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The Power of Momentum Signals

The Power of Momentum Signals

This summary is written by Rich based on a conversation in our CTA series between Nigol Koulajian, Founder and CIO of Quest Partners, and Mike Harris, President of Quest Partners, and  the podcast hosts, Niels and Alan.

About Quest Partners

Quest Partners is a short-term CTA that focuses on strategies that are positively convex and benefit from vol expansions, not just from trends. 

Positive convexity is a financial term that refers to a nonlinear relationship between an asset's price and its underlying risk factor. Specifically, an asset is said to have positive convexity if its return increases at an increasing rate as its underlying risk factor increases. This means that the asset's returns tend to be higher when there is greater volatility in the market, which can make it an attractive addition to a portfolio, especially in times of market stress. In the context of the discussion, Quest Partners focuses on strategies that are positively convex, which means they benefit from vol expansions, not only from trends.

The firm manages about $2.7 billion, and they trade primarily in futures and FX, with recent ventures into single stocks. 

The team is mainly based in New York and has been operating for 23 years. 

The conversation touched on the investment philosophy of Quest Partners, which involves benefiting from vol extensions, which they believe are a unique source of alpha in markets due to central banks' accommodations. The team's strategies are designed to be a better addition to a risk-on portfolio than a typical CTA. 

Alan asked Nigol about the persistence of the Fed put in the market, and Nigol explained that the policy of the Fed is a by-product of central banks coming under more political influence, and the economies becoming less competitive compared to the emerging markets. 

The Western economies are running at deficits, and central banks have to intervene more to keep interest rates very low. This creates excesses in the market that delay recessions due to short-term thinking, and economies are becoming weaker and weaker as they are not focusing on being more competitive. 

This is not sustainable, and historically, physical commodities break the control regime of central banks. 

Nigol also explained why they trade futures instead of options, which is because futures are much cheaper to trade, much more liquid than options, and therefore, they can trade faster and more accurately. Futures themselves have embedded convexity in them, and they measure the convexity that is embedded in markets, as well as the ones that they can create for their trading.

The Dual Mandate of Absolute Returns and Crisis Alpha and the Role of Sharpe

Niels Kaastrup-Larsen and Mike discussed a paper by Cliff Asness, who argues that CTAs have a dual mandate of achieving absolute returns and providing crisis alpha, and questions whether the industry has become too focused on Sharpe ratios. 

Mike noted that he has observed an evolution in CTA strategies over the years, with some managers becoming multi-strategy and pursuing both goals of high Sharpe ratios and tail risk protection. However, he believes that some managers may have lost sight of the importance of providing tail risk protection, and that they became too focused on absolute returns to retain allocations during long periods between crises. 

Mike highlighted the importance of being a first mover in the portfolio during tail risk events, and notes that mean reversion strategies can actually increase risk instead of diversifying it away. 

Overall, Mike believes that Quest, which employs a short-term breakout strategy, is focused on providing tail risk protection and prioritizing it over absolute returns.

The Focus on Short-Term Momentum

Quest Partners is considered a short-term manager who uses momentum signals in the portfolio. 

Mike stated that the real differentiator for this approach is not just the trades they take, but the trades they don’t take. This is because false breakouts and the cost of getting into and exiting these trades can add up over time and drag on performance. 

Thus, Mike emphasized the importance of playing good defence in the strategy. 

To help figure out which trades to take and ignore, they use a tremendous amount of filters, one of which is trend crowding. By understanding where the crowded trades are and when they can unwind, it can provide a strong signal for the market leading to a potentially profitable trade. 

Crowding and its Impact on Markets

Nigol Koulajian discussed the concept of crowding and its impact on markets. He believes that Sharpe ratios are over-emphasized and that investors rely too much on volatility and past returns, which distorts prices and the returns of trading strategies. 

Crowding can occur when too many investors are doing the same thing, resulting in strategies underperforming. 

To combat this, the firm uses a four-dimensional matrix to predict where cheap trends and cheap volatility can be found. They analyse where allocators are putting their money and what researchers are optimizing for. 

