Chris Kennedy: Decoding the Evolution of Managed Futures Hedge Funds

Introduction

In this episode, we are joined by Chris Kennedy from Bridge Alternatives. Chris shares his insights on the evolution of the managed futures space, the complexities of adding more parameters to trading models, and the unique challenges and opportunities that come with being a smaller manager in the alternative investment industry.

Topics Discussed

  • Chris Kennedy’s background and journey into the alternative investment industry
  • The evolution of managed futures space and the increasing complexity of trading models
  • The balance between model complexity and performance in managed futures
  • The challenges and opportunities for smaller managers in the alternative investment industry
  • The benefits and trade-offs of firms at different levels of size or maturity
  • Bridge’s niche in the pure commodity space and how it came about
  • The role of Bridge in coaching and guiding managers through the process of launching and growing their funds
  • The lessons learned from years of experience in manager selection
  • The reporting challenges for multi-strat funds
  • The unique position of Renaissance Technologies in the industry

Conclusion

This episode provides valuable insights for anyone interested in the managed futures space, the complexities of trading models, and the dynamics of the alternative investment industry. Chris Kennedy’s experience and insights offer a unique perspective on these topics.

This is “ReSolve Riffs” – published on YouTube every Friday afternoon to debate the most relevant investment topics of the day, hosted by Adam Butler, Mike Philbrick and Rodrigo Gordillo of ReSolve Global* and Richard Laterman of ReSolve Asset Management.

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Summary

The managed futures industry has seen significant evolution, moving from a simple Trend following approach to complex, parameterized models. Initially, managers relied on a single moving average crossover for all markets, but as the industry matured, models became more rigorous and systematic, with strategies being fine-tuned over time. This evolution can be likened to the classic bias-variance tradeoff, with model complexity increasing in response to the dynamic nature of managed futures. Despite some advocates for simple, tried and tested approaches, the modern era of trading has demanded more complex models for competitive advantage and precision in forecasting. As the industry evolves, assessment of potential managers in the alternative investment industry necessitates an understanding of their relative pedigree and their ability to compete for alpha. The complexity of their models, the trade-off between model complexity and forecast improvement, as well as the size and maturity of the firm all play a role in its ability to trade in certain markets and attract capital. Lessons from the past stress the importance of a strong network, support for small managers, and a robust investment process. Bridge Alternatives, a firm specializing in the commodity space within the managed futures industry, has noted a resurgence of interest in commodity investments. A shift in methodology has been observed among commodity managers, who now focus on understanding supply and demand for futures contracts. This shift led to the emergence of a new kind of trader who blends fundamental supply and demand analysis with a participant-minded approach. However, quant funds reporting and evaluation, especially those with a Sharpe ratio of 1 or higher, can be challenging due to lack of transparency in the industry. Quantitative strategies in the managed futures space are becoming increasingly complex, with managers exploring shorter-term trading and using more observations. This rise in complexity also brings operational and computational challenges. There is a high demand for skilled managers in this space, with benefits for investing in smaller, less mature firms who can trade more frequently and in less liquid markets. However, investors need to consider trade-offs, such as potential for higher turnover and the need for more operational resources. In conclusion, the managed futures industry has greatly evolved, with a shift towards complex models and a focus on supply and demand analysis. Investors need to carefully consider the trade-offs and challenges associated with different levels of firm size and maturity. Despite the complexities, the evolution and continued sophistication of this industry make it an exciting space for investment.

Topic Summaries

1. Evolution of the managed futures industry

The managed futures industry has undergone a significant evolution over time, progressing from simple Trend following approaches to more complex and parameterized models. Initially, managers relied on classic chartist ideas, such as using a single moving average crossover applied to all markets. However, as the industry matured, models became more rigorous and systematic, adapting to remain competitive. This natural progression led to the development of more fine-tuned strategies, with managers incorporating multiple moving average crossovers and even applying them to specific sectors or individual markets. The evolution of the industry can be likened to the classic bias-variance tradeoff, where model complexity increases in response to the adaptive and ever-changing nature of managed futures. While some old school Trend traders argue for sticking to the tried and tested approaches, the complexity of modern models has allowed for more specific forecasts and increased competition for alpha. The industry has moved beyond the simplicity of the past and embraced the need for adaptability and sophistication in order to thrive in the current market environment.

2. Manager selection and lessons learned

Selecting managers in the alternative investment industry requires assessing their relative pedigree and understanding their ability to compete for alpha. It is important to consider the complexity of their models and the trade-offs between model complexity and forecast improvement. Additionally, the size and maturity of a firm can impact its ability to trade in certain markets and attract capital. Lessons learned include the importance of having a strong network, the value of coaching and supporting small managers, and the need for a strong investment process. For example, smaller managers may be able to operate in less competitive niches and trade smaller futures markets, but the impact of their performance may be higher for investors.

3. Bridge’s focus on the commodity space

Bridge Alternatives has carved out a niche in the commodity space within the managed futures industry. They have established themselves as experts in this area by building an index and gathering data from 15 of the largest commodity managers. The commodity industry has been slower to institutionalize compared to other parts of the managed futures business. However, Bridge has observed a resurgence of interest in commodity investments, particularly in the early 2010s. They have noticed a change in the methodology of commodity managers, who now focus on understanding the supply and demand for futures contracts. This shift has led to a new group of traders who combine fundamental supply and demand analysis with a participant-minded approach. Bridge is excited about the work being done in the commodity space and believes there is more to come for commodity investments.

4. Challenges in reporting and evaluating quant funds

Reporting and evaluating quant funds, especially those with a Sharpe ratio of 1 or higher, can be challenging. Investors may struggle to understand the rough periods that can occur with higher Sharpe ratios. Additionally, there is a lack of transparency in the industry, making it difficult to assess the true performance of quant funds. For example, there is a significant gap between smaller successful futures traders and large firms like Renaissance, and the industry has not yet found a way to bridge this gap. Quantitative strategies in the managed futures space are becoming more complex, with managers exploring shorter-term trading and utilizing more observations. However, this increased complexity also brings operational and computational challenges. The talent pool in the commodities space is relatively small, and there is a shortage of people trained to run these types of funds. As a result, there is a high demand for skilled managers in this space. When it comes to allocating to quant funds, there are benefits to investing in smaller, less mature firms. These firms have the ability to trade more frequently, have shorter trade horizons, and can trade in less liquid markets. However, there are trade-offs to consider, such as the potential for higher turnover and the need for more operational resources. Overall, the reporting and evaluation of quant funds can be complex, and investors need to carefully consider the trade-offs and challenges associated with different levels of firm size and maturity.

Chris Kennedy
Partner, Bridge Alternatives

Chris is a Partner at Bridge Alternatives, where he focuses on business development with an emphasis on brokerage and capital raising.

Prior to joining Bridge, Chris was a Director within the Advisory Group of Societe Generale’s Prime Services division.

TRANSCRIPT

[00:00:00]Chris Kennedy: Just from a purely like participant minded, where is the opportunity, where is it moving kind of perspective. I think complexity is good, right? And I think there’s reasons to believe that as you add more complexity to your models, you have the better chance to compete for alpha, right?

