Benjamin Brodsky – Rewiring Credit with Science

00:00:07 [Benjamin Brodsky]
We believe the opportunity is tremendous. Developments like this occur only once or twice in a generation.
00:00:14 [Vincent Weber]
A lot has changed in the credit market over the last two decades. How we access them, how we trade them, and more recently, how we analyze them. What used to be driven largely by human judgment is now being reimagined through the lens of data and technology. My guest today is someone who's been right in the middle of that evolution. Benjamin Brodsky is Chief Investment Officer at BlueCove, a firm that's taken a fresh approach to credit investing. One that's systematic, scientific and built entirely for this space. Ben, welcome and great to have you on the show.
00:00:52 [Benjamin Brodsky]
Thank you, Vincent. I'm really happy to be here.
00:00:56 [Vincent Weber]
Thanks. So we'll get into what scientific credit really means, how BlueCove came to be, and what it takes to build a strategy and a firm around this approach. So let's start with how it all began. So Ben, what led you to leave the big machine at BlackRock to build BlueCove from the ground up? What was the motivation behind starting something new and why focus specifically on credit?
00:01:27 [Benjamin Brodsky]
These are two really big questions to start off with. Obviously, we have been doing this here now at BlueCove since 2018, but after years at BlackRock and before that, importantly to mention, at BGI as well, where we launched our first systematic credit strategies in 2004 already. We saw firsthand how scientific approaches had reshaped equity investing and bringing scale to that discipline and obviously strong outcomes as well. We started building systematic fixed income models in the 2000s, initially in corporate credit, but then also across the entirety of the liquid fixed income spectrum. We were inspired by BGI's equity experience that had started in the 90s. And that was really the starting point. Now, clearly fixed income remained years behind equities. That's not a secret.
Despite the growth in data and electronification, most credit investing was still discretionary, highly siloed and driven by a few individuals, really, rather than a holistic team approach. So we saw this firsthand at our old company. increasingly, we believed fixed income was ready for emancipation. But to unlock that potential, we had to start from scratch. We believe the opportunity is tremendous. Developments like this occur only once or twice in a generation. And we believe that an independent, specialized, purpose-built firm is the best way to exploit it. So there were mostly pull factors at work here, I'd say, though there were probably also some push factors. But in 2018, we found a blue curve with a single purpose, really, to build the first truly scientific credit manager.
And as you have already suggested, purpose-built, independent and unconstrained by legacy systems, culture or processes. We chose credit because it was more inefficient, more fragmented and less competitive than equity, for instance, making it ideal. ground for systematic alpha, but it also required a different set of tools and deeper domain knowledge. And that's why going independent wasn't just an entrepreneurial leap. It was a necessity in our view to build what we knew the market needed. And that was a scientific credit platform built from the ground up, basically.
00:04:08 [Vincent Weber]
Right. Benjamin, you just mentioned briefly the holistic team approach. So talking about that, you brought together a pretty diverse group of quantum technologies traders. So what kind of culture of collaboration do you aim for inside your firm?
00:04:24 [Benjamin Brodsky]
Yeah, that is true. Scientific credit isn't really something that you can improvise. It demands a convergence of disciplines. So fixed income expertise, obviously. quantitative research, data science, technology infrastructure. Our team reflects that blend. Many of us have worked together at BGI and then at BlackRock over the past 20 years now, and we've deliberately built a firm around domain debt collaboration and, very importantly, intellectual integrity as well. We are structured as one unified investment team, not in silos. Researchers, engineers, and portfolio managers work together daily, not in isolation. That allows us to iterate quickly, integrate insights faster, and manage the complexity of credit markets with more rigor, I would say. Every person at BlueCove is an equity owner. That shared alignment fosters long-term thinking and a real sense of collective responsibility.
Culturally, we have built something quite distinct, a flat, transparent environment where Challenges encouraged and ideas are stress tested. That mindset is as important as the maths, really, if you think about it. Scientific credit isn't just about algorithms. It's about building a research culture capable of evolving in step with the markets.
00:05:51 [Vincent Weber]
So for those who might be less familiar, how do you define scientific or systematic credit? And how is it different from traditional discretionary fixed income or from systematic equity?
