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Best of TTU – The Problems with Smart Beta & is Filtering the Holy Grail?

Kathryn Kaminski and I had the opportunity to sit down with Nigol Koulajian, the founder of Quest Partners, in which he shared his deep insights in many areas of trading. During our conversation, there were a few short segments that I particularly enjoyed and I would love to share them with you here. Below you can learn about Nigols take on the problems with Smart Beta, and also his explanation of Filtering being ͞the Holy Grail. This interview was packed with insight and wisdom so if you would like to hear the full episode then you can do so by clicking here (#101) and also here (#102).

The problems with Smart Beta

"Nigol: So, the short-term index is flat since inception, or negative or actually down since inception. So yes, "challenging" is, as I said, you’re being extremely gentle.

So, why are short-term managers still getting allocation? This is because of diversification, but also because of convexity. If you’re long a put on the market and it’s going to cost you ten percent a year, fifteen percent a year, twenty percent a year, you can get the same protection for five percent a year negative.  Investors who are pricing these things accurately will see a great investment.

Nigol Koulajian – Quest Partners

"...what’s critical is that the short-term space is much more easily crowded"

So, lucky for us, we’ve been able to provide substantial alpha relative to the CTA indices, whether short-term or long-term. So, in the short-term space what’s critical is that the short-term space is much more easily crowded. When you’re trading short-term, you’re typically trading more stops and trading on stops intraday rather than VWAPing (Volume Weighted Average Price) or trading market on open, market on close.

So, you’re very sensitive to spikes in the market, up or down, and you’re getting whipsawed much more if you have short-term noise. So, what’s critical to do then is to have the right filtering techniques.

Of course, you want to buy cheap convexity, and you want to sell expensive convexity, realized in the markets. One way to do this is, if everybody is trading a ten-day channel breakout, you want to short ten-day channel breakout.

If you look at the short-term CTA index, you can have seventy percent correlation to it by trading ten-day channel breakouts. I know short-term CTAs are much more short-term than that, but ten-day channel breakout I think has seventy or eighty percent correlation.

So, this is kind of like the smart beta version of the short-term CTA index. You want to be shorting that and going long momentum around it. So, if everybody wants to buy the S&P, nobody wants to buy stock number five hundred and one, short the S&P and go long small cap. It’s typical arbitrage of equity long / short.


The same thing applies in the CTA space. You want to short smart beta and go long everything around it. In the short-term space, in particular, where you’re highly affected by the liquidity of the markets this becomes very, very critical. So, there are ways to trade mean reversion where it’s kind of like the “lazy man’s trading,” where you want to find the positive convexity around it, the same as I explained with the S&P.

Niels: Just curious, maybe on a slightly different tack. You bring up the words “smart beta.”Of course in our industry, and in particularly the trend following space, over the years it has been... Certain firms promote trend following as being a very easy risk premia to replicate, so they sell their products very cheaply. Yet, I have not really seen that these products have outperformed the true veterans in that particular strategy.

I’m just curious, but the smart beta products, some of them, have raised billions of dollars because people look at the fees and say, “Oh yeah, it’s easy so we shouldn’t pay so much for it.” So when you say you should short beta and do everything around it, what should you then do? I’m curious whether, in fact, is smart beta (and I’m referring to trend following because it’s easy for me to understand), is smart beta overrated in some ways? That it’s maybe not as effective in capturing that risk premia in some ways?

Nigol: It’s very effective when nobody has money in it.

Niels: Right, OK. (Laughter)

Nigol: The issue with smart beta is that with transparency comes a certain amount of decay. So, although you have multiple firms, each firm is managing three, four, five billion smart beta type of products; or AQR is managing, I don’t know, fifteen or twenty billion, the returns were great just prior to them raising the money.

So, take an AQR replicator: let’s say you use twelve-month momentum, six-month momentum, three-month momentum in combination, you end up with like an eighty-five percent correlation. But then you see that, relative to that basic replicator, once they actually raised the assets then they had substantial decay in performance relative to their own replicator.

