Asif Mohamed: 

Hi, I'm Asif Mohamed from Global Research, head of digital product.

 

Sam Azzarello: 

And I'm Sam Azzarello and I lead content strategy for global research.

 

Asif Mohamed: 

So Sam, how are you approaching digital research content to make it more impactful for our clients?

 

Sam Azzarello: 

Well, I think at the core, it's about meeting our clients where we are. So on our digital channels, we like to think omnichannel. That could be through email, through our portal, JP Morgan Markets, it could be through a podcast on Apple and Spotify, or it could be on LinkedIn or through client interactions. So there's many different ways that we think about getting our content, our best in class content to our client in a form that they want and meeting them where they already are. So Asif, I know we invest a lot. We do a lot to make sure we're at the forefront of what our clients need. Are there any recent tools that we've created to help our clients access our content?

 

Asif Mohamed: 

Absolutely. There are lots of technological innovations that are happening across the industry, and we've been leveraging AI, the cloud, and data in many ways to deliver content to clients in the way that they want to receive it.

 

Sam Azzarello: 

I know we've been doing things in AI. What can you share in terms of how AI is changing the global research model?

 

Asif Mohamed: 

We've been using AI for quite a while. We've been using it in macroeconomics. Our economists use AI to understand what central bank speeches are all about and when they're sentiments that are changing on a daily basis. We use it to identify thematic information in the news that can help our clients invest and generate more alpha. And we use it to understand how companies, sectors, and countries can be affected by thematic news on a daily basis.

We're also using AI in search. Search is a challenge, has been a challenge for the research industry for a while, and it's because keyword search doesn't give you the contextual value that folks are looking for. And so we're using AI to better search, scan across lots of information, decades of research, both written and data to be able to synthesize that information relatively quickly. And so that's a game changer, we think.

 

Sam Azzarello: 

Really valuable, I would add, right? AI is facilitating semantic search. And for those who are maybe not as familiar, it's this idea I can search DC or US policy or Washington and get all the content that I need no matter what term is perhaps mentioned in the report.

 

Asif Mohamed: 

That's correct. That's a contextual search, semantic, and we're able to generate insights faster, and that benefits the clients. It also benefits our analysts who are looking for information in lots of data.

 

Sam Azzarello: 

Also interesting to note that our analysts are using AI, like you mentioned, economics reports, sentiment coming out from central bank governors, different speeches that we're hearing. We're using AI to do sentiment analysis in a very innovative and value add way for clients.

 

Asif Mohamed: 

That's right. We've got the central bank product that looks across all of the central banks and their statements and analyzes it for whether it's hawkish or dovish sentiment coming from those central banks. So Sam, how do we meet clients where they are?

 

Sam Azzarello: 

Situations arise and we want to be able to bring our insights to clients and meet them where they are. So when tariff news broke earlier this year, we set up a webcast right away for clients, answering their questions. We had a podcast. We pulled together content from research analysts all across the globe, cross asset into one piece. And we had an omnichannel approach to how we brought these insights to our clients.

 

Asif Mohamed: 

And how are these research products changing and adapting to market conditions?

 

Sam Azzarello: 

Well, I always think our research analysts have to react to the news, but at the core of it, they're providing processes and frameworks and ways of thinking to our clients because our clients are building out a mosaic of a view, and JP Morgan Global Research is one input in that. So to the extent that our analysts with deep expertise and very deep knowledge can provide a way of thinking to clients and investors, I know that that's very valuable and it allows it to be adaptable for clients no matter what really happens with the news and how things change.

 

Asif Mohamed: 

I'd love to hear about how you got here and a little bit of your background.

 

Sam Azzarello: 

So I think my journey's a little long-winded, like many peoples'. I actually started as a research analyst. So I love markets and I learned cross-asset markets and how to do research. And then I found over time it evolved to wanting to be on the backend and making sure that that research gets to clients in the right way, at the right time, in the right format. So still out of the deep love of markets, but I would say the expression of that has evolved.

 

Asif Mohamed: 

And do you really like what you do today?