00:06:04 [Benjamin Brodsky]
Well, scientific credit is about applying the principles of the scientific method. That means hypothesis testing, iteration to every stage of the investment process. We break the traditional discretionary process into its component parts. So that's universe curation, signal design, portfolio construction, execution, risk, process review, each component. is modeled, measured, and constantly improved. With respect to terminology, and that's probably worth mentioning here, most people call it systematic credit, and that's fine too for us. We don't want to be dogmatic about the term as such. Just want to highlight that the application of the scientific method to credit markets is key here. You can be systematic or quantitative without really being scientific about it.
Unlike discretionary credit, where decisions often depend on a few individuals and qualitative judgment, scientific credit builds a system that combines both domain knowledge with qualitative scale. For someone used to quant equity, this approach might feel quite familiar, except credit is more complex. Issuers have multiple bonds, liquidity varies widely, and data is noisier. and historically less reliable, creating traps and plenty of opportunities for data mining and in-sample fitting. Now, the benefit is repeatability and decision breadth. Our scientific credit doesn't rely on intuition or star managers. It relies on process. We test what works and what doesn't. We monitor every single insight, every trade, and attribute performance precisely. That transparency leads to higher conviction and to continuous refinement and improvement.
For allocators specifically, scientific credit is a diversified return stream, lowly correlated with traditional managers, disciplined through cycles and built for scalability across the entirety of the liquid credit universe. We are breadth investors, Vincent, right? And can apply our insights to a very large universe at any given point in time, the fact that we operate in credit assets also makes us uncorrelated to systematic equity investors.
00:08:30 [Vincent Weber]
Right. Thank you, Benjamin. You mentioned universe and being a brisk investor. I think this is a very important point. So how much of the credit market is actually addressable for a systematic strategy today?
00:08:44 [Benjamin Brodsky]
Well, the investable universe for scientific credit has grown very heavily, so it has exploded really in the recent years. Growth in the investable universe is significant and the total face value of liquid global credit sums up to about $16 trillion or so equivalent. This has risen from just over $12 trillion at the end of 2019 and the number of bonds has expanded with that, of course. This is one way of looking at the opportunity set for us. Today we cover over 20,000 bonds and around 4,000 issuers across developed and emerging markets. That includes investment grade, high yield corporates, as well as credit derivatives like CDS securities. And it's growing every year. Now, the liquidity is not uniformly good among all of those. And this is an important issue, of course.
But we will get into this a bit later, I expect. Now, this expansion is being driven by three major trends. First. Corporate issuance has surged globally, especially post the global financial crisis. This is obviously a function of corporate financing needs, financial conditions and growth opportunities. Second, electronic trading has gone mainstream. More than 40% of US IG credit now trades electronically and that number is rising in high yield as well from currently somewhere in between 20 to 30% or so. We, as a scientific investor, trade over 80% of our tickets electronically. For us, this is the major way of scaling our implementation. Now, third, data quality has improved dramatically through trace and portfolio trading, as well as dealer RFQ platforms.
Now, this matters for scientific credit because it increases breadth, lowers transaction costs, and improves the precision of our models. A larger, more liquid and more transparent universe allows us to deploy capital efficiently across more securities and to do so with a higher degree of control, I'd say. The scale is there and the ecosystem evolves through ETFs, clearing and also all platforms. It's making systematic credit not just possible now, it is really making it increasingly optimal in our view.
00:11:08 [Vincent Weber]
Right. So, scientific investing is very common in equity. It is a very long tradition. So, what do you think is being slower to catch on in credit?
00:11:21 [Benjamin Brodsky]
Well, that's a good question. Now, I think the short answer is credit is hard. It's complex, fragmented, historically opaque. Most bonds don't really trade daily. Pricing is decentralized. And unlike equities, where you have one security per company in credit, as I've said a bit earlier, you might have many, each with different terms, risks, liquidity. Systematic equity matured decades ago, as you said. Tools are widely available. Data is clean. Markets are decentralized. But in credit, firms face massive barriers to entry, you need bond-specific risk models, default estimation, execution modeling, issuer mapping infrastructure. Most equity quant tools just don't translate into credits. And it's not just technical, it's also cultural in our point of view. Now, building a scientific credit platform takes long-term investment and cross-disciplinary talent. Talent is very scarce in this space.
as neither equity knowledge nor fundamental credit experience are easily applicable to it. Many firms tried to bolt this onto existing equity or discretionary fixed income setups, but without the right foundation, it's hard to make it work. And that's why we built Bluecore from the ground up. We knew this wasn't an incremental improvement. It was a ground-up re-architecture, really. Few have the patience, alignment, or conviction. I would say, to do this. this is why this space for us now still remains very uncrowded.
