TTU133: Nigol Koulajian & Mike Harris of Quest Partners

[00:00:06] Nigol

Politicians today are not held accountable in the way that they used to be and they have tools to effectively shut down markets and control markets in a way that they didn’t have before. So I’m worried about how extreme the behavior of central banks can be for the sake of effectively controlling markets. They might shut down markets very easily if things don’t go their way, etc. So, I worry about the level of liquidity of markets and it’s changing very, very quickly.

[00:00:38] Intro

Imagine spending an hour with the world’s greatest traders. Imagine learning from their experiences, their successes and their failures. Imagine no more. Welcome to Top Traders Unplugged, the place where you can learn from the best hedge fund managers in the world so you can take your manager due diligence or investment career to the next level.

Before we begin today’s conversation, remember to keep two things in mind. All the discussion we will have about investment performance is about the past, and past performance does not guarantee or even infer anything about future performance. Also understand that there’s a significant risk of financial loss with all investment strategies, and you need to request and understand the specific risks from the investment manager about their product before you make investment decisions.

Here’s your host, veteran hedge fund manager, Niels Kaastrup-Larsen.

[00:01:28] Niels

Hey everyone, and welcome to another edition of Top Traders Unplugged, where today, Alan Dunne and I are joined by Nigol Koulajian, Founder and CIO of Quest Partners, and Mike Harris, who’s the president of the firm as part of our mini-series focusing on the one investment strategy that beat everything else in 2022, namely, trend following and managed futures more broadly. First off, Nigol and Mike, it is great to have you both back on the podcast. Thanks so much for joining us today. We’ve really been looking forward to our conversation. I hope you’re both doing well.

[00:02:04] Nigol

Great. Thanks, Niels.

[00:02:07] Mike

Thanks for having us.

[00:02:08] Niels

Absolutely. Now, before we dive into all of the different topics that we’re going to discuss, I would like maybe to set the stage for our conversation so that the audience knows a little bit of background to your firm. So perhaps I could ask you Nigol to share a few highlights in terms of the type of strategies you focus on and also maybe where the business stands as we have entered 2023.

[00:02:34] Nigol

Sure, Niels. Yeah. So the business is basically currently still trading futures and FX primarily. We started trading single stocks recently as well. We manage about $2.7 billion. We’re a short-term CTA, so our average days per trade is about eight, nine days per trade. There’s about 40 of us at this point. We’re mainly New York based. We’ve been around for 23 years. We tend to focus on strategies which are more positively convex, which means they benefit from vol expansions, not only from trends, and that’s quite valuable for hedge fund and equity portfolios.

[00:03:20] Niels

Yeah, absolutely. In fact, you were one of the first guests we had on the podcast back when it started in 2014 so I have followed your success very closely and congratulate you on all of that. Now, Alan and I created a list of topics that we thought would be interesting to dive into as we go along, and we’ll alternate in bringing them up. But of course, since we’re the four of us in this conversation, feel free to also pick up anything that you want to bring up at the same time. But Alan, as we normally do, why don’t you kick off with the first topic?

[00:03:59] Alan

Sure. Yeah, it’d be great to just get a sense on the investment philosophy of the firm from the sense that obviously, as you say, you’re focused on being a short-term CTA with a particular focus on strategies that are attuned to volatility expansion. So why is that? What is it about how markets operate that you think that’s a particularly unique area, a unique source of alpha in markets?

[00:04:30] Nigol

For us, it’s an interesting topic. Let me explain. Since the 2008 crisis in particular, and even before that since the 2001 equity crisis, central banks have been more and more accommodating and basically providing liquidity into markets and they’ve provided what they call the Fed put. What this has done is effectively encouraged people who are into selling volatility, or people who are into buying the dips, or people who are trying to benefit from selling insurance of one sort or another to be more aggressive in their strategies. Effectively, they’ve been protected from losses for a very, very long time.

What that has done is it’s created a market which is effectively even more mean reverting because of the success of those strategies and that has created a certain pattern in markets where the volatility compresses abnormally much more than the fundamentals in a market would, and then it creates breakouts which are more aggressive. So we’ve done research that shows that the alpha of hedge funds, the alpha of CTAs, the Sharpe ratio, is all effectively 85% correlated to convexity. Effectively, the risk in the market is no longer volatility.

Volatility is abnormally very, very low. But then the volatility itself has become extremely unpredictable. So what we do is we try to benefit from the vol extensions as opposed to assuming that the volatility is constant. The fact that the alpha and the Sharpe ratio of hedge funds is coming from effectively shorting volatility, not really going long with the market anymore, or at least the alpha is predicted by the amount of 40 volatility that a hedge fund does, effectively, they’re shorting more volatility than they think they are. They’re shorting at prices which are below real actuarial value.

We’ve been able, as a result, to generate more returns than the typical CTAs while being more short the market on average, and this is very, very attractive from a hedge fund portfolio perspective. So the real risk is not vol, it’s the tails. The tails can be measured through skew and convexity. The market is effectively selling the tails too cheaply because the central banks have effectively delayed the corrections in the market cycle, and as a result, we’re able to, even in a very strong very liquid market, create alpha to hedge funds, create alpha to CTAs. So what we do is specifically designed to be a better addition to a risk-on type of portfolio than a typical CTA.

[00:07:37] Alan

Okay. You touched on the Fed put and how that has come into the market. It sounds like a structural change, as you say, that the market participants are selling the tails too cheaply. Is that something you can identify when that started? Why is that likely to persist even if we’re into a different kind of Fed environment now? Obviously, the Fed are trying to flex their anti-inflation muscles, I guess.

The second question is, obviously, you’re very focused on the volatility argument, but you express your positions in futures as opposed to options. So why do you think that’s a better way to capture these volatility expansions and these mispricings?

[00:08:24] Nigol

Yeah. Great questions, by the way. The policy of the Fed is a by-product of central banks coming under more political influence. So effectively, the old classical equity recession or correction is no longer accepted by politicians today. There is much more short-term thinking. Another reason for that is that the economy is today much more leveraged than it’s ever been. If you look at the financial market’s size relative to the size of GDP, it’s at the highest ever. So that’s called the Buffett ratio.