I think Alpha and model complexity are very highly related. Can I think your models, do I think your models can be too complicated? Absolutely. And that’s where this kind of bias variance trade off comes to. I think, classically there is an optimal model complexity, and I think for years, many managed futures groups found the marginal return to complexity to be positive. The issue with complexity is, as you make your models more specific. you have less and less data to work with, right? So if I have all of these markets and I’m trading daily data and I use one model across them, I have tons of data to validate any sort of statistical testing I do. As I make those theses more specific, I just have less and less data, and so there is this clear optimization issue here. And I think, the space may be wrestling with some of those things. In a meta sense, I think there’s still plenty of good ideas and things like that, but just making your models more specific in kind of a simple way, I don’t think that will yield significantly better results.

Backgrounder

[00:01:29]Rodrigo Gordillo: All right. Welcome everybody to another episode of ReSolve Riffs. And today we have a very special guest Mr. Chris Kennedy from Bridge Alternatives. And before we do begin, why don’t you tell us a little bit about your background and how you got started in the alternative investment industry?

[00:01:44]Chris Kennedy: Sure guys. Thanks for having me. I joined the investment industry in 2010, started working for NewEdge. At the time was a great place to work. It was the recent merger between … and Kelly Financial. It was a futures commission merchant. It had a very large book of business.

It serviced many of the large CTAs and managed futures funds across the street. I worked for the Prime Services Group there. It’s a notable group. This was the team that created the SocGen CTA Index, the Trend Index. It was known for its research events and a lot of the publications that I think brought managed futures to kind of institutions for acceptance. The team I worked for there in 2015 decided for their own reasons to launch Bridge Alternatives. Bridge is an independent introducing broker. We also have a capital raising business and we recently have a newly formed outsource solutions business. That’s an outsource CFO and COO, offering primarily serving the private marketplace in that part of our business, but I do have some hedge fund clients as well.

[00:02:46]Rodrigo Gordillo: So just out of curiosity, like I’ve always wondered what the service, when I look at the NewEdge Indices, just going back to that, the origin, what was the goal there and what were you guys thinking at the time?

[00:02:58]Chris Kennedy: Oh, so a lot of this predates me. This work goes back to the year 2000 and the primary drivers of those projects were to create kind of one of the first daily hedge fund indices. In the year 2000, this was a technological achievement, right? You needed managed accounts to get the daily settlements.

A lot of the industry still operated via funds. It was a huge undertaking. And I think my partners at the time were probably the only group that could have done it. They convinced, I think at the time, upwards of 15 managers, the methodology changed slightly over the years, but 15 managers to send them daily returns. This was when CTAs viewed their daily returns as trade secrets and there was a ton of trust involved. Barclay Hedge, which is a database provider, was a key partner. And the result was, I think, an incredible tool for institutions to wrap their heads around this kind of managed futures thing, which, at the time was really nowhere near as broadly disseminated as other hedge fund strategies.

[00:03:57]Adam Butler: How big was that CTA universe at the time?

[00:04:01]Chris Kennedy: Oh man it wasn’t as big as it is today. Some of the brands have stuck around. And some haven’t but the task was, it really never changed over the, now over two decades that it’s been running. It was, what are the largest managers that broadly represent managed futures? And what do those, what are the performance of those assets look like?

And I think, the constituency changed over the years. It was almost all Trend followers to begin with. Today, when you look at the index, there’s some short term managers, there’s macro. But it remains the place to look for, how has institutions and their assets performed when invested in managed features?

[00:04:35]Adam Butler: Yeah. I’m more interested in how, what was the AUM like back then?

[00:04:39]Chris Kennedy: Oh…

[00:04:39]Adam Butler: Were CTAs managing…

[00:04:43]Chris Kennedy: The cutoff has changed dramatically. The smallest managers would be hundreds of millions, and today, I think the smallest managers still have billions. The space grew, I think, as we all know, very quickly, post 2008 the sell, I would say in the advocacy that we espoused was partly the crisis alpha thing, which was developing in 2010 when I started, but before that, CTAs and managed futures was just a great liquid alternative, right? It was uncorrelated, and the sell was its lack of correlation. We saw institutional hedge fund portfolios as being levered beta, even at the time, right, to some degree, and CTAs presented something completely different. From the CTA Index we took that playbook and all the interest generated for us and used the same tool set to develop the Short Term Traders Index. We carved out the Trend Index as well. And it’s always amazing how, those exercises, while they’re a bit simple, and it’s definitely a consistent playbook, the ability for institutions to then point to something and say, this is now a sector that we can invest within. It was always interesting to see how much of an effect that had.

[00:05:54]Rodrigo Gordillo: Did you find that the interest back then was mostly in fund of funds or direct purchasing of fund units, or was it all SMA?

[00:06:05]Chris Kennedy: It’s a great question. And it’s probably worth going back to the future of that team. Even in the late eighties and early nineties, our team was meeting with investors, exploring managed account solutions, and there was at a period of time, a great network business, that my partners really figured out.

It was, find investors who are broadly interested in managed futures. Get to know them, introduce some managers to them, determine if they have managed accountant relationships, ultimately pitch for the brokerage. If you do, and you get a sense of where they’re clearing these funds, ask who they’re clearing with, ask who they’ve added, allocated to, and you get this nice effect where you’re meeting managers from investors, and from managers, you’re meeting investors. And as the space itself was growing, it was a very successful time for that team. Ultimately I think that business matured. It found its foothold within the institutional community. And being in an investment bank changed as well. And I think we’ve all had friends that maybe moved on from some of those careers in investment banking. A lot of those roles have normalized today too, but it was a very interesting time.

[00:07:08]Rodrigo Gordillo: So what…

[00:07:08]Adam Butler: There was a mandate to generally expand the kinds of investors that would have been interested in managed futures too, right? I heard you say there’s a group of investors that were investing in managed futures and then there were a group of funds and there was some really good synergy there. How did you guys think about expanding the canvas into institutions, or into areas of asset management that just had, weren’t familiar with that style at all?

[00:07:32]Chris Kennedy: Yeah, it’s a great question. The way that we solved that was through education. So we ran a series of research events. These were well attended by large institutions. It was at the time, maybe in alternatives when you could get away to a nice, fancy place and you could entertain a bit. and I think, that doesn’t happen quite as a bit anymore. But we had this great model. We would invite 10 managers and upwards of 70, 80 investors, and purely focus on research. And at the time, people were talking about using multiple Trend signals at once, maybe trading a market as its own, as opposed to using the same parameters across markets.

And the level of research certainly has changed over the years. And I think that’s followed a natural progression. That’s interesting in its own right. But the strategy was education, right? It was a time when, as you, if you grew the space, we covered so much of it, that our team’s P&L will grow. It was inevitable. As the space matured, I think it drove us to form Bridge. And I think at Bridge, what we did initially was try and apply that old playbook. We formed the Time Summit, which was broadly the same model. We had to run it on a different financial model, but succeeded in that. But, Bridge was an opportunity for us to start fresh and maybe look beyond kind of the large Trend followers and the large constituents in the managed futures industry, and look for maybe more niche players, which I think we’ve done.

[00:08:47]Rodrigo Gordillo: Right, so you guys focus on that, those niche players. So this is, there’s been an evolution, obviously, not only in the business model but in the managed futures space. How, tell us a little bit about what you’ve seen in terms of that evolution and specifically in the areas that you like to focus on.

[00:09:03]Chris Kennedy: Yeah, this is something Rod that you and I have talked about. I think, in my mind, being in a research and cap intro seat for all these years, I still don’t know who is going to perform well or what sectors will perform well, but you do get quite good at establishing kind of relative pedigree, which is, I think the job calls for that. The last, one of the main roles I had at SocGen and NewEdge was creating our manager reporting platform. And so I built from scratch a toolset to create something like 3000 manager monthly tear sheets, refreshing those as we receive new data and building this kind of large reporting database.