00:13:11 [Vincent Weber]
Right. So let's get into the details of what you're doing and maybe start a bit with your universe. So how do you decide which instrument makes sense to include and which to exclude before your research even begins?
00:13:31 [Benjamin Brodsky]
Well, this is a really important question. Our starting point is quite ambitious here, I would say. We seek to systematically analyze and model as much of the global liquid credit markets as possible. So today we include, as I said, over 20,000 individual bonds across over 4,000 issuers. And this includes investment grade, high yield, credit derivatives, financials, industrials. You name it. But not every bond qualifies for inclusion in our investment universe. Scientific credit depends not just on breadth, but on quality of inputs, model reliability, tradability as well. So we apply a rigorous filtering process across. Three key dimensions, I would say. First of all, data quality. So we require consistent and accurate data on pricing, spread, liquidity, and issuer fundamentals.
If the data is missing, stale, or unreliable, the bond is excluded from modeling and hence from the universe. You can't really build a systematic strategy with noisy inputs. Second is liquidity and tradability. We only include securities that meet our thresholds for market activity, bid-ask spreads, trade frequency. Bonds that trade too infrequently or are subject to wide, unstable pricing are not suitable for systematic execution. So we also assess whether those trades can be executed at scale with reasonable slippage. There's no point if we can only trade in $100,000 clips. So the last one is model fitness. So even if a bond has data and trades regularly, we ask, can we reliably model this? Are our skills appropriate to trade that sub-portion of the universe? Can we compare it fairly to other bonds?
If not, perhaps due to unusual structure or capital treatment, it might be excluded until conditions change, for instance. Now, additionally, Our universe is quite dynamic, if you think about it, right? It updates daily to reflect maturing bonds, new issuance, shifting liquidity or data improvements. If a bond that was previously excluded improves in liquidity or data quality, it can re-enter. Conversely, if a bond that's trading dries up or spreads become erratic, it may be pulled out of the scope of our of our modeling efforts so so this curated evolving universe is really essential it allows us to maintain broad coverage crucial for diversification really without sacrificing precision or robustness of our modeling and because credit markets are constantly changing we've built the infrastructure to adapt automatically ensuring our research pool reflects the live investable market not
like the static snapshot that then we automatically bank on, basically.
00:16:34 [Vincent Weber]
So once you have a research idea, how do you go from testing it to actually putting it to work in your live portfolio?
00:16:45 [Benjamin Brodsky]
So this is, I would say, generally a bit of a lifecycle approach. Every new signal begins with a hypothesis that could stem from a behavioral anomaly, for instance. that we have observed, a structural feature of credit markets, or even a question from a researcher or portfolio manager. The insight needs to be sensible rather than just driven by data inquiry, for instance. If we can't explain why it should work before we start testing, the process should actually finish right there. Now, next, the idea is subjected to rigorous testing, both in backtests and out of sample environments. Beyond economic rationale, we are looking for statistical validity, durability across time and regimes, as well as additivity to other existing insights that we already have in the models.
Now, once validated, the signal enters something that we would call a sandbox, where it's monitored in parallel to production. We study its behavior in real markets, including turnover, capacity, interaction with other signals. Only when it meets our threshold for contribution or stability do we incorporate it into our live portfolios. Even then, though, signals are constantly monitored. We run performance analytics. such as decay analysis and correlation checks. If a signal underperforms inexplicably or over longer periods or its rationale gets increasingly questioned by the team, it will be reweighted or even retired. Now, this disciplined process from research to rotation is how we ensure that every position in the portfolio has a reason to be there, is grounded in data and continuously re-evaluated.