The Buffett ratio, effectively, is a great measure of stock market value. Effectively, the markets that depend on the economy have become double the size of the actual economy. They reached that high not long ago. Today, they’re closer to about 1.5. Historically, that ratio is closer to 0.7.

The reason for this is more the fact that the Western economies are less competitive compared to the emerging markets. As a result, we’re running at deficits, very substantial deficits. So central banks are having to intervene more, treasuries are having to print more money, governments are running at a deficit, central banks are having to keep interest rates very low, and to do that, you can’t keep interest rates at 1% while volatility is high.

So effectively, it’s a whole chain of events, but the chain of events starts with the economies becoming no longer competitive and politicians are not accepting that going through the recession might be positive for the economy because it encourages investors to look at what are the real companies that are making money, what are the ones that are growing, as opposed to the Fed providing liquidity the way it has. It does not allow for the market to be a fair risk pricing mechanism anymore. Everybody makes money. As a company, you don’t have to have earnings, you can think of growth now in terms of the value is completely disregarded.

So all these are kind of like excesses that have come from the fact that the governments are trying to delay recessions due to short-term thinking. As we do that, the economies are becoming weaker and weaker, because we’re not focusing on being more competitive. The Fed has just made it too easy to be an economic or financial participant. So this is not something which is sustainable of course, as you know. Historically, everybody’s tried to print money as a way to smooth out economic corrections and to make it look like the economy is growing, when in real terms inflation-adjusted, it is not.

What we’ve done today, the US has been in a very, very privileged position due to the global attraction to the US dollar. We’ve been able to do this more than ever before. So this has been assumed to be a structural change. The reality, if you look at history, it’s never permanent. Historically, the way this plays out is although money can be printed and financial markets can be controlled with well-planned injections of capital, what cannot be controlled is physical commodities. What you see today is typically, physical commodities are the ones that break the control regime of central banks. Typically, it’s seen by other countries paying more for the commodities or controlling the commodities.

Effectively, you get into real wars over commodities as different empires, let’s say, America versus China fight for physical commodities, which cannot be printed. So the financial market markets you can distort within a certain limited reality. But, at the end, because you depend on the real economy, this is not sustainable and pressure on commodity prices and shortages in commodities become more prevalent. Eventually, central banks have to match the risks in physical commodities with the risk in financial markets and interest rates go up and TEs come down. That’s the normal cycle.

In terms of your second question, why do we trade futures in terms of options? So you can be short volatility by buying the dips or you can be long volatility by adding to winning positions. So you can create nonlinear risk profiles, whether it’s in correlation, whether it’s in volatility, whether it’s in market beta, by the way that you trade. Effectively, you can replicate the short option or long option. The reason we choose to trade futures versus options is that futures are much cheaper to trade, much more liquid than options, and therefore, we can trade faster and more accurately than we could with options, which are illiquid and are more inefficient.

We actually don’t trade options. So first, with the trading, you can create gamma profiles or skews or convexity. Second, futures themselves, although they’re called delta one instruments, are still markets which have a lot of convexity. If you look at physical commodities in particular where you can have shortages in the short term; let’s say you look at nat gas, if you hold short positions in January, February nat gas, your yield is about 20% (if you look at effectively the cost of carry). Now, that’s not for free. Effectively, you’re short an option, and in case there’s a shortage, there’s just no way to meet the demand and prices can double and triple.

So delta one instruments have embedded convexity in them as well. We measure the convexity that is embedded in markets, as well as the ones that we can create for our trading, as well as the ones that other market participants are creating by effectively replicating short puts on the market in order to generate alpha in this environment.

[00:15:06] Niels

I wanted to ask Mike something that both is a general observation from your decades involved in the industry and then maybe we can turn it also specifically to your approach at Quest and that is, Cliff Asness came out with this paper last year. He talked about the dual mandate of trend followers of CTAs in terms of absolute returns and in terms of quote-unquote “crisis alpha”. But then he also raised the question, have we as an industry become too focused on Sharpe?

First, I would love to hear your general view on the industry, whether there is a point there that maybe the industry has given up on some of the true traits of, say, trend following in this respect, because people or investors find it too hard to hold onto if it’s too pure, in pursuit of a higher Sharpe that makes it more palatable for investors. Of course, the argument is a little bit about, are we focusing on the line-item Sharpe or are we focusing on the portfolio Sharpe in terms of the benefit? So I’d love to hear your thoughts, Mike.

[00:16:21] Mike

Yeah. I think first off, what’s been really interesting, and you’re right, I’m one of the few CTA market participants that’s been doing this since the very beginning of my career, both as an allocator and now at several managers. But I’ve really seen this evolution of strategies where, in the early days, many CTAs had their niche. They were like Quest, a short-term manager, or they were a pure trend follower. Along the way, we started to add people who were trading more systematic macro, and then as Nigol alluded to, we’ve started trading cash equities in recent years and more of a market neutral stat arb fashion, and there’s certainly a number of players doing that as well.

Because my first role was building fund of funds, you had these allocators in fund of funds and other portfolio managers that were putting together the building blocks, if you will, to create a really interesting CTA portfolio that would have elements of absolute return and certainly that tail risk protection that CTAs have historically been able to deliver.

I think what we’ve seen over the last 20 to 30 years is that some of the larger CTAs have morphed and become multi-strats in that sense that they’re doing all of these various sub-strategies and blending them together, and as they did that, I think they were in many cases pursuing both of those goals that you referenced, the let’s have the highest Sharpe possible to be able to keep up with the other investments in the portfolio. But oh, by the way, we can’t forget that most investors are allocating to us for years like 2022 when everything else doesn’t work and we need that true hedge in the portfolio.

If I could be so bold, I think that some managers may have lost their way. We had such a long period of time between crisis years that in order to retain their allocation, I think a lot of people became a little too focused on absolute return and building the Sharpe ratio. I think that one of the reasons you saw that evolution of multi-strat type portfolios is that the more orthogonal alpha you can add to a portfolio, theoretically, the higher your Sharpe goes.