 

Sam Azzarello: 

I truly do. I mean, I'm having a blast and I think it's important that myself and the team have fun because it sounds maybe cliche, but we spend a lot of hours at work. We enjoy the people that we work with. And overall, I do want to make sure that I'm enjoying myself day-to-day. Asif, tell me about your background. How did you get to where you are?

 

Asif Mohamed: 

So I started out as an engineer many years ago and I actually became a banker in derivatives and converts and ran business development functions and ended up currently in research. But I think the common theme throughout all of these roles, whether it was in technology or banking, equity capital markets, has been around innovation. And in every step of the way, there's always been a large component of innovation that really helps bring clients in, deliver better product, whether it's pricing or risk. A big part of it is always about innovation.

 

Sam Azzarello: 

Amazing. I would add there's something very powerful about distribution. I mean, distribution at the end of the day is how something gets out into the world. So I know we have really good content. It's award-winning content, but I would also add equally important is how we get it to clients, in what form, and to your point about how innovative we are in terms of evolving as the landscape changes.

 

Asif Mohamed: 

We deliver at scale every day for our clients. We send 1.5 million emails to our clients each day. We have 96,000 clients visiting our JP Morgan Markets platform on a monthly basis. My research is the client's personal hub. They can find information there that is based on their subscriptions, and we also make recommendations there for things that they may not know that they need.

 

Sam Azzarello: 

At scale is the phrase that always either adds the hiccup or in our case, the opportunity. So doing this for thousands of clients, millions of emails a day on all of our platforms, and I'd add we have a thousand analysts across the globe, covering every company, asset class, country, it's at scale.

 

Asif Mohamed: 

Yeah. And clients are demanding it. So we tend to want to skate to where the puck's going to be, and that requires sort of lengthy investments over a longer period of time. So it's not a one and done, it's an ongoing investment. Learning, being dynamic, delivering as the markets are changing and our clients' needs are changing. Who are the content creators within the product?

 

Sam Azzarello: 

Global Research is an absolute content powerhouse. We produce more than 130,000 pieces of content per year, and then we augment that with data sets and data experiences, lots of analysis. We cover countries, sectors, over 5,000 companies. I'm always blown away at the level of depth and expertise of our research analysts. I know however, they don't do it in a vacuum. They're working with sales and trading to get ideas. They're keeping a pulse on what's trending, what's important to clients. There is a continual dialogue between our research analysts and clients.

I also know they're keeping their eye out for what's coming, what's next, what's on the horizon, and they do that through data, alternative data, talking to different stakeholders and just drawing on their immense expertise that they've built from decades of being in their markets.

 

Asif Mohamed: 

So there are no reports without our analysts.

 

Sam Azzarello: 

There's a lot of trust and rapport built up over time between our research analysts and clients. And I think it's a show of trust that clients come to us in moments of market volatility or news flow or disruption, and they come to our research analysts to get their view because they trust our analysts and the breadth and depth of knowledge and expertise is there and it's been established. Insights from our research analysts are augmented with data use, and we know there is an insatiable appetite for more and more data. So Asif, what are we doing with respect to data in Global Research?

 

Asif Mohamed: 

You're right. Data is exploding, has been exploding for quite a while, and investment decisions are using more and more data. The electronic delivery of research, we call it machine-readable research, has become more and more important. Clients would like to search JP Morgan's research as well as their own proprietary data sets and content and generate insights. So data is a big deal for us, has always been.

We have DataQuery, which is our data platform on the JP Morgan Markets platform that has 13 million time series of data that investors are using to make investment decisions. We've also created industry differentiated data products such as JPMaQS, which is a macroeconomic quantummental data set that is used for making investment decisions and then traders use it for backtesting their trading strategies. So there's a wealth of existing information and we're creating new products as well to meet our clients' needs.

 

Sam Azzarello: 

And that's a powerful combination, augmenting our research analysts' insights with new and proprietary data sets to lead to faster and smarter insights.

 

Asif Mohamed: 

So how do we keep delivering for clients?

 

Sam Azzarello: 

We keep delivering for clients by making it as easy as possible for them. So we know the amount, the volume of information is only growing. So helping clients get to the key insights they need as efficiently and effectively as possible, that's the way forward.

 

Asif Mohamed: 

And what are some of the challenges that you're seeing today in terms of the need for information?