00:18:43 [Vincent Weber]
Right. So when you find something that works, how long does it tend to stay effective? And how do you deal with that risk of a signal or the signal's alpha decay?
00:19:00 [Benjamin Brodsky]
Now, one of the advantages of systematic credit over more mature spaces like equities, for instance, is that alpha signals tend to fade more slowly. That's largely because the credit market is still relatively under-exploited from a systematic perspective. Many of the inefficiencies we target, such as mispricings between bonds from the same issuer, for instance, or slow-moving reactions to fundamental changes, exist due to structural and behavioral frictions and aren't easily arbitraged away. That said, No signal lasts forever, clearly. Market conditions change, data availability improves, and other participants begin to respond and enter the market. So we manage the entire signal lifecycle actively, from discovery to deployment to retirement. We monitor performance degradation, capacity erosion, and cost inflation as well on a real-time basis. Signals are attributed daily on effectiveness, stability.
and contribution to portfolio diversification, if one begins to underperform or becomes too costly to implement, for instance, it's either scaled back or removed entirely. We also run continuous research to feed new ideas into the process. So this ongoing renewal ensures we aren't relying on any single edge. We also believe in a broadly diversified. signal set that helps steady performance and avoid prolonged drawdowns. And that helps us maintain a dynamic, forward-looking portfolio that evolves with the market rather than chasing it, I'd say.
00:20:51 [Vincent Weber]
Okay. So let's talk about leverage because that's the topic that always comes up when we talk about systematic strategy. So how do you approach that?
00:21:04 [Benjamin Brodsky]
Yeah. Leverage is obviously an important topic for our clients and as well for us, of course. So leverage, I would say when used responsibly, allows us to scale diversified market neutral portfolios that isolate alpha from beta. So in our case, leverage is used not to magnify directional risk, but to balance long and short exposures while maintaining neutral. Exposure to the broader credit market overall. The more factor exposures are removed, the more leverage can be taken up. Obviously, only up to a certain level, quite clearly, of course. Leverage needs to balance the need for return with the need for safety. I think that's a very important point, especially in very volatile markets when this leverage becomes more expensive to hold, given the higher cash needs, should more collateral be required.
Credit leverage cannot be ramped up or down as easily as exposures in more liquid markets, such as equities or futures, for instance. It is comparatively more expensive to trade. We never want to be forced to de-lever as this would impact performance. So we must strike that balance right. The aim is to run our leverage consistently through both volatile and relatively tranquil periods. Being market neutral in credit means hedging out exposure to spread duration, interest rates, and sector tills. So performance is driven by relative mispricings across bonds, not macro movements. The latter, so the macro movements, are more more difficult to model and tend to have lower information ratios. And we try to avoid that. So we model leverage very carefully, including stress testing, liquidity impact and transaction costs, for instance.
Risk is controlled at the security portfolio and strategy level. We also monitor funding markets and collateral dynamics closely. Ultimate leverage is a means to a risk-adjusted outcome, right? We are optimizing for risk-adjusted return. not a goal in itself. We believe transparency and process discipline are key to the comfort here for allocators.
00:23:27 [Vincent Weber]
Right. So credit markets are evolving quickly. How much of a difference has electronic trading made in terms of execution quality and transaction costs?
00:23:40 [Benjamin Brodsky]
Liquidity has long been one of the key challenges in credit markets. and is one of the hardest problems, I think, to solve. But equally, it presents one of the main opportunities for systematic investors in this space. Unlike equities, corporate bonds don't trade on centralized exchanges. Many trade infrequently, as I've said before, and price discovery still depends on RFQs and dealer networks. Historically, that made systematic investing more difficult. But over recent years, the landscape has changed quite dramatically, I would say. We have seen a steady rise in electronic trading, especially in investment grade markets where over 40%, as I've said earlier, of volumes are now electronic and high yield is catching up too. We execute well over 80%, as I've also mentioned, of our total number of tickets electronically.