You had to build all those charts. You had to do all the math and you had to do it at scale and do it quickly. It was a good exercise. It really sharpened my programming skills. But what it also did is give you this like incredible visual database that you can see really, the entire managed futures industry with.

And I just remember sitting there, as you’re testing things and going through it and you get this great visual sense of what strategies do what, and it all boils down. And so (a), it was a great time for me to see what was all available, and (b), as you’re going through this over time and testing things, you really do develop a sense for how things are changing. What was expected maybe, out of a manager in the late nineties versus in kind of the mid two thousands, and also how performance is evolving too. To me, when you couple that with kind of the research access we had through the events and things, I think most of the managed futures industry has gone through a very, maybe with hindsight, it helps, but a very kind of logical path, right?

You started with kind of the classic Trend following approach, which kind of is a chartist idea, right? That evolved into something more rigorous and systematic, and the philosophies associated with that which makes sense, and then those models continue to adapt and evolve to remain competitive. They started as one simple set of parameters, maybe one moving average crossover applied to all markets. They then got maybe multiple moving over moving average crossovers, applied maybe to even a sector or an individual market, and things became a little bit more fine tuned in a very kind of natural progressive way.

The model that sticks out to me when I think about how this is all evolved is actually that classic bias variance tradeoff, right? In thinking about model complexity in terms of this very adaptive, complicated, ever changing ecosystem that we have with managed futures. I think when you think about it in that way, that the progression makes a lot of sense and I think it helps you think about maybe where things are going and what’s next, to a degree.

[00:11:31]Rodrigo Gordillo: One of the things that I, that you hear from old school Trend managers, is that everybody else is getting wrong. You’re getting too complex. It’s not adding any value, just stick to your old guns. And this will be the best way to use the, this concept of futures contracts in the best way. What are your thoughts on that? Are you, do you have any strong opinions on the old school Trend traders, the turtle traders and their approach versus the more complex approaches today?

[00:11:58]Chris Kennedy: It’s really hard to say. The best, the only insight I would have there is, there’s definitely like an ecology to these things, right? And there’s almost a highly, a complex adaptive systems element to all this. And I think the more people make their Trend systems complicated, the more interesting I would find some of those traditional approaches, right?

Just from a purely, like, participant minded, where is the opportunity, where-is-it-moving kind of perspective. I think complexity is good, right? And I think there’s reasons to believe that as you add more complexity to your models, you have the better chance to compete for alpha, right?

I think alpha and model complexity are very highly related. Can I think your models, do I think your models can be too complicated? Absolutely. And right. And that’s where this kind of bias variance trade off comes to. I think, classically there is an optimal model complexity, right? And I think for years, many managed futures groups found the marginal return to complexity to be positive. And I think it still is. The issue with complexity is, as you make your models more specific. you have less and less data to work with, right? So if I have all of these markets and I’m trading daily data and I use one model across them, I have tons of data to validate any sort of statistical testing I do. As I make those theses more specific, I just have less and less data, right? And so there is this clear optimization issue here. And I think, the space may be wrestling with some of those things. in a meta sense, right? I think there’s still plenty of good ideas and things like that, but just making your models more specific in kind of a simple way, I don’t think that will yield significantly better results.

[00:13:44]Adam Butler: So I think it’s worth pausing here for a minute, because I think a lot of listeners may not understand the different dimensions of complexity that can be explored within the managed futures space. When you say managers are adding complexity, what are some of the ways that the strategies are becoming more complex?

[00:14:05]Chris Kennedy: Yes. It’s a good question. Getting shorter term, I think that’s for sure. And when you…

[00:14:10]Adam Butler: Which is operationally more complex.

[00:14:11]Chris Kennedy: Yeah, for sure.

[00:14:12]Adam Butler: Quantitatively, computationally.

[00:14:15]Chris Kennedy: Yeah, agreed. And I think when you maybe have shorter holding periods, you have more observations with which, to work with. Given that, I do think there is more parameterization available to you. If you broadly say that Trend is your, the model that you’re applying to trading, there are a handful of parameters typically associated with that. There are other ways to trade Trend, and maybe it’s adding complementary models to an existing Trend approach and a core satellite model, or whatever. But there are other ways to add parameterized opinions of what you expect prices to do. And the more parameters you add, the more complex your model becomes. You can think of it also as, I think model complexity will result in more specific forecasts, right? So I’ve seen groups leveraging new techniques and getting very opinionated about how the world may evolve. I think you can only do that with very, more parameters and more complicated models.

[00:15:13]Adam Butler: Yeah, so I think it’s worth again drilling just one level deeper here, right? Because, for example, examining the performance of trading a market when two conditions are present, so for example, when a market is both above its 10 month, 10 day moving average and above its 200 day moving average.

Okay. You’re now limiting the sample space, right, to the period where both of those things are true. And so that sort of leans into your idea that as you, as models get more complex, you get smaller samples for each configuration of the model, right? If you are trading both the 10 day and the 200 day unconditionally, you’re not adding any more complexity, right? You’re not conditioning one on the other. You can add a wide variety of different moving average crosses. It’s different time series approaches. You can really begin to expand the canvas of how you define Trend without adding complexity in the way that you’re describing it, right?

Complexity is adding more and more conditions that need to be true in order for a trade to be made in one direction or another, and then conditioning your, or trading on all of those different states, right?

[00:16:39]Chris Kennedy: I think you nailed it, Adam. But what I would say is, does essentially smoothing out the opinions of your models with this parallel approach, does that ultimately lead to a more complicated view? Does that compete for, maybe against peers, for alpha as well. I think, again, it goes back to this idea that the, there is a moving target with which maybe alpha can be defined.

And I think if you’re not improving your forecast and you’re not, for lack of a better word, making your models more complicated, it can be argued you will have a harder time competing for that panel.

[00:17:17]Rodrigo Gordillo: I think the word…

[00:17:19]Adam Butler: We’re in complete agreement. I just wanted to make sure that we clarified exactly what becoming more complex means, right? Obviously, trading dozens of different Trend signals, but trading them all in equal weight, or some sort of naive way, has operational complexity.

[00:17:35]Chris Kennedy: Oh yeah. Yeah.

[00:17:36]Adam Butler: But it doesn’t make the model more complex from an overfitting or bias fit variance…

[00:17:41]Chris Kennedy: Totally agree.

[00:17:42]Adam Butler: …of doing things. I think that’s all I wanted to clarify.

[00:17:45]Rodrigo Gordillo: I think there’s, language here is important. I think we’ve always struggled with the, if you’re developing something that’s, that has a wide variety of different return streams that are not overly fit like an ensemble method, there’s a word that we’ve developed, we probably didn’t develop it, but we’ve started to use instead of complex, which is robustness, right?

How do you create a very fragile system? A single 200 day moving average that could be specifically wrong. And how do you create something that is more broadly correct, trying to pursue that same parameter? In this case, we’re talking about Trend, right? So that is not super complex. That is more robust.

Now, as you get more data, and one of the reasons that you want to do this, obviously, is because you want to have as much data as you possibly can. And I imagine that’s why we’re going shorter and shorter these days, is because they’re more short term. You have more data. The more data, you can actually have enough sample size where being more complex, having more dependent parameters, you can actually get a signal there. The more and more you do it now, what are the trade offs there, because if you’re trading faster and you’re trading more short term as an asset manager, Is this something that AHL and AQR can do in their large funds, or like, where does, and capacity come into play here and all the players that you’re seeing in the market?