 

Sam Azzarello: 

Cross-asset is a space where I think we're going to see more of an evolution because many market events now, they ripple around the globe. They impact all the asset classes and the ability for us to pull together the breadth and depth of our expertise, light up that theme for clients and showcase it in a cross-asset format is going to become more powerful.

 

Asif Mohamed: 

And that's a space where we've been accelerating in the use of AI to generate insights faster by looking across lots of information, volumes and volumes of information, both written and data to generate those insights. So looking for a needle in a haystack in a very structured way is really the focus of some of the modern products within Global Research. So where do these ideas come from?

 

Sam Azzarello: 

Idea generation in research is a very creative process, I would say, and it's going to vary analyst to analyst, but it could be a tidbit overheard at a client event. It could be a news article. It could be a piece of data or a chart. It could be inspiration walking on the street. And from there, I know that our research analysts are all about collaboration. So they're bringing together their team, bringing together related asset classes and researchers across the department.

And from there, they're refining the idea, they're doing analysis, they're using data. And there's a lot of work and intellectual creativity that has to go into this process before a research note gets published. Asif, let's talk about personalization, the next frontier. So what does personalization look like for Global Research?

 

Asif Mohamed: 

So personalization for us is really about ensuring that the thousands, hundreds of thousands of clients that we have, that we have information about how to serve them best. And they've given us a lot of that information and we use it to give them a very bespoke experience and understanding obviously the research reports that they're consuming, but also delivering potentially data and other products and services that may be relevant for them.

And so in my view, personalization really tries to serve each client individually based on their needs, whether it's during a call with an analyst or through consumption of their reports. More importantly, we do listen to our clients. And so listening to clients, getting feedback helps us deliver better products and grow our products and our services for those clients.

 

Sam Azzarello: 

And I know there can at times be a little bit of FOMO, right? Clients want to know what they might be missing out on, what the next new idea is, who's a new analyst perhaps that they haven't engaged with before. So to the extent that recommendations and personalization does that, we know we're serving clients through those channels.

 

Asif Mohamed: 

That's right. We're serving clients through personalization and recommendations, both in thematic as well as process type products that they may not be aware of, we're helping them to find those proactively. Again, we create a lot of content. How does a client really identify something that they're not focused on every day? We're using technology, AI, and the client's feedback to get them the content that they may not be aware of, that they absolutely should look at for their investment decisions.

Simon Smith: 

Hi, Simon Smith, Head of Front Office Data and Analytics Sales at JPMorgan.

 

Dan Dalton: 

And I'm Dan Dalton, Global Head of Data Analytics Sales at JPMorgan.

 

Simon Smith: 

So, given your title and given we celebrated 25 years of your tenure here at JP last night, what led you to focus in data and analytics? How did you end up here?

 

Dan Dalton:

I think it's a great question as a self-proclaimed lifer at JPMorgan to ask. The biggest bit of career advice I give anyone that I have sort of a mentoring relationship is actually that I really didn't know what I wanted to do. I just knew that I worked at a good company and I'd find my way. And so, actually having done a variety of roles through the firm, including being the chief operating officer for a big part of our research department, it was just very apparent to me that there was a real opportunity in the data analytics space.

So much content that we were producing, so much amazing intellectual output, but perhaps the way in which we were positioning it, serving it, the way in which clients were receiving it could do with a bit of tuning. So, I put my hand up and said, "I'd like to do something different." And as is always, people don't always want you to do something different. And so, it was inherently there was a bit of risk in that, but it's been an incredible journey. And that's why I've been doing this now for about 13 years of those 25. So, it'd be great to hear though how you came about a similar space.

 

Simon Smith: 

Yeah. So, I did a technical degree. So, I did a computer science degree and then I took an internship here at JP. I ended up in the data and analytics space within equity derivatives in that original internship. And then from there, I was actually asked to stay on and not go and finish my degree. I chose to go finish my degree anyway and came back and ended up working in the research group similar to yourself. So, I was working more on the engineering side within research.