Now, depending on your choice of universe and trading sizes, one could easily imagine this to be 100% soon. Portfolio trading, credit ETFs, and all-to-all trading platforms have helped standardize execution and tighten bid-ask threat overall. This has significantly improved transparency and made implementation far more scalable for systematic managers. the average traditional discretionary manager would hear. In addition, electronic execution produces important data to feed back into our investment process. So it helps our modeling overall. We retain all of the trading information, of course, historically. Now, at BlueCove, liquidity isn't just an operational consideration, right? It's embedded into every layer of the investment process. We model it explicitly using bond characteristics, historical trade data, observable bid offer spreads, deal inventories, trace volumes, a frequency of trading across issuers and specific bonds.
Every bond gets a liquidity score, which feeds into signal design, portfolio construction, and risk management as well. We also simulate the expected cost of entering and exiting a position at different sizes and under different market conditions. I think the later point is really important too. Obviously, transaction cost and liquidity is time varying, right? Both liquidity and trading costs can change quite dramatically depending on what is going on in the market overall. And they are a factor, obviously, of market vol. If a trade looks good on paper but isn't realistically tradable, it shouldn't end up on a trade list. I think that's quite straightforward to understand. Now, ultimately, bond liquidity is central to the modeling process and is a dynamic feature of the market that we actively model, monitor, and increasingly use to our advantage.
Now, that's part of what allows us to scale scientific credit responsibly, even in less forgiving. Market environments, if you think about it, like April of this year, for instance, or the COVID period, 2022. Obviously, when you have those liquidity challenges, that's a very, very important aspect that we take into account.
00:27:22 [Vincent Weber]
Great. So Benjamin, you're a quant, so you won't escape a few questions about AI. So everybody's talking about it. So where does it actually add value to your investment process? And what's your take on it? Is it more hype than actual value?
00:27:46 [Benjamin Brodsky]
Obviously, AI. You can answer this question in many different ways. Let me say that AI is Certainly one of the most hyped themes in finance today. I mean, I think everyone recognizes this. But in scientific credit, we see it as a set of practical tools rather than a silver bullet. So the key is knowing where it adds value and where the challenges are still significant, I'd say. So many bonds have limited trading history. Defaults are quite rare events and overfitting is therefore a constant danger. At BlueCove, we think of AI along a gradient of complexity. Now, at the easier end of the spectrum, we use AI for efficiency gains in research, for instance, such as automating internal processes like software delivery or the acceleration of research workflows. These are immediate real-world benefits.
They're quite deterministic in nature, right? They don't involve any form of forecasting. We also can use it to support raw data processing, filling in missing data points, spotting outliers, improving data set quality and so forth. Now, for example, we might impute volatility data using peer group behaviors and peer group information. This is an important benefit after all. Clean data is the foundation for all good modeling. So here it can be very helpful. Where it gets more sophisticated, I'd say, is natural language processing. Now, we've experimented with this now for over 10 years or so. We can extract sentiment and themes from credit rating reports, earnings calls. That kind of unstructured data is rich in information, but historically quite hard to model.
Now, with NLP, we can systematize what used to be purely discretionary judgment before where analysts had to meet management. And the precision has, of course, gotten much better and the accessibility has gotten better. I think everyone can experience this in their own interaction with AI tools. Now, harder still than that are applications like modeling market dynamics, so proper forecasting. estimating the fair value of a bond spread or forecasting transaction costs and return forecasting, obviously, based on current information. Now, these are high rewards, but also high risk areas where overfitting is a real danger. We have to approach them very cautiously, always combining machine learning outputs with human oversight and intuition, domain intuition of our researchers and PMs. What's the key takeaway here? Well, AI isn't clearly magic, but you have to use it thoughtfully.
It's a really powerful way to enhance what we do, but our goal isn't to replace judgment, but to systematize judgment. We have to back that by testing transparency and obviously economic logic as well.
00:31:08 [Vincent Weber]
Thank you. So let's take a step back. What are the key drivers of performance in your strategy? And what kind of environment really plays out to your strength?
00:31:23 [Benjamin Brodsky]
Now, in scientific credit, performance is driven by the consistent application of high-quality, economically grounded signals across a broad universe of securities. We're not trying to predict where spreads are going. Instead, we are asking, which bonds are mispriced relative to their risk, relative to their fundamentals and technical drivers, for instance, right? While the structural factors I've previously discussed, such as increasing electronification of trading, offer long-term improvements in performance, the key return driver is cross-sectional dispersion. And this is what I mean over the shorter term, right? That means the variation in credit spreads between issuers and instruments that we use. When dispersion is high, it means markets are differentiating more aggressively in between different credits. That's ideal for us. Why? Because our models are designed to identify and exploit those relative mispricings.