One of the things that I was really excited about when I joined Quest last year was that I don’t think that we’re in any way confused about the prioritization of providing that tail risk protection. Albeit it’s not to say that we’re not focused on absolute returns. That’s an element of what we do. But I do think that there may be some managers out there that whether it’s looking at longer term time horizons and holding positions longer, whether it’s adding in strategies like mean reversion.

You think about it when the market sells off aggressively. If you take 2020 when the pandemic first set in in March as an example, you had so many managers running mean reversion where when the market sells off, those mean reverting strategies were trying to buy the dip. For a pure short-term breakout player, like we are, trading just momentum, obviously, our strategies are going to get very short in that moment, and that’s what investors whether we’re their only CTA or whether we are their short term CTA, they expect us to be that first mover in the portfolio, the thing that helps to start hedging their risk right out of the gate.

When you’re running mean reversion and you’re loading up and buying in that dip, all you’re doing is adding to a large portion of risk that already exists in their portfolio, whether it’s from long-only equities. Let’s face it, outside of the CTA and macro world, so many traditional hedge fund strategies have a high degree of correlation to the rest of the portfolio and so when that tail risk moment happens, I think investors underestimate how much risk they have. You can imagine, if your CTA is adding to that risk instead of helping you diversify away from it, that can be a very surprising outcome.

[00:20:51] Niels

Yeah, you bring up a lot of good points, Mike, and I want to bring the goal back in a few moments. But I want to stay with you for a second longer. From recollection, you’re obviously classified as a short-term manager. There aren’t that many successful short-term managers, certainly less than five, I would say, probably only two. But from memory, I seem to recall that there is an element of trend following in the model. There is also something you call trend crowding. Can you explain a little bit about what role that plays in the portfolio, and then I’ll come back to Nigol and talk a little bit more about it in a slightly different way?

[00:21:31] Mike

Sure. You’re right. We have momentum signals in the portfolio. I would categorize them as certainly faster than what I’ve experienced across the industry. Like every manager, we have our own secret sauce or spin on our approach to momentum. We start with an understanding of momentum in general. We actually have a trend beta approach in addition to our flagship that we call QTI. One of the reasons that we’re doing that, obviously, is to help educate the industry about, in some senses, how simple trend can be. But then also, we use it as an indicator to allow us to understand, maybe broadly speaking, where other managers in the space are.

Your point about crowding is super relevant because as a short-term manager (and I’ve learned a lot in recent months from Nigol about this as I’ve been able to get more into our value adds), it’s not just the trades that you take. For us, I think the real differentiator is the trades that we don’t take. Let me explain that. Anyone can go out on Amazon and buy a book on short term breakouts, right? There are tons of them out there and it’s not that difficult. On any of the common trading platforms, you could just pull up Bollinger Bands and use that as a potential signal for breakouts.

But I think what Quest has found, in its research over the years, is that it’s those false breakouts and the cost associated of not only getting into the trade and then realizing you’re wrong and having to exit, but that they add up over time and that’s what ends up being that drag on performance of short-term managers. Thus, [this is] the reason (as you alluded to) that many have come and gone over the years, and there really aren’t that many that have the length of track record that Quest does. So I almost think of it as playing good defense is the key to our strategy.

We’re using a tremendous amount of what I would call or refer to as filters to help us figure out what trades to take and what trades to ignore. One of those filters is crowding. So, understanding what other market participants are doing, where are the crowded trades, because obviously, when they unwind, that’s going to be a pretty strong signal. So when you can marry that and you’re seeing a breakout and you know it’s a crowded trade and you know that obviously the market is probably going to go a little bit further than it would have normally, then that’s probably a trade that you want to be in.

[00:24:20] Niels

I appreciate that, Mike. I wanted to ask you, Nigol, and I don’t know if I understood it correctly, so please correct me if I’m wrong. You mentioned that you started trading individual equities. So are you seeing the program evolve more to (multi-strat is not the right word I’m looking for, but maybe it is because it does have several strategies, now it may have more instruments, etc.), but are you trying to say, “Well, we’re going to stick all of our best ideas into one strategy. We’re going to keep, as much as we possibly can, the profile that people have gotten used to in terms of what it looks like, the way it reacts, but there’ll be more bells and whistles beneath that.”

If that’s the case and you now have a few more tools in your toolbox, how do you think about deciding how much to allocate to each of these tools in the process of delivering that?

[00:25:20] Nigol

Thanks Niels. It’s a lot of questions in one. Let me try. First, let me address the crowding from multiple angles. Starting with the dimension that Sharpe might be a little bit over-emphasized. I’m going to say that Sharpe is distorting markets. The fact that people or investors are chasing Sharpe ratios is actually distorting markets. Sharpe ratio, which is heavily dependent on volatility measures, substantially underestimates tail risk. It doesn’t account for tail risk. As a matter of fact, the holy grail of hedge fund valuation (which I’m going to say is convexity) is predicted by Sharpe ratio, as well as by skew.

Suppose you don’t know how to measure skew, measure Sharpe ratio; and Sharpe ratio is about 50% to 60% correlated to skew. So effectively, you can predict the size of the loss in a hedge fund as a multiple of its volatility by its Sharpe ratio. The higher the Sharpe ratio, the bigger the drawdown as a multiple of volatility. So effectively, what investors are chasing; they’re chasing Sharpe ratio too much, they’re relying on volatility too much, and they’re relying on past returns too much. It’s effectively distorting price, distorting the returns of trading strategies. It’s creating what we call crowding.

So how does crowding come into play? Most investment managers tell their investors that they have no market impact, that they can handle much more assets than they currently have. That’s possibly not totally true. It’s possible that this is a convenient statement, but the reality is that too many people are doing the same thing. So although you’re a $10 billion long-term trend follower and there’s another 10 of you guys out there, it’s possible that you’re all doing the same thing and you’re highly correlated to one year price momentum, similar to the large smart beta providers in the industry. So crowding can easily happen.

Our experience is that markets are very crowded, which means the strategies which are transparent, that are heavily marketed by smart beta providers, substantially underperform strategies with similar timeframes and similar portfolio constructions. So there’s a huge cost to investing either in a smart beta strategy, if it’s public and if it’s heavily allocated to. What we do in crowding is create a four-dimensional matrix where we’re trying to predict where we can find cheap trends and where we can find cheap volatility. What we do, to do that, is we’re mainly trying to replicate the decision-making processes of large allocators and of large PM platforms.