The four dimensions of crowding include: which markets within a sector to be exposed to, which asset class to focus on, which timeframe to use, and which momentum methodology to employ. By using this approach, they can predict what allocators and researchers will chase and find sources of alpha.

  1. Which markets within a sector: This dimension refers to choosing which markets within a sector to be exposed to. For example, if you want to be exposed to momentum in fixed income, this dimension would help you determine whether trend following is more favourable in bonds versus US Treasuries.
  2. Which asset class: This dimension refers to choosing which asset classes to be exposed to. For instance, you might want to determine whether trend following is more favorable in fixed income, commodities, FX, or equities.
  3. Which timeframe: This dimension refers to determining which timeframe is most valuable for trend following. For instance, you might choose to apply the same models to different timeframes such as very long-term moving averages or short-term two or three-day trends.
  4. Which momentum methodology: This dimension refers to choosing which momentum methodology to use. For example, you might choose between moving average crossovers, exponential moving average crossovers, price momentum out of AQR, channel breakouts like the Turtles, volatility breakouts like the Toby Crabel, or Bollinger Bands.

By considering all these four dimensions, you can predict where to find cheap trends and cheap volatility. Moreover, it allows you to identify which markets and asset classes have the best potential to perform. Finally, it can also help you anticipate what allocators and researchers are likely to chase in the future, based on current data and recent trends.

Niels asked Nigol about the dynamic allocation to various types of momentum strategies, wondering whether they will be moving towards multi-strats. Nigol responded that they are close to capacity at $3 billion, and that they are not moving towards multi-strats the way other people have done it. He explained that they are building a portfolio with multi-strats, but with a positive skew angle. They want to be exposed to as many sources of alpha as possible, but they want to do it in a stable manner over decades.

Alan then asked about the concept of crowding and whether they are trying to pick up on where multi-strats have been successful and are likely to attract capital. Nigol responded that they are timing the entry and exit into the equity curves of the different strategies that multi-strats use, rather than selecting specific trades. They are saying, for example, that 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.

Alan then asked about the persistence of these environments, to which Nigol responded that 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. As the firm has grown, they have gone more short-term rather than diluting what they do by becoming more long-term to reduce market impact. They have invested in a couple of hires from high-frequency firms to learn how to test the market, become more price sensitive, and find more quirks.

Machine Learning

Alan asked Nigol about his approach to machine learning. Nigol responded by stating that machine learning is not a black and white issue and that there is a gray area between the type of optimizations used 20 years ago and the more complex learning methodologies available today. 

Nigol doesn't spend time thinking about machine learning, even though it's available, because financial markets are highly unstable and susceptible to false confidence due to a lot of data. He believes that focusing on tail as a measure of risk is more important than volatility and that using machine learning to analyse financial markets can be dangerous because it can give a false sense of stability. Nigol thinks that machine learning is a commodity, not an achievement.

Nigol sayed that his focus on tail as a measure of risk, as opposed to volatility, will eliminate 90% of the optimizations that super smart researchers typically do. Therefore, he sees machine learning as a commodity rather than an achievement or a sign of superiority. 

Unique Research Opportunities

Nigol Koulajian explained that the big source of opportunity for his firm is doing what other people are not doing. 

While many investors are focusing on delta one markets, his firm is using illiquid indicators to trade liquid markets. 

When Nigol Koulajian talked about "delta one markets," he is referring to highly liquid financial instruments, such as exchange-traded funds (ETFs), index futures, and single stock futures, whose price movements closely track the underlying asset. These markets are highly efficient and therefore highly competitive, meaning that there are many market participants competing for profits and driving up transaction costs.

Instead of trading these highly competitive markets, Quest uses "illiquid indicators" to trade liquid markets. Illiquid indicators are signals or data sets that are less commonly used by other market participants, and therefore less likely to be already priced into the market. By using these less common indicators, Quest hopes to gain an edge and generate uncorrelated returns to those generated by other market participants. This approach requires a deep understanding of the markets being traded and the indicators being used, as well as the ability to process and analyse large amounts of data.

