while. Is this just luck, or is there a deeper explanation?

I believe that there is a philosophical difference between factor models and many of the mean-reverting strategies that day-traders like to employ, a difference that works to the day-traders' favor. I recall a wise musing from one of my former bosses: he believes that a trading strategy will be profitable in the long run only if it performs a service for other market participants. The service that mean-reverting strategies performs is the provision of liquidity, in particular, short-term liquidity. What service does factor models provide? They seem to be just arrogant bets on the correctness of the managers' convictions. For e.g. I believe that stocks with good earnings will rise in value. Or, I believe that stocks with increasing price momentum will continue in that momentum. True, most of the time the convictions of the best managers are correct, and many of these convictions are actually mean-reverting as well (for e.g. the "value" factors). But on average, a factor model may take away as much liquidity from the market as it provides. And sooner or later, some of these convictions are wrong. Maybe not wrong for very long, but long enough to cause investors' panic. This may be part of what we are seeing recently.

Now am I advocating that every gigantic fund simply just switch from factor models to pure mean-reverting strategies? No: that would be impractical when the portfolios involved are in the tens of billions. If everybody run mean-reverting strategies, there will hardly be any mean-reversion left to profit from. (Look what happened to pair-trading in the last few years.) When you are an investor in a multi-billion fund, and you expect the fund to deliver higher returns than the risk-free rate, you just have to accept that high short-term returns volatilities will be part of the bargain, just like any long-term investments.

CNBC Interview video

Here is the link to my interview on why quantitative models are losing money of late:

CNBC interview

Folks, I will be appearing on CNBC tomorrow (Tue, August 14) for a live interview about quantitative trading with Maria Bartiromo. The segment will be aired around 3:20 pm ET.

A reader's comment on quant funds' losses

A reader of mine (who wish to remain anonymous) pointed out that most of the losses seem to come from low-frequency trading models, while high frequency models continue to perform superbly. This also confirms my own experience. My enthusiasm for high frequency trading was expressed previously here and here.

An update on why quantitative funds are losing money recently

A story just came through Dow Jones newswire ("How Black Boxes Became Pandora's Boxes" by Spencer Jakab) suggesting that recent losses are due to factor models gone bad. Given my expressed distaste for such models, that should have been my first guess instead of blaming the "exotic" models!

Why are quantitative funds losing money these days?

The New York Times today has an article about several well-known quantitative hedge funds incurring significant losses in recent months. I was quoted in saying that traders running similar quantitative models could contribute to market volatility. This is certainly true if the strategies are trend-following. What puzzles me, however, is that most statistical arbitrage strategies are mean-reverting: they buy during investors' panic, and sell during investors' euphoria, and should be richly rewarded in this volatile market by providing sorely needed liquidity. And indeed, from my own experience as well as hearing from other traders, mean-reverting strategies are performing very well recently. So where did those losses come from? My guess is that, as I have observed before, many traditional stat arb strategies are getting boring and generating diminishing returns, and therefore many of the quantitative researchers are driven (by their own professional pride or their bosses) to come up with more exotic and higher-return strategies that ultimately may not stand the test of time. For us quants, remembering Occam's razor and that our job is to generate returns as opposed to producing brilliant mathematical models is often a hard lesson to learn.