So when you go to the Millenniums and the large PM platforms, they tell you that if you lose half your volatility, you go into a drawdown, they’re going to cut your allocation by 50%, you lose the full volatility, you’re actually deallocated 100%. Similarly, on the upside, you make half your volatility, you’re allocated more to, 100% of your volatility, you get more money, etc., etc. So effectively, with the platforms, because they’re trying to manage huge amounts of money, they don’t have the time and the resources to analyze traders. They’re quick to give you money, but they are also very quick to take the money away. The structures and the processes and the dynamics of that allocation-deallocation process is very, very useful in predicting future returns.

So what are the four damages of crowding? We measure crowding across first, which markets within a sector, if you want to be exposed to momentum in fixed income, where is cheap momentum? As Mike said, you can have 90% correlation to the index with positive alpha just trading moving averages and beating the market 100% of the time. But in reality, is there a way to say that trend within fixed income is going to be better in bonds versus US Treasuries? Crowding allows you to do that.

So one dimension is you can choose which market, within an asset class, you should be trading. Second dimension is which asset class should you be exposed to? Should you be exposed to more fixed income, trend in fixed income, or trend in commodities, or trend in FX, or trend in equities? That’s the second dimension. The third dimension is, which timeframe is going to be more valuable? Is it going to be the very, very long term; one-year momentum, two-year momentum, three-year momentum, very long-term moving averages or two, three-day trends? The third dimension is this timeframe. So you can apply exactly the same models to different timeframes.

The fourth dimension is what type of trading strategy should you use? Should you use moving average crossovers, exponential moving average crossovers, price momentum out of AQR, channel breakouts like the turtles, volatility breakouts like the Toby Crabels? That’s the fourth dimension. So there’s different trading mechanisms or Bollinger Bands for example. One is which market within asset class; second, which sector; third, which timeframe; and fourth, which momentum methodology should you be using?

So if you look at a matrix of all these things, you can see how things are performing and you can predict what allocators are going to chase first. Second, you can predict what very intelligent but not street-smart researchers are going to optimize in their machine learning algorithms.

So when we think of learning, when we think of research… There’s retail thinking. Retail thinking is 200-day versus 10-day single moving average crossover. That’s retail trend following. Then you have the smart beta providers who have diverse versions of that. Then you have smart hedge funds, the big math brains and machine learning and the quants and all the data and all that. Those guys are just typically optimizing over what has worked historically.

Another order of thinking about that is (what we try to do) to try to predict, based on the current data, on the recent data, what are people going to optimize towards; both researchers with their classical optimizing mechanisms as well as investors. So crowding is a way to predict not what the current market regime does and what is currently working, but based on what has worked, what is going to work in the future.

Market is about supply and demand. Math is useful, but you cannot think with math. You can express with math, but thinking has to happen one level beyond the math, one level beyond the optimization. What is going to be the next market regime based on what is currently working now?

Making the assumption that what is working now is going to continue to work is not correct. As a matter of fact, it’s counterproductive. Which is why, if you interview 100 researchers with Sharpe ratios of 1, 2 and 3 and 5 these days, and you give them money, less than one out of 100 will give you that type of Sharpe ratio in real life. This is not because they’ve over-optimized, but because the market is going to chase those parameters and the strategy is going to experience a lot of slippage and more false signals in the future. So the risk pricing of trading strategies is similar to the risk pricing of markets supply demand. So what we do with crowding is actually try to address this as a source of alpha. Sorry, for the long answer.

[00:33:53] Niels

Just to clarify the question, because I think that’s a great explanation. Very interesting insight, Nigol. So, does that mean that you will have a dynamic allocation to various (let me just call it) types of momentum strategies in order to deliver the total profile that you want and do you then consider the program moving more into a multi-strat but with that clear focus, so not really multi-strat the way a lot of people think about it?

[00:34:28] Nigol

Definitely, there’s dynamic allocation decisions to momentum across those four timeframes, and they’re very useful, versus the smart beta strategy, 100%. In terms of how we are evolving, we’re often 1% of the volume in the big markets like US Treasuries, energy, gold, even foreign exchange. So even at close to $3 billion, because of the short-term nature of our trading, we’re close to capacity. So what are we doing to expand our capacity? We’re not going towards multi-strat the way other people have done it.

So effectively, if you take strategies which are negatively skewed, at low volatility, have zero correlation, as you know (as well as expressed), typically strategies that have negatively skewed correlations to each other are going to increase when markets go down. Just quickly, negative skew gives you a lot of alpha, gives you a lot of Sharpe ratio, gives you typically low correlation, gives you low correlation to the S&P and to other assets. But it also predicts larger drawdowns as a multiple of volatility and it predicts that you’re going to lose money quite substantially when the market is going down although the risk statistics told you that you weren’t.

So multi-strat is typically a thing which is taking a bunch of negatively skewed strategies (which at low volatility have low correlation), and putting them together to achieve something quite incredible, which historically hasn’t been achieved. Now, the danger of these things is that as tails happen less often, but they’re bigger than they used to be, there’s a tremendous risk to those large multi-strat strategies. So even in the COVID crisis, some of the best bonds and best Sharpe ratios were close to getting wiped out if it wasn’t for Fed interventions, similar in 2008, similar in 2001.

So, the way we’re diversifying, we try to take the same equity curves that these multi-strats are drawing, but we try to generate positive skew out of them. So we try to time in and out of them in the periods where there’s positive convexity. Effectively, we don’t want returns with negative skew. We want returns and Sharpe ratio. It won’t be as high as a multi-strat, but it will be most importantly with positive skew, which means whether the economy goes towards the real world, which is effectively China and the US fighting over physical commodities, and volatility becomes more in line with the fundamentals, we want to be able to make more money, not less.

So we want a program which is stable over decades, not only over the short term. So we’re building a portfolio with a multi-strat. We want to be exposed to as many sources of alpha as possible but we want to do it with a positive skew angle. So it’s quite different how the portfolio ends up being built.