The more differentiation in the market, the greater the opportunity for alpha. In fact, Our return profile has a kind of positive convexity to dispersion. That means when dispersion increases, even moderately, our opportunity set expands non-linearly. The spread between the cheapest and richest bonds gets wider, making signal strength more predictive and the cost of trading more justifiable. Simply put, our signals just work better, our Sharpe ratio improves and our capacity to deploy capital effectively increases during those periods. Now, critically, the short side of the portfolio becomes a major return contributor in those periods. We haven't talked a lot about the short side. Obviously, I've mentioned in the beginning, we are market neutral investors, but having a well-developed large short side in those volatile periods is very powerful.
While in low vol environments, shorts can have less of an impact. In stressed or volatile markets, the gap between strong and weak credits widens dramatically. Our models tend to identify deteriorating issuers early, and when volatility spikes, those names underperform sharply. That makes our shorts, not just our longs, significant alpha drivers over time. This is a key distinction, Vincent, from many of our traditional discretionary competitors. Traditional hedge funds can suffer in market drawdown periods because they typically run significant amounts of excess carry and that gives the market directionality. Our market neutral positioning and heavy use of the short side provides important benefits in these market environments and present quite clear distinguishing features to many other funds. Conversely, obviously, And there's always a downside when dispersion is low, like in tight, complacent markets, for instance.
It becomes harder to distinguish relative value and alpha compresses as well. So the opportunity set is smaller. In those environments, we scale risk more conservatively, lean on higher confidence signals and reduce turnover. The models reduce turnover automatically in those periods because alpha opportunities shrink. While we are designed to function in any market regime, we tend to outperform in environments where markets are stressed, fragmented, or adjusting rapidly, for instance. Conditions where traditional credit managers quite often struggle and where scientific investing shows its real edge in our view.
00:35:11 [Vincent Weber]
So Benjamin, you've been at this for a while now. So how has the conversation with institutional investors evolved since you launched?
00:35:23 [Benjamin Brodsky]
It has evolved quite a bit. So if I remember five years ago or so, most conversations were quite introductory, right? So questions that were asked were, what is scientific credit? Can it actually be done? We spend a lot of time explaining the difference between what we do and discretionary fixed income or quant equity, for instance. I mean, many people were obviously much more familiar with that. So today we are past the basic education phase. Allocators are now asking deeper, more technical questions. How are signals constructed? What's the capacity of your approach? How do you handle liquidity stress? How does it fit in a portfolio alongside traditional credit? Or how can I combine your long-short strategies with long-only credit approaches, for instance? So people look much more at those deeper. deeper issues and questions.
So this shift reflects growing comfort, I'd say, and growing demand. An increasing number of allocators are exploring the asset class and more managers are working on their own systematic credit strategies. I mean, since we have launched, for sure. We welcome that, of course. I mean, we want competition as this is a clear indicator of increasing interest, obviously. Institutional investors want scalable, diversified sources of alpha. They've seen what Quant has done for equity portfolios, and they're now looking for the same in fixed income. But it has to be well understood as it is. As I've explained, difference to Quant's equities in many important ways, but that's the beauty of it. And it's up to us to make sure those differences, advantages, complementarity are clear to allocators via. education, and very importantly, increased evidence of this approach.
Now, the momentum is real, that's for sure. And it's encouraging to see that many investors now see scientific credit not as an experiment any longer, but as a core building block of the future fixed income allocations that they have.
00:37:34 [Vincent Weber]
Right. So do you see scientific credit becoming mainstream? mainstream building model portfolio like it's the case already for QuantEquity?
00:37:47 [Benjamin Brodsky]
Yes. And it's already starting. So QuantEquity took time to move from niche to core. But once the infrastructure, talent and track record were there and in place, adoption accelerated clearly. We see the same pattern unfolding in credit. We have over five years of track record now at scale and people get more comfortable with the concept and develop an intuition. for return behavior as well. They increasingly start speaking our language and that is very exciting for us to hear. It is important to repeat the core messages and evidence what we say. Now, scientific credit offers uncorrelated alpha, transparency and repeatability. It's not about market timing or economic forecasting. It's about systematically harvesting. inefficiencies that makes it a powerful diversifier and a complement to credit beta and other active discretionary strategies.