[00:19:10]Chris Kennedy: It’s hugely important. And I think the style has to fit the size for sure. And that’s something that it’s hard to test for. I remember we wrote a paper gosh, when was it? Probably in the middle two thousands in a period where Trend following, broadly, was flattish or maybe it was experiencing some struggles, and it was titled The Capacity of the Managed Futures Industry, and we used a hypothetical Trend model that we had developed and is still used today in the Trend Indicator to just see how big could you get. Absolutely, like turnover capacity. These things are all related. And, you would expect someone who is trading a lot to be aware of the frictions that are associated with that, and have the necessary techniques and technologies, which are completely independent in some cases, than your alpha strategies, and maybe your signal prediction. There’s an entire set of techniques that you have to develop to make all that work.

[00:20:03]Rodrigo Gordillo: Yeah, absolutely, on the trading side. Yeah. When I remember visiting AHL, went back when I was 26 years old, I went to the trading desk, and trades were coming in super fast. They had 25 people that they’re, they walked us through how they would have Chinese walls to make sure that nobody knew the trade that was coming in. It was one guy in charge of calling certain desks so that there was no slip. Nobody was trying to front run them like, the amount of, is this trying to capture as much alpha as possible? No, we’re just trying to get our trades in, right? That’s all that, that the whole floor was about trade execution. It was not about alpha.

[00:20:40]Chris Kennedy: There’s a well known, there’s a well known short term futures trader who has devoted a significant amount to their execution infrastructure, and, it’s an ongoing exercise. It is never static. It is an ongoing investment. And it’s extremely competitive. I think the amount of talent, both technical and quantitative, and what you’d call the short-term or mid-frequency spaces, other people claim it, is immense, and it is very difficult to stay at the state of the art there.

So look at to, to answer your original question, that absolutely limits the sorts of strategies that can be run at certain frequencies. And again, once you meet enough of these managers and you see what they’re trying to design, depending on the stage that they’re at, you get a sense for, does this fit with successful businesses that I’ve seen previously?

[00:21:27]Adam Butler: So that’s a good segue, because I wanted to talk about the relative merits of firms at different levels of either size or maturity. There are some obvious benefits to being able to allocate to firms with less AUM, right? We touched on some of them, right? You can trade, you can have higher … turnover, you can have shorter trade horizons, you can trade less liquid markets, that sort of thing. Maybe just spend a moment describing some of the trade offs. What are some of the benefits versus, of small firms versus large firms and vice versa?

[00:22:04]Chris Kennedy: Yeah, the benefits are, there’s been some, frankly, some academic research on what people, and I think there may be some issues with those studies, but we can cover them anyway. It appears and there’s some literature on this again, that emerging managers may have a slight Sharpe benefit when invested in, compared to maybe a larger manager.

I think there’s reasons to believe, as you said, that I think that’s true. The managers would be smaller, there’d be less friction associated with their size. So as an investor in one of these strategies, you may benefit from that. Smaller managers may be able to operate in less competitive niches.

That’s the obvious one, right? They may be able to trade smaller futures markets where there’s just frankly, less competition, maybe more natural participants looking to hedge and more sources of alpha from just a purely participant standpoint, What may be tricky with those, and I think what may diminish the benefit is, I think those studies neglect the influence that an allocator would have themselves in those managers, should they participate, right? The marginal dollar to a very established manager, the effect of that will be very low. The marginal dollar for a very small manager, the effect of that may be very high, right? We’ve seen it firsthand.

We do look for groups that I would say have high Sharpe ratios. They tend to be capacity constrained. They tend to be smaller. That doesn’t always mean that it’s a great investment, right? And I think those are 2 different things. Seeing someone who has an opportunity set that can grow, and they have reasons for how they can grow it and operationally they can scale there. There’s a lot that has to happen correctly for that to work. So it, just being small obviously doesn’t necessarily mean you’re capable. Look at the teams at Two Sigma and Citadel and all these places, they have tons of money to invest and they seem to do it very successfully.

So there’s absolutely economies of scale to certain, of certain businesses here. I would also say we’ve seen people who are sole proprietors do extremely well, right? They just have to be working in the right place and probably they have to watch their size.

[00:24:07]Rodrigo Gordillo: So let’s go through that range, because you look for high Sharpe ratio managers. What size do you cut off conversations with people that want your help? Is there an AUM size that you prefer to be in, because you find that’s where the most opportunities are for your, for Bridge?

[00:24:28]Chris Kennedy: So currently, if I think about our current clients, capital raise clients, they vary. We probably don’t have many that are over a billion dollars though. Yeah, very naturally, I think at some point you have large IR teams, you have, you start to hire people, you get tons of help from your cap intro desk since you’re a meaningful client. There’s less need for maybe someone like Bridge at those sizes. Yeah, the vast majority of our groups are slightly smaller. But I, it’s extremely case dependent. These things are so multifaceted. If we met a manager who just felt like they were reaching the wrong audience, maybe they were European and just didn’t have a great US contingent, we would absolutely be able to help someone like that for sure. I’d say what we have the luxury of doing here at Bridge is seeing a good amount of flow and having a strong network that in some cases is 30 plus years old for some of my partners. We meet a lot of people at various stages of their business.

So we have the luxury of kind of selecting who we can work with and being a really instrumental partner for them. I think, for Bridge, it doesn’t make a ton of sense to be a tool that’s underused. We really want our partners to rely on us, to value us. And we like to get our hands dirty. I would say more than half of the things we work with are day one. So there’s an element of what you’re saying, Rod, that’s totally correct. In addition to raising capital, we’re often coaching people through fee negotiations, helping them hire people, consulting them on how they should grow their business, helping them consider other factors and really just leveraging the experience and the insight that we’ve gathered really firsthand in the trenches with these people. And it’s a very hard business, especially for some of these small managers. It is extremely path dependent, it’s emotionally challenging and, you really become close to some of your clients, just because of how hard it can be.

[00:26:21]Adam Butler: Now Bridge, I know, does have a bit of a niche in the pure commodity space. How did that come about?

[00:26:28]Chris Kennedy: It’s an area that, the manage, just being within managed futures you always hear about, I would say the commodity industry was later to mature and institutionalize than maybe other parts of the managed futures business. We probably, part of forming Bridge was absolutely tied to trying to do more with commodity managers.

There are reasons why some investment banks struggle to do brokerage with commodity managers. It requires a kind of a risk philosophy that not every large “bulge” bank can get their head around. At Bridge, we could help these groups. We could use a variety of brokered solutions as an independent IB.

We could help them broker at a number of different places, and it gave us more flexibility with which to engage with this community. Ryan Duncan here, who’s managing partner, he made a number of I would say really intellectually driven bets on that side of the world back in kind of the early 2010s. And we started work again as our playbook dictates and building an index in that corner of the world. So when we launched Bridge, we got to work on this and it reminded us a lot of what we saw of CTAs in the early kind of nineties, which is, many didn’t want to give us data. This was monthly and they still were concerned about it, reverse engineering and positioning is very delicate business within the commodity sector. But nonetheless, we convinced 15 of the largest managers in that sector to start sending us data, and I feel like really established ourselves as, within the hedge fund corner of the commodity ecosystem, a really connected expert. And we’ve written some research on that part of the world.

It’s been. I’d say up until 2020 and COVID, it was a really additive and interesting sector to complete kind of a managed futures investment with. Since then it’s changed and I think it’s really morphed into its own investment category. It’s received a ton of interest from the multi-stack community, from institutions, and we’ve been helping educate people.

