Adaptability through the Pandemic: Poetry and Machine Learning

A Q&A with Millburn’s Barry Goodman and Grant Smith

Machine learning takes its lessons from data. And while history rarely repeats perfectly, in words attributed to Samuel Clemens (a.k.a. Mark Twain), “…it often rhymes.” The beauty of machine learning, in our view, lies precisely in its ability to find these rhymes—repeatable, tradable patterns in market behavior teased out, tested and confirmed through often decades worth of data.

“History may rhyme, but some rhymes are easier to spot than others.”

To extend the metaphor, some rhymes are easier to spot than others. For example, it doesn’t take high-powered computers or a sophisticated statistical learning approach to find a basic relationship between, say, low global supply of coffee beans and rising prices. Think of this as a traditional rhyme. Simple, understandable, but perhaps a bit limiting.

But what about when you consider the effects of seasonality (winter and summer demand patterns). Is the relationship between inventory and price still valid, or is it conditional on time-of-year? And if we also consider shifting market sentiment towards coffee consumption, does the relationship strengthen or weaken? Now we are venturing into the land of the Shakespearean sonnet. While more complex, and requiring perhaps more effort to construct, it is a different and potentially very powerful way of examining the world.

Advantages of machine learning approaches include this ability to uncover complex relationships between different data inputs (or “features” in machine language parlance)—put another way, these approaches are well-suited to extracting signals from noisy data sets. And the data sets themselves can be vast, meaning the strategies can have long memories. Depending on the specific approach, machine learning can utilize decades worth of data. Finally, machine learning strategies can learn over time, adapting autonomously as market conditions change.

Of course, no strategy is perfect. While learning from history is, we believe, a very logical and powerful approach to predicting price movements in markets (and one that has proven profitable in many cases), it is simultaneously a potential weakness that must be carefully considered. Specifically, in order to be most accurate in these price forecasts, we want: a) to include all potentially relevant features (i.e., drivers of return) in the models’ training sets; and b) environments today that are (somewhat) similar to the past…i.e., environments that “rhyme.”

March 2020 provides perhaps the perfect case study to demonstrate both strengths and weaknesses, and the opportunity to think about how to continue to improve. Below we present a Q&A with Grant Smith and Barry Goodman, Millburn’s co-CEOs and senior members of Millburn’s Investment Committee.

Q. How did machine learning strategies fare in March?

1. [Barry Goodman] I can only answer from our perspective because managers that use the technology use it in very different ways and to different degrees. For our programs, the common tread is that the active risk-taking—the “signals” that the programs generate that direct whether to go long or short, or whether to take a more opportunistic or defensive stance in a market—are all driven by machine learning technology.

In our experience, performance in March differed by program, with some posting large losses and others actually posting some of their best ever monthly returns. So, for anyone who asks the question “is machine learning broken,” we think the answer is clearly no. But this doesn’t mean we aren’t working harder than ever to analyze, understand, and make improvements.

For strategies that underperformed, what was the reason? What has your analysis found so far?

[Grant Smith] In terms of attribution for our machine learning strategies that underperformed, the source of the pain was really focused in one sector: equities. If you followed the equity markets closely, as the pandemic accelerated we started to see some very unusual behavior in many—actually almost all—of the equity markets that we trade. As an example, at one point in March we saw the S&P500 drop more than 28% in a matter of 13 trading days. This was something truly without precedent. Using the S&P as an example again, we also saw material, sustained volatility, with daily price moves exceeding 10% both up and down. Again, this was behavior we had never seen before. These are just two examples in only one market, but we were seeing this type of activity practically everywhere in the equities sector across all regions.

“Machine learning strategies learn from history. Models analyze decades of historical data and try to find tendencies, or patterns.”

[BG] I think it is important to reiterate that it really was overwhelmingly the equities sector that caused problems in March, and this sector was where we saw this unique market behavior that simply was not seen in the training sets for these models. And it really was a coordinated move across practically all global equity markets, which meant diversification by geography, which normally would help, did not in this case. On average the other non-equity sectors in our machine learning programs were able to make good forecasts and were positive.

In equities then, what were the machine learning models seeing?

[GS] Our research has shown that, based on history, swift moves in equities, either to the upside or the downside, are typically followed by the market reverting to the mean shortly thereafter. This is based on decades of data and is a particularly strong effect in equities as opposed to other sectors. These mean reverting tendencies can be reinforced by other factors that have historically resulted in rising equities, such as falling interest rates, or cheap energy. Given the full line-up of data inputs that our machine learning models consider, it was not surprising to see them indicating high-conviction, long positions in equities. There were some models that were forecasting prices to fall, but the strength of these high-conviction long signals really carried the day. The signals were primarily pointing to a swift market rebound.

Have the models “learned” from these market events?