Now, as more managers prove their ability to deliver in this space and as allocators grow more comfortable with the tooling and techniques, we believe scientific credit will become a foundational part of institutional fixed income portfolios, just like one equity did in the early 2000s or so. It won't replace discretionary credit, of course not, but it will sit alongside it, bringing process, discipline, scalability, and very importantly, resilience as well to portfolios.
00:39:14 [Vincent Weber]
Right. But Benjamin, with every new approach, there's often skepticism. So what are the most common concerns or misconceptions you hear from allocators?
00:39:29 [Benjamin Brodsky]
I think those have changed a bit over time too. I'd say the three most often recurring questions from prospective investors, and they're thoughtful in nature, really, are really first scalability and crowding. So we hear this very often. Investors want to know, can the strategy scale without degrading returns? Obviously, an important topic. Also, what happens if too many players pile into the same trades, for instance, right? So that's a justified concern in general. Now, our answer to this is twofold, I'd say. So one, credit is still vastly underpenetrated by systematic investors compared to equity markets. I think that's an important point. And two, our strategy is designed to avoid concentration.
We operate across a very broad universe, as I've mentioned before, and our signals are proprietary, diversified, and constantly refreshed capacity is actively managed through real-time cost and liquidity modeling. And the important point really here is to consider that systematic credit is still a tiny fraction of overall credit assets under management. Now, the second point that clients inquire about is execution. Credit is less liquid and more fragmented than equities. Allocators rightly ask how we can actually implement trades. We would describe it as what's your transfer coefficient like? Do you get the majority of your trades into the portfolio? And that has shifted massively through time. We always give the example of our Our high yield transfer coefficients used to be somewhere between 50 and 75 in the early 2000s.
Now we are running them in between 95 and 100. And that's because obviously we have learned a lot over the years, but also because markets have become more transparent. Obviously, we model bond level liquidity and transaction costs directly into the portfolio construction process. So we use techniques like bond substitution and portfolio trading to reduce slippage and maintain. efficiency, even in very volatile markets. And so that implementation has improved beyond recognition, really, I would say. Now, the third important question I think that we get is a very thoughtful one. And it is, can this strategy still work if dispersion stays?
permanently low right and that's obviously on on investors mind i always say well if dispersion stays permanently low and markets don't correct then might as well just be long the market there's no there's no real need for anything else obviously and that's something that you can have at a at a very low price you don't need to you don't need to buy an alternative product for this now the short answer though is that we can definitely deliver in low dispersion markets. So the real answer is that we adapt to them. Our return potential, as I said, is positively convex in volatile periods, but we design our signals to function across regimes and scale risk exposures accordingly. We have also increased the number of strategies substantially since we launched.
We launched with six strategies, and now we are approaching 14, essentially covering the entirety of the global liquid credit universe. More strategies are always added. We have added convertibles last year. We're adding US convertibles. We're adding European convertibles this year. The goal is consistency without altering the unique return profile that the strategies have to offer. I'd say these concerns are smart concerns and we welcome that form of questioning, right? Clients need to gain comfort with this. They reflect a growing sophistication, I think, among many allocators evaluating systematic credit and they give us the opportunity to demonstrate just how deeply and deliberately we've built our platform to address them.
00:44:04 [Vincent Weber]
Great, thanks. So systematic investing in credit has come a long way. But in many ways, it feels like we're still just getting started. The tech is improving, the data is broader, and the understanding from investors is deepening. Benjamin, thanks so much for sharing the journey you and the BlueCove team are on. It's exciting, it's ambitious, and it's clearly working.
00:44:28 [Benjamin Brodsky]
Thank you very much, Vincent. It's been great to be here today.
00:44:33 [Vincent Weber]
Thank you, Benjamin. So you can find more insight and resources at resonancecapital.com and don't forget to subscribe to stay up to date with future episodes. I'm Vincent Weber and this was the Resonanz Spotlight. See you next time.