[00:28:22]Rodrigo Gordillo: And are you finding a lot of uptake from large pensions and institutions, or are we looking at more mid-sized?

[00:28:29]Chris Kennedy: Yes, there has been a community that has always invested in commodity funds, always, and that community has never left. I would say the interest in commodity funds has grown substantially across the institutional community, though, in the last three years, and there’s been a lot of new entrants. The reasons are obvious, right? Like, people concerned with inflation may look to commodity funds, particularly manage those trading futures contracts, really for two reasons, (a) they’re going to hold a lot of their cash and interest bearing instruments, which are short term debt. It should do fine in these interest rate environments. But secondarily commodities themselves may benefit from an inflationary cycle. So that was a big driver. The other driver was the performance during the COVID period. I wrote a paper called The Compelling Case for Commodity Hedge Funds, where we reviewed data from this index and talked about it in aggregate.

A lot of that information is protected by NDAs and such, but studied how this contingent did during COVID. CTAs did really well during COVID. But commodity managers did far better. And I think that fact is lesser known in the institutional community, still to give you a sense, if you were to, say in February of 2020, if you were standing there, and you looked forward 3 years, the performance at the end of COVID represented a 5 sigma move from your expectations established in February 2020. And this is at an index level of 15 managers. There were several months when all 15 managers made money during that period. There were cases where managers continue to make double digit returns. There was something that happened in the macro universe during COVID that, I think, really lended itself to those managers.

It’s hard to say exactly why that is. It’s not something you want to pontificate about too much, but, I do recall that even back in February of 2020, a lot of our clients in that part of the world were very concerned. They were hearing stuff, they were seeing demand issues in China, and they were positioning in accordance with that. Furthermore, they were quick to realize that this recovery that was happening and the spending mostly on heavy goods from the US was material, right? So they seemed to be ahead of things from the get-go.

[00:30:47]Rodrigo Gordillo: So these are not, these largest 50, 15 managers, right…

[00:30:52]Chris Kennedy: 15.

[00:30:53]Rodrigo Gordillo: They’re not largely Trend followers, I imagine, or maybe they are. Why don’t you tell us what else they do? It seems like a macro call there, where they’re seeing some demand, they’re looking at positioning in China or whatever it was, and then making some decisions. We’re not looking, we’re not talking about a traditional…

[00:31:08]Chris Kennedy: No. And look, there are commodity Trend followers, and that’s part of the toolkit for sure. These are managers though, that are essentially applying macro type strategies within the commodity ecosystem. There is a whole different category of supply and demand analysis that you can do within those sectors. And it’s a very diverse ecosystem. You have diversified groups, you have both systematic and discretionary groups, and you have a lot of sector specialists. You’ll see energy funds. They may just trade crude oil. They may just trade gas. You have Ag funds. They may just trade a part of the Ag complex. They are extremely specific in their expertise. But commodity funds are not a new thing, right? What we have found though, is that industry itself has changed over time too. There was some notable closures of commodity hedge funds in the late 2010s, I’d say, the mid 2010s and late 2010s. Edesia was one, this was Louis Dreyfus’s hedge fund. There were some other notable ones.

We saw this latest resurgence accompanied by a change amongst the methodology and these managers. Broadly speaking, something we noticed in that a lot of people can do really good fundamental supply and demand work, right? They can build the balance sheets. They can collect interesting alternative data sources. Some of these can even be proprietary. What we felt like changed and modernize the industry is, you needed to do all that work really well, but you also had a great sense for what was the supply and demand for futures contracts.

And at times, those could be completely at odds for temporary periods. And so it was being extremely participant minded of who was on the other side. Why might I make money? Who might follow me into this idea? And thinking about things very causally, I think, has led to a whole new group of traders. It keeps us very excited about the work being done there, and I think, what’s to come for commodity ….

[00:33:01]Adam Butler: Yeah, so it’s a much more heterogeneous group than the CTA and even the CTA index, which is not even the Trend sub index, which clearly tends to focus on Trend-only managers or mostly Trend managers. The CTA index itself, I think the average pairwise correlation is in the sort of 0.5, 0.6 range. But if I recall from the paper you wrote, in the commodity index, it’s much, much lower, right? They’re doing, clearly doing very niche things and they’re doing very different things, deriving edges from different areas and different insights.

[00:33:34]Chris Kennedy: No, you’re absolutely right. And to be fair, some of that is just instrument selection, right? Some of these groups, if you have a group trading energies and a group trading eggs they may not correlate, but as a sector, the pairwise correlation is essentially zero, right? And there’s quantitative evidence of that too.

We have 15 managers. A lot of these managers do not shy away from volatility in their investment products, culturally, but the net results when you combine all these things together in an index is you end up with five vol. The CTA indices run it far higher than that. And what they’re putting into those baskets of 15, 20 managers, is even lower idiosyncratically. Yeah the, there is a ton of diversity within the commodity ecosystem. And when you think about these areas top down from an investor’s perspective, I think that’s exactly what you want is equality, right? You want a lot of different ideas. Now it makes your job a bit harder. You have to suss through that, and kind of assess people qualitatively in a relative sense. And that takes a lot of work, but there are I think you put it well, it’s a very heterogeneous space and, surprising no one, the space has raised some money in the last several years, and also of no surprise, there are a lot of new launches coming in there.

These markets are still, in some parts of the ecosystem, fairly small. There’s whole questions around capacity and where people fit. And, but there is at the same time, an incredible talent gap within commodities. I think the number of people trained to run these sorts of funds is very small, and I think that the jobs at the investment banks where these people traditionally would have been trained or started, they don’t really exist anymore. So while there are some launches, it’s interesting. I still feel like the space is in this odd situation of being undersupplied.

[00:35:15]Rodrigo Gordillo: And Chris, is the interest in this space, mainly just for that exposure and COVID and inflation, expecting a reasonable Sharpe ratio of 0.8 to 1, or are these managers, because of their level of expertise, able to provide Sharpe ratios above 1?

[00:35:32]Chris Kennedy: In cases they can but. I think the driver is always, when you’re thinking about these things top down, it’s just it’s always kind of correlation driven, right? And that’s the lesson that we always espoused at NewEdge and SocGen, but if you can get a positive Sharpe that’s uncorrelated, there’s always a role for that, right, especially if you can do it in scale. The argument in commodities is maybe you can’t do that at scale.

Maybe you can’t put 500 million to work very easily if you’re a pension fund, and yeah, that may make the space inaccessible to you, but there are plenty of investors who don’t have maybe that size and that sort of friction to manage, who can find places to put capital to work in very different ways there, where they can be quantitatively, relatively comfortable of their investment objective.

[00:36:20]Rodrigo Gordillo: We’ve spoken to guys at Campbell, we’ve spoken to people that have been in the industry for a while, very successfully, and their response is that there’s, you still gotta sell this. That there’s no, there’s nobody running out and putting out RFQ’s for this type of fund and en masse.

That it is something that’s very nichey still. Do you get that or are you plugged into a different type of audience that really wants this and is using it? It’s just, we don’t see it. We don’t see a lot of it anyway.

[00:36:51]Chris Kennedy: I would say we do. We must have access to a different network. I would say the demand for these investments is still, I think, broadly speaking, outstripping the supply now.

[00:37:02]Rodrigo Gordillo: Commodity specific though, not, commodity specific.