"...The issue with smart beta is that with transparency comes a certain amount of decay"

So, what I’m trying to say is that although it seems that individual CTAs are trading one, two, three, four, five percent of volume, or ten percent of volume VWAP, and very gentle, and making markets. Still, markets are heavily influenced by CTA trading. At the points where liquidity is required, there’s not that much liquidity [where they are trading].

CTAs are a very big percentage of volume in the market. It’s easier to see that, trading short-term, saying to yourself, “How are my assets influencing my slippage,” (or that type of thing) and seeing what type of influence you have.

We’re trading one or two percent of volume or three percent of volume, but I think we still have impact. Analyzing the impact on short-term and then going and applying the same techniques of analysis to longer-term, you’re saying that the same exact thing is happening to long-term trading where you’re running hundreds of billions.

So, going back to smart beta, smart beta is great when nobody has money in it. The more assets that come in, the more those specific entry points are going to be affected... those entry points are going to become counterproductive.

So, trend following works. I can give you that as a general statement, as a technique, because it captures something that individuals or investors don’t want to do which is trade...

Niels: Buy the high and sell the low which intuitively feels weird.

Nigol: Correct. The natural thing for human beings, whether in kindergarten, whether in high school, whether you go to business school, everybody teaches you to buy low and sell high. Nobody wants to do the opposite.

So those psychological biases are why trend following (which is a technique which is cyclical in return, it’s definitely not a straight line) sometimes works and sometimes doesn’t. But, when people believe in it, it stops working because assets influence it.


This is a very, very big factor that you cannot look at smart beta outside of this factor. It’s not that investors are coming in because there was a run in return... I don’t believe it’s purely a timing issue, like coming in 2009. I believe that the assets coming into a strategy are having a major impact. That’s my opinion based on what we’ve seen in the short-term space and, again, applying the same things to long-term.

Niels: Just staying with that a little but because now you’re talking about industry, capacity, and so on and so forth, what about the short-term space in itself? Where do you think that some of the techniques that you use... You say one or two percent of volume, but even with that, you can see the footprint. Where is enough, enough, in your space? (Not to end up in the same place as all the ones who have sold smart beta, but are not delivering for their clients.)

Nigol: So the death of channel breakout... Let’s say the Turtle system (buy at fifty day high, sell at fifty day low sort of thing, with a shorter-term stop loss) came when the model went public in the late ‘90s early 2000s. Those models had straight-line equity curves and flat lined in the 2000s.

Then longer-term techniques such as those typically used with moving averages and exponential moving averages, flat-lined later in 2009. So, in the short-term space—let’s say things more similar to vol. breakout—flat-lined, definitely, since 2009 when the vol. has been low. But they were, with filtering... let’s say the smart beta in short-term, that was dead that long, long time ago.

Using certain types of filtering you could still survive and make alpha. Then it became extremely... the more the vol. compressed, the more difficult in became. I would say, short-term smart beta is dead, long dead, and as a matter of fact you can trade against it which is what all those mean reversion models that you see today are..."

Is Filtering the "Holy Grail?"

"Katy: Nigol, you talked a little bit about filtering. Could you maybe clarify more about how you think about that and what you mean by filtering?

Nigol: So, filtering is like the Holy Grail. Basically, the alpha comes from filtering. So, filtering, fifteen years ago trade momentum or trend following occurred only when the vol. was compressed. That's one way, that's the way it was.

Then maybe five or ten years ago it was trade momentum in the direction of the negative skew. The skew in option pricing tells you that, effectively, if a market starts to go down there's going to be a trend. There is going to be a very strong drift.

"...The market is telling you that if we go down, we're going to go down a lot"


If the S&P starts to go down today, the market is expecting that it is going to lose twenty percent a year - that type of thing. So that's another way of filtering. The market is telling you that if we go down, we're going to go down a lot. So you say, OK, we'll go along with you, no questions.

Katy: So do you think that there is more that you need to spend more time thinking about that in today's markets as opposed to a few years ago? Is that the secret sauce, I guess we might say?

Nigol: There is more clarity gained not around the way the markets are but by the way that people trade. I don't believe that the markets are a certain way; that if you're long the S&P you make seven percent a year, and if you're long CTAs you make ten percent a year. It depends on what people are thinking and the way that people are trading.