And then for a period I, as research, were really building up a lot of the modeling capabilities they had in house, moved to build out an engineering function alongside the research desks, alongside some of the rate strategists. And then from there moved as we were seeing more focus from our clients, looking at what the output of those models were and how we were modeling a lot of the value in fixed income markets, moved to focus more on client facing analytics, more so in a product role.

And then about seven years ago, as that was becoming very adopted by our client base, we were really looking at the commercial opportunity around that. And then obviously our paths cross, you'd obviously looked at the commercial opportunity of some of the rest of the research output. And so, moved to join your group in about seven years ago now.

 

Dan Dalton: 

So, not just a life or a data analytics life of it.

 

Simon Smith: 

Yeah. Domain and company lifer, I would say.

 

Dan Dalton: 

So, how does that feel? How many years for you now?

 

Simon Smith: 

So, I'm 15 years in, so it's been great. I think whilst my domain has been consistent, my function has been very varied. Obviously starting off in more of an engineering function, moving through a product group and then ended up in a sales role.

 

Dan Dalton: 

So, I guess as both of us have gone through a bit of a transition, we weren't salespeople, we became salespeople. How would you talk about that journey a little bit and making that transition?

 

Simon Smith: 

I think people have these ideas that everyone has a ... Is that a career endpoint that they've driven towards? I think for me, a lot of it was quite organic in the sense that we were originally building content, which was then shifted to being more externally consumed. And then as that was requiring more presence and time with the clients for them to understand what it was that we'd built and how we'd built it, really that time with the clients made you realize how they were using it and then how they could maybe derive more value from it. And that really sparked my interest in being client centric and client focused. And then I think from there you end up naturally tending towards the sales side of it. I thought what about yourself?

 

Dan Dalton: 

Yeah. I think for me, it was a bit more organic, I would say. Actually, because I started the business, it was very small. You become the expert. And so, along the way, you accumulate those skills. So, going back to what I was saying really about not having a plan, it felt like the right next thing to do at the time. And through some really, really in-depth conversations with our customers and our clients about what they actually wanted from JPMorgan data analytics, you built the relationship.

And I think as we're going to talk about today, the relationship and us as a firm and our ability to aggregate all of that demand, if you like, from our clients and to build products as a result means you're really a little bit sat between product and client. So, it's a really interesting vantage point. And so, I think for us, it's not really sales in many ways. It's all about relationships. And the firm has a ton of relationships that we've been able to leverage to expand our enterprise.

 

Simon Smith: 

I guess maybe outside of the client relationship and sales side, maybe just a grounding around what is the data and analytics business at JPMorgan? What is it that we offer to our clients? What services and functions do we provide?

 

Dan Dalton: 

Yeah. Great question. I mean, really, firstly, I'd say that as a team, we are the one faced to everything data analytics that JPMorgan has to offer. What we also have very consciously decided though is that the product teams that build the data analytics don't all belong together. So, as I said at the beginning, we have a large intellectual center in our global research department. We'd spent many years, 25 plus building fixed income indices, for example. That team rightly still remains very embedded in research.

And as a result, it has access to the full power of what our research department can deliver. Our pricing direct business, given the nature of the proprietary but independent valuations that we do for client portfolio holdings, help them strike NAV and do other valuation process, it has to be independent. They have information from our customers that we wouldn't want to share with the rest of the firm. And so, that remains an independent entity.

And then if you look through then the range of content across our trading business, some of what we're doing in the data management space, it's appropriately aligned to the businesses that it supports. I guess the newest of that would be our Fusion platform, which is being built out of our security services function. Obviously, a global administrator, lots of clients challenged with using data and analytics and integrating them into their workflows. It's appropriate for to be with that business.

But what's key for me is I can amass a team of professionals that can sit across all of those different disciplines. And while we obviously align people like yourself with all of our front office solutions, we align people to those different disciplines. We can have really one conversation with the client.

 

Simon Smith: 

That's a good grounding in terms of the different product lines that we provide. And then thinking about how my team or other teams face off against our clients, how do we typically put that to a client? Because obviously there's quite a lot there. Is it just like, here's the Rolodex of items, pick and choose, or how do we make sense of it to a client?