[00:37:04]Chris Kennedy: Yeah, absolutely. Yeah. That may be temporary. It may be structural. It’s hard to say the types of people allocating to this space, it’s definitely not there. There are some institutions, as I said, corporate pension plans, state pension plans, sovereign wealth funds. They’ve, the large investors still do have some long-term exposure here, I would say. We would say the category that we would use for the other types of investors in this is, they tend to be slightly higher velocity capital, right, maybe slightly more autonomous capital.

These would be family offices with a long history of investing in hedge funds. These could be the multi-strat platforms who have become increasingly active on an external basis. Anyone who can really get their hands around a managed account and can be comfortable there, which I think is a small part of the world, but is a part of it nonetheless, has been looking at this sector for sure.

[00:37:57]Rodrigo Gordillo: And are these managers also being fee compressed or are they commanding traditional hedge fund fees?

[00:38:04]Chris Kennedy: I would say their fee dynamics have been commensurate with their kind of relatively undersupplied, the relatively undersupplied state they find themselves in. So I think there’s been some, all fees have come down in hedge funds, but I’d say in that part of the world, there has been slightly less decompression.

[00:38:21]Rodrigo Gordillo: Sorry to interrupt, but I did want to take a quick second to remind listeners that while we do absolutely love providing our audience with world class guests and weekly investment insights, we wanted to remind you that we actually do our best work outside of this podcast. And we try to do this by providing cutting edge, globally diversified, and systematic investment strategies that are designed to be broadly non-correlated to traditional equity and bond portfolios.

So we actually manage private and public funds, as well as bespoke separately managed accounts for investors that seek the potential to smooth out portfolio returns in the long run. So if you do want to see that theory that we’ve been talking about put into practice, please do go ahead and check us out at www.investresolve.com. Now back to the podcast.

Broadening out a little bit again, you’ve got an institution who has come to you with little experience with alternatives. They say they want to build out an alternatives sleeve. What kind of guidance do you give them on how to think about manager selection? You alluded earlier to the fact that you acknowledge you can’t forecast which managers are going to outperform, right?

[00:39:37]Adam Butler: Say more about that, right? What are some of the qualities that may suggest that some managers or some strategies or what have you, may be better positioned to outperform in a space than other managers. I know you’ve been doing this for a long time. So I’m just wondering, and you’ve been doing it in a relatively small niche.

What have you learned over time about manager selection, and it doesn’t need to be forecasting performance. I’m happy to broaden that out a little bit more but just in general, what are some lessons that maybe a lot of people who are just coming into researching alternatives or active management in general may not understand, that you’ve learned through the course of your career?

[00:40:21]Chris Kennedy: Yeah. It’s, we were faced with that direct question many times at the bank, and we were not in, are not today consultants, right? Like we are not going to take someone’s portfolio and break it down into line items and do it at that level of granularity. The answer we had at the bank tended to be core satellite, right?

Find a part of the sector that you’re interested in, build some comfort there and try and access it for relatively low fees. And most often in futures you’re tying yourself to Trend. Trend is the risk premium that I think the space is most known for, arguably. It’s the one that people have the most confidence in and you can get it for relatively low fees today.

So if that’s the way into the space, great. From there, you can leverage existing infrastructure, or you can meet, there’s also products sometimes offered from the same firms, that are just very complementary from a quantitative basis. So you can look for short-term or macro, all these things are very similar in their ideology. If I would say what’s a little bit different here at Bridge today is that we tend to work with investors who have very strong processes already. They tend to know more than we do about what they would like from their managers, from a quantitative sense. What we are good at is assessing, like there is so much to look at, right?

There’s so many managers out there. What I think we have become quite good at is filtering out those who may be good, but may not meet some certain hurdles for some of these investors, and maybe, what are those? So some are quantitative for a contingent of the world, a one Sharpe, even on paper, is just not something that meets their kind of return on capital commitments.

And that’s fine. For others, they just want to know qualitatively, where do we think someone stacks up competitively? So if we know, maybe without, with a handful of exceptions, all the managers in some subsector of the commodity space, and we’ve talked with all of them at length, and we’ve gotten to know them, there will be some that naturally rise to the top in terms of what we think of their competitive edges. And this could be, what kind of data are they receiving? How sophisticated are their views? How able are they to adjust those views when they receive new information? Are they going to build the business, right?

Can they hire people? Can they bring in other traders around them? Do they have a good vision for the business, right? These are the kind of things that we think about when we are discussing with managers, and then, it’s funny the places where we get excited, it’s a lot of that stuff, the basics.

But then there’s also this general bucket we have for philosophy, right? We may need to meet a manager who is just doing something we never thought, of and it’s in those cases where, maybe someone is not necessarily adding a leaf far out on a known branch, but they’re creating a new branch entirely that really gets us excited.

And it’s hard to know what that looks like. And it’s hard to put very structured ring fences around what that may be, just given its own, given its nature you know it when you see it. I can think of a handful of meetings here where it would have never occurred to me to build something in that particular way. And you see it right there and it’s working, right? And it’s when you’re surprised, and then there is this data validation where I think, that’s when we get excited about the opportunity.

[00:43:42]Adam Butler: And what’s the mix of systematic versus discretionary? How do you think about forming portfolios? Or do you find that those styles are complementary? They thrive in different macro environments. What are the axes of consideration there?

[00:43:57]Chris Kennedy: Maybe the insight we can add there is I’ve been surprised people are surprised at how discretionary the commodity space remains. It is, and I would say institutions that invest there prefer often discretionary approaches rather than systematic. That’s complete 180 to other areas of the futures complex. I think the reasoning there probably has to do with how difficult it can be to model quantitatively what’s going on from a data perspective in that world. Anyone can come up with an S&D estimate for a particular instrument. But do you have a sense of maybe the non-normalities associated with those estimates and the tails associated with the different pieces of data that go in.

And there becomes a limit there. I tend to see discretionary managers in that world synthesize information extremely efficiently, and they get it from a ton of places. It is not uncommon for some of these groups to have fully automated paper trading systems, and maybe many of them, that they are using as input into the decisions that they ultimately make, right? They sit on top of all this information in a way that’s very impressive. In the systematic-only world, there’s absolutely things that we like to see from that part of the world. I think it’s very hard to be someone competing for high Sharpe’s in the systematic space today. Again, I think there’s interesting places to research and maybe places where that world is going that we tend to look for. AI is obviously on the table and we’ve seen some disasters and some success stories with those techniques. I think we’ll see a lot more there too.

[00:45:30]Rodrigo Gordillo: Yeah. So why don’t we talk a little bit more about that? Like that’s an area that obviously has been very popular in 2023

and it continued to grow in 2024. What, tell us a little bit about the, what your, what Bridge’s view is on the possible success of that area and who’s more likely to succeed there.

[00:45:49]Chris Kennedy: There’s a video on our website that I think everyone should go watch and I think it’s on YouTube as well. This was filmed at our Time Summit, which was our little research event that we did pre-COVID. And it was from Michael Brandt. Michael Brandt runs QMS, which is a large macro manager, and he basically walked people through why AI may be fundamentally challenged in financial markets. And I won’t give away the punchline, but he makes a very strong case why the signal-to-noise is incredibly low and why it may be difficult to ever really ascertain strong uses out of these very capable, but very flexible models that we have available today as financial practitioners.