[00:37:37] Alan

Maybe just to pick up on that a bit more, just so I understand. The concept of crowding often is applied to positions to avoid, “This trade is a crowded trade,” but are you looking at also trying to pick up on where these multi-strats… What types of trades have been successful and are likely to attract capital, and then participate in those, as well as identifying trades that are excessively crowded? Is it getting in and getting out before the crowd gets out of the trade?

[00:38:14] Nigol

Absolutely. But in this case we’re not timing which trade, we’re not deciding which market is crowded, what are the positions of hedge funds in a specific security or futures market? That’s not what we’re doing. We’re actually timing the entry and exit into the equity curves of the different strategies that multi-strats use. So we’re not selecting specific trades but we’re saying, for example, in this environment, you should be trading channel breakouts, not moving averages, or you should be trading more long vol rather than classical beta one trend following.

So it’s more the nuances within active strategies rather than individual trades. So we’re saying expose yourself to very short-term trend following in fixed income and expose yourself to very long-term trend following in dollar-yen because of the way these things have performed relative to each other.

[00:39:15] Alan

This is a very adaptive approach. Speaking to a lot of managers, everybody is highlighting the benefit of being adaptive. In your experience, how persistent are these environments do you find? If it tends to be good to be trading bonds relatively fast or whatever, does that last for weeks or is it more days? Is the portfolio constantly changing how it trades different markets?

[00:39:45] Nigol

This type of factor which is used as a filter to decide when to trade momentum is valid in all timeframes. The shorter you go, the more powerful it becomes, the more market participants are likely to create market impact by, on one side trading ahead of them; on another side, providing liquidity to them when they overdo the trading or they become desperate for liquidity. Short-term, you’re going to have more market impact. So we try to apply that in all timeframes, not only long-term. As a matter of fact, as the firm has grown, we’ve actually gone more short-term rather than dilute what we do by becoming more long-term to reduce market impact.

So the firm has recently invested in a couple of hires from high-frequency firms where we’re very carefully learning how to test the market. So it’s not a question of theoretically or academically having a formula that predicts market impact. It’s a question that we’re trading already. We’re 1% of volumes of the biggest markets in the world. How is the market responding to what we do? How can we signal to the market that there’s a large player in the market and how can we hide that there’s a large player trading in the market? So we’re trading anyway. We’re not paying transaction costs. The transaction costs are already a sum cost.

There are a lot of things you can do in the way you execute to become more price sensitive, which CTAs are not. There is no relative value concept used (I’m going to say) in most CTAs based on my interviewing New York researchers, etc., etc. So we’re saying go as short-term as possible. The more short-term you go, the more diversification potential you have, the more quirks you can find, the more complexity it takes, and the more skill it takes as well. But we’re not afraid of that. So there’s a lot to be done, basically.

[00:41:46] Alan

Yeah, it sounds a lot like an adaptive learning technique. You haven’t used the term machine learning at all? I’m wondering, do you see it as machine learning or a form of learning? How would you characterize it?

[00:42:01] Nigol

Machine learning is not black and white. I think that going from the type of optimizations that we used to do 20 years ago, in TradeStation, versus the oddly more complex, different learning methodologies that are available today is a gray line. Mathematical optimization is a commodity. You can buy it for free. The understanding of what you’re doing, even in a static environment, is something which is a commodity. It costs almost nothing to hire some of the best machine learning experts. What’s hard to understand is the impact on the market of what the market is learning.

I don’t mention machine learning because I don’t spend time thinking about it. What the market provides as a commodity is already overkill compared to the type of optimization that financial markets allow you to make without artificially convincing yourself of things that are highly, highly unstable. So conviction, when it comes with a lot of data, is actually problematic in financial markets because of supply-demand equations. You’re not measuring consumer behavior, you’re measuring something that the day you measure it, it’s going to flip on you because other people measured it and allocated to the same thing that you saw, to the same factor that you saw.

So machine learning is there, but I’m not proud of it. I don’t think it’s a sign of superiority. I think it’s a sign of danger, that you’re fooling yourself to believe something out of data, in a particular environment which is highly unstable and susceptible to false confidence due to a lot of data giving you a sense of stability when it’s exactly the opposite.

So the fact that we are focusing on tail as a measure of risk as opposed to volatility will eliminate 90% of the optimizations that super smart researchers will typically do. So machine learning is not like this achievement. It’s, I’d say, a commodity.

[00:44:15] Alan

Thinking about research and the types of strategies that you’ve come up with and the whole research approach, you mentioned TradeStation 20 years ago to where we are now. What’s currently the big source of opportunity for you? Is it more datasets or alternative data or better computing power or getting to be able to trade faster? Where do you see as being the source that gives you opportunities as you look ahead?

[00:44:47] Nigol

With the advent of better optimization tools, the Sharpe ratio that investors are achieving is not getting higher. So I just don’t think this is like we’re getting technologically better and we can do things that we couldn’t do before. You’re still limited by the supply demand and risk equation of money which is real world. This is not something theoretical. How you can compound your money and the risk of blowing up are the real world.

So the way we’re expanding is we are doing everything that we can do; so alternative data sets, new markets, but more importantly, we’re doing the things that other people are not doing. So I would say that there’s too much focus today on delta one markets. You could trade 20-day channel breakouts and be quite crowded, right? As a matter of fact, some strategies are so crowded that you’re better off shorting them long-term than long in them. So, if you want to add value, do what you’re doing, but also short the crowded trend following strategies such as the 20-day channel breakouts.

What we do, for example, is instead of trading based on price breakouts which are very crowded, you can take something which is correlated to the market that you’re trading such as CPR, GDP growth, unemployment, for example, and use those factors (so illiquid indicators) to trade liquid markets. Why is it so hard to trade the S&P using momentum strategies? Last year, the S&P was down 18%, 17%. It was very hard for long-term trend followers to make money on equities. So the type of noise that markets start to generate when too many people are trading the same way becomes very counterproductive to returns although there are big moves.

A similar thing to energies in the past years. When people start making money, they allocate a lot of money to trend following in a sector, then the trends become very noisy. So in markets, you want to trade things entering and exiting at times that other people are not. You can take a market and remove a principle component of trend following off it to create diverse entry points. So remove the basket of trend following positions, use that as your index, remove it from dollar-yen, and then trade that differential because that differential will be much more diversified than what other market participants do.