The way to (on the run) evaluate the level of conviction and risk people are taking means asking, ͞Where is the surprise potential? So vol. compression was fifteen, twenty years ago; convexity was five, ten years ago. Today a lot of what we do is around crowding.


Effectively trade against... Whatever feels good, do the opposite. (Laughter) You can do this looking at the relationship of the between the returns of different sectors within momentum. You can look at certain markets within a sector and assume that things are going to mean revert.

Katy: So if it's uncomfortable that's a good thing.

Correct, if it's uncomfortable or... The way that we're programmed to think... How does the way we are comfortable thinking lose money? That is why we bet on that.

Today, you can hire people who are undergrads and who can run optimizations that quadruple PhD's couldn't do twenty years ago. The techniques available for optimization are lacking – they are not free. As a result the ways that people are chasing returns, and therefore reinforcing short-term bubbles and then breaking them down (how the markets de-pattern themselves), are really critical and has changed.

Our job is to find ways to provide this convexity with positive alpha at the same time by relying on what people are comfortable doing and trading against it. In all time-frames, it͛s available. You can hire ten quants, and they all tell you the same thing, and you do exactly the opposite.

Niels: Is there a risk, in some ways, that...

Someone shared this with me, and I thought it was interesting. If you look at the European part of our industry, and you look at the US part of our industry, there is no doubt that in the late 90s (I think) the Europeans, the managers were seen as being more scientific. Therefore, a lot of institutional investors preferred that over the US managers who were seen more as ex-floor traders who systematized their rules - whether it be Turtles or someone like that. But, it also meant that a lot of the European managers were recruiting from universities to emphasize this scientific approach to investing.

Of course, as we know, in the universities they teach from the same books. So, their approach to (for example) risk management is, perhaps, more similar than what we think in that they start looking very similar. I don't know, it's not a study that I've done, and it's not a right or wrong, but you're saying is, do a little bit the opposite, be the rebel. In a sense, if you recruit from the same university and they all end up doing the same thing (maybe that's not down the route yet, obviously with a few exceptions), that's a lot of money still flowing to those types of strategies.

Nigol: Sure, there are two aspects to that trade. First, universities don't teach you what you need to know; they teach you what recruiters want you to know in order to go sell their products. So, it͛s not necessarily the best knowledge available. You know how it works.

The next phase is the scientific phase where are we progressing scientifically in everyday life? Again, investing is not something that can be managed through a scientific process - absolutely not. It's a complex system where high conviction results in the pattern breaking.

So if you hire a scientist who says, ͞People who wear white shirts typically go to the beach, and people who wear black shirts are going out (whatever), you can do that in everyday life. It doesn't affect the process, but in investing, the more advanced... With science, you need to do it just to understand what people are thinking and feeling, not because it gives you an idea of what the markets are going to do. It's very different.

When everybody believes that buying the S&P after two down days is a one hundred percent trade or a ninety percent trade, the market de-patterns. Science is not very good at figuring this out unless you're looking at the science of the mind (we're getting there).

Another way for us to measure this is the level of conviction that people have in certain trading strategies. So, you evaluate the behavior of the returns of different trading strategies, and that tells you how the people trading these are feeling based on the Sharpe Ratios, and you can do certain things with that.

"...First, education is not ideal. It's intentional"


First, education is not ideal. It's intentional; it's not pure knowledge. Second, they are cyclical, and science will not understand markets. Depur Neuton, I think, was a good example of that.

So, you cannot have a purely rational mind when you're trading. You need to realize the cycles of emotions that investors are going through. At a certain stage that might change, but the frustration today...

So the people are saying, want to invest with quants, that is because they are frustrated with the macro managers who haven't done anything because central banks have made it impossible for them. That's, again, a cycle where people will run away from quants and go back to the thinkers one day.

I hope you enjoyed these insights about Smart Beta from one of the most successful short-term trading firms in the world. If you want to check out the full conversation... just click here! Also, if you enjoyed this short takeaway from a past episode of this show then you will love the free book I'm giving away right now.... it's called ͞"The Many Flavors of Trend Following" and it includes some of my best insights on this perhaps the Most Dependable and Consistent, yet Often Overlooked Investment Strategy... Get you free copy here