I think there’s a lot of reasons why his arguments make a ton of sense. And it’s why, broadly speaking, the institution community that we have access to has been fairly skeptical of these ideas. Some of them have even been burned and trying to invest in a fund that does end to end deep learning, and it just doesn’t go well, and things don’t go as expected. It tends not to be a disaster. It just tends to not go well. Having said that, there are some interesting insights out there now that people think may argue the other side of that. So did you guys see Brian Kelly’s white paper? He’s at AQR, The Virtue of Complexity. That paper got me thinking a lot, right?

So maybe just to rehash what that is, we were talking earlier about the bias variance trade off and why that’s fundamentally difficult, why that kind of puts a limit on model complexity. There is this thing that people have been playing around with in the deep learning community called the kind of double descent issue in machine learning. So the idea there is as you increase model complexity, you do reduce your bias, which makes sense, but you start to increase your variance as you start to train on the noise, which is fine. That’s all, what we’ve all done. It turns out if you increase the variance, I’m sorry, if you increase the model complexity to the point where you have more parameters, and pieces of data, and you continue to go, under certain kind of setups, you can get to a point where the variance starts to reduce again, and the total area of your model also starts to reduce, even below that first descent. And intuitively this is, it just doesn’t feel right?

Like we’re trained to, and I think we are, in my seat, I am as well, I’m looking for the most parsimonious way to get to something complicated, right? Complexity is not always a good thing. But what I think Brian Kelly started to show people is, that may not necessarily be the case. And it clicked for me when he said, maybe the relationship that you are looking to model is just simply non-linear, right? It doesn’t mean it’s complicated, but it’s non-linear, right? It could be as simple as log of X, right? Your linear model will never capture that. And the only way that you’ll maybe find log of X is through these deep learning techniques.

So to be more specific, right, once you admit that maybe the world is no longer easily linearly model-able, right, like, maybe you’ll never find all those explanatory variables, or maybe you’ll never have enough of them to get confident in your linear model. It doesn’t mean that the relationship, though, is necessarily complicated. It could be as simple as that. But you need a model that can model arbitrary functions, right? And that’s when it clicked to me. If you buy into all that, yeah, there may be a simple relationship between the variable that you’ve chosen to look at and the price you’re trying to predict. But if it’s non-linear, you have to find what that is. It could be log of X, whatever. Again it’s very interesting and it makes you start thinking about how that would apply in our world.

And it’s no mistake that he’s at AQR and I think some of the big groups are looking at some of this research for sure. The question I would have is at the end of that, let’s say you end up with this deep learning model that is highly overparameterized, but really looks good.

Your test error is good and your train error is good. What do you have, right? Maybe it is a log of X thing, but once you’ve gone beyond linear state space, and you’re now in this kind of any function could work world. What are right? Like what is this thing that you’ve trained? And in some ways I wonder is the variance of the model that you have now trained this non-linear model that you have, could you be looking at something that’s one thing one day and something another, and have you really gained anything?

[00:50:25]Adam Butler: You mean from a style standpoint, for example?

[00:50:28]Chris Kennedy: 100%. Yes.

[00:50:29]Adam Butler: If you’re training on trend-like features and your response functions end up being counter-Trend, are you a Trend manager? Are you a mean reversion manager or are you just a quantitative, a quant, right? And I think increasingly quant means that you’re doing something in the non-linear space and, you’re not really going to look like any particular style most of the time, right? Because you’re seeking those non-linear relationships or complex response functions that are just going to look different at different times than, and they may look very different at times when the traditional style is working very well. And then you’re, people are scratching their heads.

You’re using Trend features. So why don’t you look like a Trend strategy or what, why do you have a negative correlation over the last 12 months? And that, that I can see that being very difficult from a, the industry is or do you still find that the industry is still very style box oriented, even if that style box is Trend or managed futures, is going to look all managed futures are going to look like Trend, but there’s going to be small differences and across funds that like our investors perceiving that, or articulating that to you at the moment.

[00:51:54]Chris Kennedy: Those labels totally matter, but I think the managers are moving away from them, broadly, like if you can find Trend from a number of places, but, whether it’s Winton or Two Sigma, many groups would rather call themselves a quant fund these days, and they would rather have that flexibility, especially in their higher margin product, right?

You can still get Trend from Winton, but it’s a lower margin product. It’s probably relatively more static compared to maybe their kind of unconstrained quant fund. That is absolutely the way the industry has evolved. And I think, yeah, there will always be someone to service the Trend world for sure.

But it seems to me that increasingly, model the world quantitative, quantitatively, these managers are looking for more flexibility. And what’s cool about that is you wonder if that will increase the diversity of kind of the net return streams. And I think that’d be a huge net benefit to investors that, there’s a lot of correlation in this industry and a lot of managers doing similar things, tying their hat to Trend. You do hope maybe that we’ll see a bit more different approaches, at least on a net return basis as a result of all this.

[00:53:04]Rodrigo Gordillo: What one does wonder what the reporting looks like for that. We certainly struggle with that as a multi-strat. People want to understand. It doesn’t matter how sophisticated the organization is, if you say you’re a quant fund and you’re not, if you have a Sharpe of one, one and a half, you’re going to have some rough periods, right?

[00:53:22]Chris Kennedy: Oh yeah. No doubt.

[00:53:22]Rodrigo Gordillo: Like it’s, you can still have a Sharpe of one and go through long and prolonged flat and negative drawdowns at that level. You have to be able to articulate what’s going on underneath the hood. It’s easy to do that with Trend. It’s easy to do that with Carry. But once you start getting into these non-linear states, reporting is huge.

Being able to explain what’s going on is huge. If you’re doing some sort of a non-linear regression that’s tree based, how many levels down do you want to go to explain what’s going on? How do you find reporting has evolved in the space? Or are people just saying, Hey, this is, it is what it is. We’re not going to tell you, you get what you get and you don’t get upset.

[00:53:59]Chris Kennedy: No, to circle back to that, to the original, like where has AI been used successfully? I think people have used it very, in very specific parts of their process, right? And almost all of it falls under the general idea that you’re filtering out bad ideas. And I, I don’t know why that tends to be the case, but I just tend to see people using these models, again, I think successfully to avoid bad trades. It just seems to be something that’s worked. They tend not to be there for idea synthesis, is I guess the other way to think about it. Do I think people are very transparent about what their model will do in different situations? Maybe not. I, there’s probably good work to be done there, but I do think people have been very open, at least in our experience, about their architectures, right? As I think, as people are working on more complicated infrastructures with some of these new techniques, the way that you put it all together may look quite different. And I tend to see the conversation, people aren’t sharing code, but they’re totally sharing how things are all put together to, to a fairly fine degree.

[00:55:02]Adam Butler: The other complexity, of course, is that there’s a trade off between reporting granularity and trading efficiency. So if you’ve got a variety of different strategies, you’re vastly better off trading them all in one account because you get all this trade netting, right? A lot of these high quality signals tend to be, as we already discussed earlier in the conversation, they tend to be higher frequency, and incur greater trading costs and trading frictions. So to the extent that you can aggregate a large number of these signals in one account, so that the signals are netting out to the maximum extent possible, then obviously that is, that accrues directly to the bottom line in trading costs that you did not incur, that you would have had if you were trading them all in different accounts.

If you trade them in the same account, though, then you can’t directly attribute a position to any particular signal or strategy. And for that reason, you can do approximations of attributions at the sort of signal level or model level, but you can’t do it at the actual fine tuned P&L level anymore, because every position you have on is informed by some ensemble or aggregation of a variety of signals, often that are operating at different timescales too, right? So there’s this trade off between wanting to have trade efficiency, but then also wanting to have transparency. Do you see that as well?