So I’m not saying trade a purely neutral spread, but remove some of the volatility. Let’s say you remove 20% of the volatility of dollar-yen by using a basket of trend following or, instead of trading dollar-yen, use a trade balance indicator to trade dollar-yen. So that’s another way of diversifying your entries. Third, you can trade the relative value to dollar-yen. You can start using fundamentals in a way which is positively skewed to trade dollar-yen. So there’s all these different approaches to create uncorrelated return streams.

Think of macro investors, they’re not trading breakouts only. They correlate to trends, but they’re using a bunch of uncorrelated indicators which are correlated to the liquid markets to trade the liquid markets. So they end up generating higher Sharpe ratios than the typical CTAs. So those are the ways we’re expanding what we do.

[00:48:35] Niels

I want to go in a slightly different direction with you, Mike. Because back when I started in this industry in the ’80s, there was a D word that when that came up, people were really frightened, and it was derivatives. In our industry, there’s another D word, it’s discretion. I wanted to ask you, based on the fact that you’ve obviously been in the industry for a long time but you’ve also covered a lot of different roles on the manager side, we had someone write in to us as we were releasing the first episodes of this series asking whether we could bring up the use or not use of discretion. So maybe I can just throw it out there to you and ask you, how much discretion is too much? Then you can run with it from there.

[00:49:24] Mike

Yeah. I would agree that the D word of discretion comes up in a lot of conversations, particularly with investors. I think the reason is because 10, 20 years ago, there seemed to be really two crowds. There were the pure, traditional, fundamental, discretionary investors and traders, who at that time were probably the majority. Then you had this small pocket of quants in our world who we had to pound the table to reinforce the notion that we are in fact 100% systematic in everything that we do, and if we’re not, the minute that we stray from that, how can you ever believe a backtest? How can you ever look at a track record of returns over 10, 20 years and believe that we will produce something similar in the future? Just the very notion of turnover and succession plans.

One of the things that I’m most proud of in our industry is that there are quant firms out there that have been around like Quest, 20 years, 30 years, 40 years, some approaching 50 years, and that’s not the same person running those firms. But in many cases, it’s the same family of models that are being used. If we were a discretionary shop or discretionary industry, when that person that has that secret sauce in their brain decides to hang up their cleats and retire from the industry, in many cases, those firms either close or people take their money and they get deallocated as a result.

What’s really interesting, I think, in recent years is this term quantamental. So there are so many people, particularly I think, coming more from the fundamental and discretionary side, that have realized the power and the importance of data. As a result, our industry, the cost that we pay for said data has gone up exponentially for a lot of reasons. Number one, because there are more people using it and more people seeing the value of it. Number two, because many of the people who had data that we were using, either cheaply or for free, realized the value of that data and started monetizing it.

We’re not here to get into a discussion about exchanges and how they become for-profit entities and how they’ve been raising data costs, obviously, exponentially over the years. But that term quantamental, I think, may have come from several places, but I saw it start to pop up really in the traditionary, global macro, discretionary shops where they would hire PMs, and that PM may bring a quant onto the team and a data scientist, and they would create models, and then those models would throw off signals that then the PM could then use their discretion on. “Yes, this fits with my macro view, I’m going to go put that trade on.” But they would, in many cases, decide how to size it, decide how long to hold, when to exit, which all of those things are the opposite of what systematic traders do.

I think that in many cases, some of those quantamental investors have had success. So now, instead of just having quant and discretionary, we have three separate camps. We’re interviewing a ton of researchers and portfolio managers, and so many people that come, and when I look at their resume, look like a pure systematic quant, but when they walk me through their model, or how they’ve built models in the past at other shops, there is a good amount of discretion that’s happening. That’s an approach. I think some people are having success with it, but that’s not the system that we’ve built at Quest.

[00:53:28] Niels

I think that’s great to get that view. The next question I had is, I’ll start with you, Mike but I think also Nigol might have something to say about it. Just to spend a little bit of time on it, not too much. It’s this thing about CTA replication. Now, of course, as you’ve mentioned, Quest has, for many years, had its own replication model and of course, it’s a real model, so to speak. It has models that generate the signals, etc., etc. Now we have this new breed of replication where they do some kind of regression looking at daily returns of the index of the largest managers to essentially guess what exposures they have on and then they try to mimic that, etc.

I wanted to ask you, Mike, from your point of view, what do you think? What do you think about replication in general? I’d love to ask maybe Nigol, from a more quant point of view, what are the risks that you could see when using this type of regression analysis rather than actual models to do replication? Because that’s way above my math grade. So Mike, in general, what do you think about CTA replication knowing full well that you have a program like that, but of course, that was there before you joined?

[00:54:52] Mike

So when I started in the ’90s allocating to CTAs, we had many trend followers in my portfolio and we were paying 2 and 20 for that exposure. Fast forward 20, 30 years later and now we have this term cheap trend because many managers realize that, with the longer term lookbacks in particular, they could manage billions upon billions of assets. There was a real scale to it because it was already running, it was already automated, so they could then start to offer it at a lower price. The first thing that disappeared, and one can make the case that by selling trend to the retail community and packaging that up in many cases via 40 Act vehicle, the first thing that disappeared was the performance fee for regulatory reasons.

Then what we saw, over the last 10 years, is some of the larger (particularly public) pension funds have gotten into the space, and even the management fee has started to come down, as managers were competing with each other in order to try to win those very, very large, (in some cases) multibillion-dollar tickets. What’s really interesting is that alongside what I’ve also witnessed, on the investor side, some of the world’s largest sovereign wealth funds, even some of the world’s largest family offices have now also moved into the space of, “Forget getting trend for cheap from a manager, I’m just going to go out and hire professionals from the industry to build it for us and we’ll run it ourselves at an even lower cost.”

Now, what’s interesting about that is that I’ve seen a number of them actually decide not to do that after a period of time. I think, in large part, that was because of the operational nature of running a CTA, the fact that you’re trading 24 hours a day, the fact that data doesn’t always come in clean, that there’s a lot of work behind the scenes in order to keep that trend following engine running. So, it’s interesting that we’ve gone on this path, and yet I think people still see the value in employing professional managers who can take the basics of momentum and then put their unique spin on it to create a return stream that maybe they either A, can’t replicate or B, can’t run themselves.