So for example, rather than trading based on price breakouts, which can be crowded and counterproductive to returns, Quest is removing a principle component of trend following from their Program to create diverse entry points. Additionally, they are trading the relative value to dollar-yen and using fundamentals in a positively skewed way to trade dollar-yen, which creates uncorrelated return streams. By taking these approaches, Quest is able to generate higher Sharpe ratios than the typical CTAs.

Views on Discretion

In the past, there were two camps in the financial industry:

  1. traditional, fundamental, discretionary investors and traders, and 
  2. quants who were 100% systematic in everything they did.

Investors were scared of the use of discretion, which was seen as a negative. 

The very idea of using discretion seemed to conflict with the notion that systematic trading could produce repeatable results over time. 

However, the rise of quantamental investing has led to a third camp that combines quant models with the discretion of portfolio managers. The result has been success for some investors. 

Mike noted that some researchers and portfolio managers that look like pure systematic quants on their resumes actually have some discretion built into their models.

CTA Replication

The discussion turned to the topic of CTA replication, where investors try to mimic the trading strategies of CTAs through regression analysis without actually having a real model or risk management framework. 

Mike mentioned that many managers have realized they can manage billions of assets by selling trend to the retail community and packaging it up via a 40 Act vehicle. He also noted that some of the world’s largest sovereign wealth funds and family offices have moved into this space to build it themselves at a lower cost, but some have decided not to do so because of the operational nature of running a CTA.

When Niels asked Nigol about the risks of CTA replication, he explained that the longer the manager is in their trading strategy, the more confident one can be in the accuracy of the math used to replicate them. Nigol said that the risk is minimal for long-term managers, but becomes more difficult for short-term managers, as the math becomes harder to know when a new condition kicks in. He added that the risk of smart beta is not in the math, but rather in allocating to large cap versus small cap or replicators, which can be dangerous.

Capacity and Liquidity Issues for Short Term Programs

The participants discuss the issue of capacity for short-term managers, with Nigol stating that size is not a goal for his firm, and that they are focused on creating returns that are positively skewed. 

He also commented on liquidity, stating that it is much more dynamic than it used to be when it was the banks making markets. 

High-frequency trading firms are able to provide liquidity when nothing is going on and then completely pull the liquidity out of the market when things are not going well for them. Liquidity is drying up very quickly when markets are going down, much more than before. 

There is much more intelligence in pricing of markets, which means the intermediate-term size hedge fund or the large hedge fund is more easily picked up by these high-frequency firms. 

Mike added that the HFT space has two camps - those who have reinvested their profits and are playing the arms race, and those who have waved the technology white flag and are competing on their understanding of market microstructure. The latter group can help short-term managers to execute their orders more intelligently and provide additional signals that can be fed back into models.

Misconceptions of Trend Following

Niels asked Mike and Nigol to share their thoughts on common misconceptions about their respective trading styles. 

Mike disagreed with the notion that trend following is easy, citing the need to focus on data quality, strategy types, team diversity, execution, time horizons, and market selection. He noted that alternative market CTAs have increased liquidity in certain markets, making it easier for short-term traders like themselves to participate. 

Nigol disagreed with the idea that short-term CTAs are less reliable and harder to understand than their long-term counterparts. He believes that short-term trading, if done with discipline and intellectual integrity, is more reliable and valuable than long-term trading in the current market environment. 

Outlook for 2023

Nigol believes that the most exciting prospect for 2023 is the fact that the era of central banks printing money without any impact on inflation is over. He believes that physical commodity inflation is here to stay, and this will bring about new opportunities. 

However, he is worried about how extreme the behavior of central banks can be and how they might shut down markets if things don't go their way. Finally, Nigol is concerned about the level of liquidity in markets and how quickly it is changing.

This is based on an episode of Top Traders Unplugged, a bi-weekly podcast with the most interesting and experienced investors, economists, traders and thought leaders in the world. Sign up to our Newsletter or Subscribe on your preferred podcast platform so that you don’t miss out on future episodes.