[BG] One of the powerful things about these machine learning models is their self-adapting properties. This is one reason we think investors should have machine learning strategies in their portfolio, especially in times of uncertainty. In normal market environments, machine learning models are “re-fit” or “re-learned” periodically throughout the year. So in our case maybe every few weeks or months a subset of the models in the portfolio are being rebuilt, essentially folding-in the latest several months into the training set and asking the machine learning algorithms to determine whether the drivers of return are changing.

“These models can self-adapt. This is one reason we think investors should have learning strategies in their portfolio, especially in times of change and uncertainty.”

[GS] Yes, each time you fold that new data into the training set and apply the machine learning algorithms it will probably mean the models will change a bit—the process will find new, or slightly different, patterns, or will make slight adjustments to what it sees as the influencers of market movements. In a case like we saw in March, recent market behavior was so different from what the models thought was the structure of the market, a refit would certainly impact the models’ structure. Changes will be slow, in general, but can be faster when you encounter unique and/or very extreme moves.

[BG] Yes so to Grant’s point when we see unique market behavior, one of the first things we can do is accelerate this “re-fit” schedule and refresh models to enable them to learn immediately. It is a way for the systems to keep up with a changing environment, and we believe potentially one of the key advantages of our approach.

Many machine learning strategies are re-learning (or “refitting”) the models using March data, but some people think that March was a true “one-off” situation that may never be repeated again for 100 years. Others disagree. How do you address this tension in your re-learning process? How do you make sure you don’t put too much weight on what may be a very unique period?

[GS] Time will tell whether this was a one-off or whether we will see variations of March 2020 going forward. Certainly, it was a unique moment in history relative to the data on which our models were trained. So, when we re-fit it will have an impact. That doesn’t mean all other data is forgotten, or that the fitting process won’t recognize the uniqueness of this event, but at least the models will be aware that such an event is possible.

Having said that, there are many statistical techniques we and others use to avoid the idea of “overfitting” to any particular period in the data, which is a key consideration in any machine learning approach. Even when we re-fit, March data will by definition be recognized as a rare set of observations. Some models will exponentially weight recent history, others will not. Some models use five years of history and as a result March will be more important, potentially, but other models use 30 or 40 years of history, and therefore March will probably have less influence.

“Time will tell whether this was a one-off or whether we will see variations of March 2020 going forward.”

[BG] But we want to include it, because that is the whole point of the machine learning technology—to learn from history and not form a biased or discretionary view on whether it was an outlier or something more meaningful or more likely to repeat. We let the data and statistics lead us.

So were something like March to happen again, would your machine learning models react differently?

[GS] Yes they would, for the simple reason that the models now have March data included in their training sets, so they are considering this. They have indeed learned from March. That doesn’t mean a particular machine learning model wouldn’t still be long equities in a similar situation, but the signals would almost certainly be less stubborn.

Each environment is unique, however, so we don’t focus on perfect repeatability, which really never happens, but rather on setting parameters so the techniques can look for “close approximations” to a match. So as I said while we wouldn’t necessarily expect to see massive signal changes in future situations that are similar, simply because the models won’t necessarily want to overweight March, the models should be better positioned given the additional data.

“We don’t focus on perfect repeatability, which really never happens, but rather on looking for ‘close approximations’ to a match.”

But there are other things that could also have an impact on how the systems react. A different volatility model might be faster to restrict maximum positions, for example. The research team is constantly looking at ways to generate more accurate forecasts, and ways to better manage risk.

What do you see going forward? Will machine learning strategies be well-positioned in what looks to be a period of uncertainty?

[BG] The world has changed, and at least for the near-term we are all making adjustments in our personal and professional lives. Nothing is certain, but right now from a markets’ point-of-view it looks like we will be entering an era of increasing frictions, including across geographies. Economies, currencies and asset classes in general seem to be heading towards a decoupling. There is massive fiscal and monetary stimulus that has been put into place as governments and central banks seek to prop up the economy, while at the same time forces of stalling global growth and increasing unemployment act to suppress it. In my nearly 40-year career I have never seen such a strong confluence of market conditions.

We think this will demand an approach that is adaptive, that can trade a range of instruments and find alpha wherever it occurs. A portfolio that has opportunities to trade a variety of asset classes—including equities but also sectors like currencies, commodities, and fixed income, for example—and a portfolio that trades cross-geography, will be potentially well-positioned, in our view. We believe less flexible rules-based approaches or approaches that can’t make use of the growing amount of data available to us may be less effective.

[GS] Like our models, we are continually learning.

“The future may demand an approach that is adaptive, that can trade a range of instruments and find alpha wherever it occurs.”

DISCLOSURE

PAST PERFORMANCE IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS. THE POTENTIAL FOR PROFIT IS ACCOMPANIED BY THE RISK OF LOSS.