[00:57:22] Niels

Yeah, absolutely. Now, Nigol, in terms of the math in all of this, what’s the risk that people may not be aware of when you start replicating managers by just trying to do some kind of analysis, regression analysis based on daily data, but without actually having a real model risk management framework around it?

[00:57:43] Nigol

So the longer term the manager is in his trading strategy, the more confident that you can be that your math is going to be accurate in replicating him. I would say that the math is very powerful in replicating long-term managers. So I would say the risk is minimal. The more short-term you go, the more filtering there is, the harder it becomes to replicate. So if you’re doing five-day channel breakouts, for example, but you’re doing it under certain conditions that kick in and out three, four times a year, you can replicate the five day channel breakout, but it’s harder to know when a new condition kicks in.

With long-term managers, you don’t have that risk as much. So the math is very, very accurate. In today’s world, also you can interpolate right. You can create your own replicators and then create something your replicators can be close enough to the manager, and then you can analyze a lot of other things based on the very high-frequency data that your old replicator is getting you of the manager replication. So you might have monthly returns for a manager, but then off your application you can get daily returns, and then you can learn, over time, other things as well. So I’d say math-wise, the risk is very low. Very, very accurate.

For me, the risk of the smart beta stuff is not in the math. The risk is in allocating to large cap versus small cap. It’s the risk of allocating to the S&P 500 relative to small cap. Why do small caps outperform the S&P 500? Because too many people are investing in the S&P 500. Same thing in the CTA space. I would say that hiring the Wall Street Journal, having the monkeys select stock randomly is better than investing in the S&P 500. So it’s the same thing in the CTA space. Small cap is better than large cap. Random is not better than the replicator, but random also means it’s small, and that’s valuable.

So investing in replicators is very dangerous. You can replicate the large manager and have transparent models and see the specific slippage increase as the assets increase in the replicator and then the slippage comes down when the assets go back down very, very precisely.

[01:00:13] Niels

Alan, before you jump in, I just want to follow up with one question because you kind of already alluded to it. You talk about small cap, large cap in the manager space. We’ve talked about capacity. You are in the shorter term space. I think a few years ago, I saw you make reference to the dangers of becoming too big when you are a short-term manager. Where do you think it’s realistic to go to, as a short-term manager nowadays, given the instruments we can trade, the liquidity, etc., etc.?

How are you thinking about that? Obviously, you mentioned it earlier today that single stocks are one way for you to expand the capacity. But is there a limit out there that you already think about where you say, yeah, if we’re going to keep our profile, we want to have this average duration of our trades, this is kind of where we probably will have to close or soft close.

[01:01:14] Nigol

I think in terms of maintaining the quality of what we do and taking the next small step, I don’t have a rule for the firm in terms of asset size. Let me give you a better answer, which comes from somebody who was much smarter than I am, who runs a very, very large firm. He says that he always laughs at allocators who ask him like, “What is your capacity?” He says, “I can go and buy every hedge fund out there. My capacity is infinite.”

So capacity really is infinite from that perspective. But for us as a firm, based on our resources and our capabilities and the depth of our management, we’re trying to enter new areas with a very, very strict filter, which is we want returns which are positively skewed. Size is not a goal, so I don’t think much about your question specifically. But I think definitely there’s a lot of leeway based on the resources that we have today to create capacity slowly. The speed of growth of assets is attractive to a business, but it shouldn’t be that effective because it creates long-term instability. That’s how I think of it.

[01:02:26] Alan

I just wanted to pick up on a couple of points raised. You mentioned liquidity, you mentioned platforms. March 2020, the system reached a breaking point really, and then I guess what we’re seeing is maybe an evolution in the market microstructure in terms of the participants and the size of assets being held. So what are your thoughts, firstly, on liquidity as a short-term manager? Are there issues there from your perspective or are the markets still deep enough?

Secondly, that ongoing change in the market microstructure, I guess you’re attuned to that, and the crowding and the opportunities that might be developing. But are there other ones like we’re hearing a lot at the moment, more use of zero days to expiry options, things like that, more retail participation in the options markets? Are they sources of alpha that you’re trying to capture?

[01:03:41] Nigol

We don’t try to use options. As a matter of fact, we don’t choose implied volatility data whatsoever in all of our models, although we’re very convexity focused, because the delta on the markets have more precise and more short-term information than options.

[01:03:59] Alan

I’m getting more the impact of those participants on the delta one, the futures markets, if at all.

[01:04:09] Nigol

It’s hard enough to isolate, in the futures market, the effect of the retail participants relative to what the Citadels provide hedge funds in terms of the way retail is trading. So we really haven’t focused on that much actually.

[01:04:32] Alan

And then the liquidity conditions more generally?

[01:04:36] Nigol

So liquidity, think of it this way. The high frequency firms, the ones that are successful, are so profitable; are generating so much cash, that they’re able to get much smarter very, very quickly compared to the typical hedge fund. As a result, the intelligence that they have and the dynamism in their pricing of markets is way more advanced than a typical hedge fund can handle. So what I’m saying is effectively, liquidity is much more dynamic than it used to be when it was the banks making markets. So these high-frequency firms can provide liquidity when nothing is going on and then they can completely pull the liquidity out of the market when things are not going well for them.

What that does is creates more convexity. Effectively, it creates a false sense of security for the large managers to think that you have liquidity and it will be there when you need it. The reality is that the liquidity is drying up very quickly when markets are going down, much more than before. Within that, there’s much more intelligence, which means the intermediate-term size hedge fund or the large hedge fund is more easily picked up by these high frequency firms because it’s very hard to hide your market impact as size grows, compared to the short.

So you’re getting worse and worse as you grow by definition, because you’re harder to hide as the elephant in the room. The small guys are so profitable that they can get very, very smart quite quickly and be very, very adaptive. So I’d say it’s a very dangerous situation. The profits of the high frequency firms and of the market makers is not visible to the large guys, but we’re in touch with some and it’s quite impressive what they’re able to achieve not only in terms of Sharpe ratios, but in terms of the net dollar sells of gains. All that tells you liquidity is very dynamic and unpredictable, and more and more so in ways that are very harmful to market participants.