[00:56:33]Chris Kennedy: Yeah, I do. Transparency is always going to be difficult. Managed accounts help, but not every group can take on that operational burden. For those that can, you get a live look into how things transpire, and you can see if it matches up with kind of what you’ve been told. And I think transparency has always been a tricky thing. I think groups want all of it and then don’t know what to do with the information, or really struggled to digest that level of granularity.

[00:56:58]Adam Butler: Yeah, I guess I’m, I was trying to go to the point where even if you have individual trade level and position level transparency on what’s going on in the account with modern quant funds, there’s very little you can learn…

[00:57:15]Chris Kennedy: Oh, totally. I agree.

[00:57:17]Adam Butler: … about what is informing the bets that are being made because there’s so many different signals being aggregated, so many different timescales.

And therefore, obviously we publish position level attribution. I’ve never found that helpful and I find it completely unhelpful now, right? Because it doesn’t tell you at all about why those positions are on. And what’s really informative is, what are the strategies, but how the strategy has been performing, what have the, some of the strengths and weaknesses, what have some of the interactions been. These are really good ideas, but you can’t, it’s hard to publish those in a way that for example, the regulators can get behind, because you’re not actually using position level data or dollar level P&L. It’s at the one layer up, at the single level or at the model level, right?

[00:58:06]Chris Kennedy: No, I hear you.

[00:58:07]Adam Butler: Where it resides, but it’s not the layer that you can easily report on.

[00:58:11]Chris Kennedy: No, true. And I would say, yeah, my intuition is you couldn’t do a lot with that information, but I’ve never tried, and there’s some smart people out there, and that’s always the fear in this world. But no I agree with everything you said

[00:58:21]Adam Butler: Yeah, look, obviously we run Trend replication, right? Like, for slower moving strategies is an enormous amount that you can learn from position level data. And you obviously don’t want to expose that to everybody. But from an expectation standpoint, or how is this strategy going to slide in against another strategy from a correlation perspective or diversification perspective, or, setting forward expectations about performance, that sort of thing. There’s just, I think very little that you can learn. And even, there’s even very little that you can learn to tell the story about what happened to produce the gains and losses, in retrospect.

[00:59:00]Chris Kennedy: Agree.

[00:59:02]Rodrigo Gordillo: So one question that I try to ask people that have been in the industry for a while, and to the day I haven’t gotten a good answer, so this is going to…

[00:59:10]Chris Kennedy: Great.

[00:59:11]Rodrigo Gordillo: …separate it’s Medallion Fund and Jim Simons and the team, book’s been written about it, not very much in there. Do you have any thoughts as to how they’ve been able to do so well with so much money.

[00:59:23]Chris Kennedy: Yeah. I wrote a little blog called Medallion Isn’t Magic. You probably saw that, but…

[00:59:27]Rodrigo Gordillo: Oh, that’s right. A couple of years back, right?

[00:59:29]Chris Kennedy: Yeah, the, I have, the only little thing I have is someone once told me, and I actually think this is a huge piece of information, hugely, it’s very interesting, it tells you a lot if you think about it, but the average Sharpe ratio of the models in Medallion is 0.7. So the only way you get to kind of their performance, which, I’ve heard rumors of is not necessarily what it was still great, but not what it was, is incredible scale. And there’s a ton of problems that are associated with generating that many orthogonal signals at scale. And these are very technical problems. Knowing orthogonality is very hard, ex ante. I think they’ve solved some parts of that supply chain, that kind of process. Incredible detail, obviously, but I think that would be the key. They have to be doing, they have to have good predictors, not great. And they just have to have far more of them.

[01:00:24]Rodrigo Gordillo: So one of the things that has come up, and I’m curious to hear your thoughts on it, is if you look at a back-test with whatever signals that we were able to generate before trading costs and slippage, you get an 8 percent annualized return, more than what we would normally get, 8 to 12, right? And some of the rumors that I’ve heard are that they got themselves a sweetheart deal with their brokers back in the day where they were able to swap, do swaps with them at, that’s not, you’re shaking your head. So it’s not the fact that they were, they’re able to execute with very minimal slippage or trading costs. They’re paying the same as everybody else?

[01:01:07]Chris Kennedy: I think they have really great slippage, but I think that’s part of their edge, right? I don’t think that would be, no broker in the world would give them a sweetheart deal. That’s not what brokers do. Their edge would be, look, there’s two parts here.

They have to have good predictors. So that’s a whole complicated problem. That’s what we’re talking about there, but then you actually have to express those positions with $8 billion worth of capital at probably very short time horizons, or at least many time horizons. What you’re referencing is how in the world do they get that much capital to work? That’s an entirely separate problem. It crosses over in some places, but yeah, that I think is less shocking. They solved, I think other people solved it, but they probably have a very good set of predictors as well.

[01:01:54]Rodrigo Gordillo: I also hear that they can get, because they’re so diversified, the amount of leverage that you would need, most people, most banks wouldn’t give them. Look, us right, if we wanted to do that level of leverage, and they seem to have connect, enough connections where they can get more leverage because ultimately the absolute returns are what matter to money in pocket. And a lot of people can have high Sharpe ratios with very low vol using 10x leverage. I wonder if they can get more leverage than the average bear.

[01:02:23]Chris Kennedy: Quite possibly. The structure would have to be unique, if they’re trading intraday, they don’t have to hold or at least for part of it, they don’t have to hold margin on those positions. And there may be some ways around it, but yeah, I don’t think there, I don’t think the industry is there. There’s such a giant jump from maybe, who’s also very good to Renaissance, as maybe the industry perceives, I think. There’s other very strong futures traders that may not be quite as large, but that are similar, and I don’t think there’s many of them, but the model is not completely unique. Again very hard.

[01:02:57]Rodrigo Gordillo: That’s a whole different podcast. We’ll go offline and talk about every single one of them.

[01:03:01]Chris Kennedy: Sure.

[01:03:03]Rodrigo Gordillo: All right. Chris, this has been a very informative podcast. Thank you for coming and joining us and sharing your thoughts. Anything you’d like to leave us with where people can find you and your team.

[01:03:15]Chris Kennedy: Yeah, bridgealternatives.com. You’ll find out a bit about this there. You can find me on LinkedIn if you just want to chat. We’re always open to meeting people, especially people looking to start funds and want advice and just want to meet someone who can give them a bit of feedback. We’re always open to that.

I’m going to try and write a little bit more this year, so hopefully follow up on that commodity piece and do a couple others. It was really fun though, guys. I really enjoyed my time.

[01:03:39]Rodrigo Gordillo: Fantastic.

[01:03:40]Adam Butler: What a great way to kick off the year, Chris.

[01:03:43]Rodrigo Gordillo: Thanks Chris.

[01:03:45]Rodrigo Gordillo: Sorry to interrupt, but I did want to take a quick second to remind our listeners that the team works really hard on these podcasts. We spend a lot of hours trying to get the right guests and we do a lot of prep work to make sure that we’re asking the right questions. So if you do have a second, just do hit that Subscribe button, hit that Like button, and Share with friends if you find what we’re doing useful.

Thanks again.

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*ReSolve Global refers to ReSolve Asset Management SEZC (Cayman) which is registered with the Commodity Futures Trading Commission as a commodity trading advisor and commodity pool operator. This registration is administered through the National Futures Association (“NFA”). Further, ReSolve Global is a registered person with the Cayman Islands Monetary Authority.