[01:06:53] Mike

If I could just add, Nigol mentioned earlier that we have recently hired some experts in the high-frequency trading space. Just in the first few months, it’s been eye opening. The first thing that occurred to me, when I started interviewing a lot of these folks, is that there’s two camps in the HFT world. There are those (and there’s only a handful) that have made so much, in recent years, that they’ve been able to reinvest in the business in a way where effectively it’s hard for anyone to compete with them. They are effectively playing that arms race that you hear about and you read about.

Then there’s a whole secondary group of high-frequency traders who have almost waved the technology white flag and said, “I can’t compete with you on speed, but I’m going to compete with you on my understanding of the market microstructure.” These are the people that we’ve just hired, where their understanding of the participants in the market and how everyone is signaling their intentions in the market, in a way that can be picked up from a pattern standpoint, is not only helping us to think about how we execute our order flow and how we make sure that we don’t walk into any of those bear traps that maybe are being set for us in the marketplace, but also with a greater understanding of the market microstructure and how markets are moving, this idea of next tick prediction.

When you’re a market maker, you’re always trying to figure out how to skew your price so that you’re not the person that gets adversely selected. So they develop these very, very fast short-term alphas that tell them, we think the next tick is up, we think it’s down or we think it’s neutral, enables them to stay ahead of the general market. As I said, this is not only helping us to think about smarter ways to execute, but also can be fed back into some of our models to help us think about that we’re not trading maybe sub-second like they are, we’re holding, as Nigol said, for a couple of days, but thinking about as markets are breaking out, what are the additional signals that can come from that market microstructure that can help, once again, filter out the good breakouts from the bad ones.

[01:09:16] Niels

Great stuff. Now let me round off with a question that we normally ask. But since there are two of you, I have to come up with two questions, but they will be very similar. So you’re going to get the heads up here, Nigol, because I’m going to start with Mike. I’ll give you 10 seconds to think about this Mike while I confirm what Nigol will then be asked. But I’d love to hear from you, Mike, what’s the one thing you hear about trend following that you disagree with the most, and then I’ll turn to you Nigol and ask, what’s the one thing you hear about short-term trading that you disagree with the most?

[01:09:49] Mike

Well, I’ve already alluded to it, but I’ll maybe go into a little bit more detail which is I hear that trend following is easy. Yes, there are many books out there written about it, there are very simple ways. On a Bloomberg terminal, it takes you about two seconds to pull a short-term and long-term moving average and to see where they cross. But there’s so much work; and I’ve spent 25 years of my life trying to get into the minutiae of the quality of the data, the types of strategies you’re using, the people that you hire, and having a differentiated group of thinkers so that you’re not getting industry group think, the way that you execute those orders, the time horizons you embrace, the markets that you put into the portfolio.

We didn’t talk about it today but alternative market CTAs has been a phenomenon in recent years. As Nigol alluded to, we’re starting to add new markets to the portfolio. In a sense, we can thank those alt market CTAs for creating more liquidity in those markets, because as a short-term trader, it would have been more difficult to be a first mover in that space. We’re even doing research on crowding in some of those alternative markets and that might be an interesting path for us to go down. I’ve only teased out a handful of some of the things that you have to be focused on, but as I alluded to, many people have tried to either start a CTA or to replicate a CTA and I think that they get far enough down the path that they realize that it’s way more complex than most people give it credit.

[01:11:33] Niels

Couldn’t agree more. Nigol, what are your thoughts about things that you hear people talk about when they talk about short-term trading that you really disagree with? You can’t say the same answer, because the same answer could apply. But you’re not allowed to say that.

[01:11:49] Nigol

No, I won’t give the same answer. Don’t worry. Thanks for the heads up by the way. Listen, the thing that I hear the most about short term CTAs is that they’re less reliable and harder to understand than long term CTAs, and therefore, as you see, they’re very, very substantially underrepresented in hedge fund portfolios and CTA portfolios. That’s not my experience.

I think that being short term, if done with the right discipline and with the right intellectual integrity, it’s a much more reliable approach than long-term CTAs. I think long-term CTAs have become unpredictable due to the skew of the market, and their models are not fast enough to be able to adjust to the volatility changes that we’re experiencing in the world today. So I’d say that short term CTAs are more reliable, more valuable than the long-term ones by far.

[01:12:46] Niels

Okay. Very interesting. The final question I’ll give to you, Nigol, and that’s just when you look into 2023, what are you most excited about and is there anything that you’re concerned about when you look at wherever you want to go?

[01:13:03] Nigol

The excitement is that the 20-year regime where central banks would print money and, effectively, governments could overspend without any impact on inflation, that’s gone. So I think we’ve entered the regime where physical commodity inflation is here, it’s here to stay. Effectively, it means that we’ve met our match in the world in terms of people who are willing to pay up for things. Inflation is very, very hard to reverse, especially if you’re running budget deficits the way we are. So I believe that inflation is here to stay and that will bring up a lot of opportunities. That’s what I’m excited about.

What I’m worried about is politicians today are not held accountable in the way that they used to be and they have tools to effectively shut down markets and control markets in ways that they didn’t have before. So I’m worried about how extreme the behavior of central banks can be for the sake of effectively controlling markets. They might shut down markets very easily if things don’t go their way, etc., etc. So I worry about the level of liquidity of markets and it’s changing very, very quickly.

[01:14:14] Niels

I think those are two very valid points. This was a great way to wrap up our conversation. It’s been fascinating. It’s been fun. Nigol and Mike, thank you so much for being on the podcast and for sharing your thoughts and insights with us. We hope of course, we can do this sometime in the future again. To all of you listening today, I hope you were able to take something away from today’s conversation onto your own investment journey. If you did, please share these episodes with your friends and colleagues.

From Alan and me, thanks ever so much for listening. We look forward to being back with you on the next episode of Top Traders Unplugged as we continue our deep dive into the CTA industry. In the meantime, go check out the show notes for this episode, as well as all the other resources you can find on our website. Of course, not least, take care of yourself and take care of each other.

[01:15:01] Ending

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