 

Dan Dalton: 

Yeah. So, I think of it as a multidisciplinary function. We've got people aligned around our pricing and index products very much because the client at the other end really as a consumer are buying both of those things in tandem because as you'd expect, our indices are priced by our pricing direct business. And so, it makes a lot of sense for you to have one connected client discussion.

Our Neovest platform, which also, as you know, we recently acquired the LayerOne platform, has a dedicated sales team. You're having very specialists and specific conversations with our hedge fund clients around EMS, order management capabilities, et cetera.

And then really your team, and really what do you focus on? It's really the front office relationship and the solutions. And the reason we say that is it could be a bit of those things, but it's also a lot about the content that we could deliver actually from the core of JPMorgan. That's our trading desks or from our research department and other parts of the organization. And it's really like, how can we bring content to bear that's useful to our clients that we are happy to externalize and trying to find the middle ground on that.

 

Simon Smith: 

So, I guess each client conversation is bespoke to the type of client.

 

Dan Dalton: 

You would get a different version of our team and what we do for clients, depending on who you were. If you're an asset owner, you get a very specific curated services versus a hedge fund. And even at the same, even if you're thinking about a hedge fund, are you very data centric? Are you less so? And always it's about how can we actually help you?

So, it's really a coverage role and a relationship role as much as it's a sales role. It's like, what actually are you needing to find solutions for? What have we heard in aggregate from other clients battling with the same things? And then usually there's hopefully for us some commercial outcome also in terms of some of those products being relevant and you talk to the client.

 

Simon Smith: 

Yeah. And I think we skipped over some of the parts at the beginning, but that idea of solutioning with that range of products that we have, that's really what keeps me and my team excited. The fact that we go into these client sessions and you obviously get due diligence and information ahead of it, but really, it's a bit of an unknown and it's very rarely square peg square hole. It's very, very often trying to make a solution that fits the client's workflow.

There are different, even within the same client types and the same segments of clients, there's still very different workflows and practices happening. So, it's very much that that is exciting for my group to think about, okay, so how do we create something that's fit for purpose using the vast amount of technology and resources that JPMorgan has to deploy to fit the client's need?

 

Dan Dalton: 

Yeah. So, you become the master of context switching, which I know you always cite me with, but actually it's exactly what you've just said excites you about your role at JPMorgan. How do you do that?

 

Simon Smith: 

Yeah. Well, it's a requirement in terms of the role requires you to be able to go from speaking to an asset owner about post-trade reporting all the way through to the next minute talking to a startup hedge fund around how they're going to use our data in the pre-trade systematic investing space. So, it's very much a required piece and something that I picked up whilst being on the desk.

And then in terms of how you have the knowledge or the breadth of coverage to do that, it's typically around the internal connections. So, you talked about our product partners who are obviously amazing resources for us to work with. They have particular niche depth knowledge around their product area, their domain, and really leveraging that where you can to try and get enough. I think we always talk about our group being the A to F of most of our products or the majority of the products, and then the product groups behind us being like the F to Z. So, really being able to have that amount of information across a broad enough spectrum means that we can then switch between-

 

Dan Dalton: 

So, enough information to be dangerous on every product, I suppose is what you're saying, but you bring the right experts into the client discussion as required.

 

Simon Smith: 

Yeah. In terms of your tenure here, what is it that keeps you interested in turning up to JPMorgan for 25 years in a row? Yeah.

Dan Dalton: I think something really miraculous happened 13, 14 years ago when I put my hand up actually, which was I got the opportunity to work for one of the largest and best banks on the street that's obviously, as we've all seen, gone through a huge evolution and build a business. And I really feel like we've built a business and I've had the opportunity to be really entrepreneurial, which was always an itch that I wanted to scratch, but actually being able to do that in the comfort of a firm like JPMorgan where you'd built up credibility, people gave you the trust, it's really freeing actually.

And so, that was the really amazing thing that happened. I think if I put that back onto the actual business and the products, you really have to go back and think about like, why are we doing data analytics? It's actually quite unique versus some of our peers in the industry to want to be in this business and to be doing some of the things that we're doing, as we said in indices, in pricing, in the front office.

Most of the banks like us had made some investments in these spaces, but with incoming regulatory change, for example, in the provision of benchmark, there was different regulations coming in. Some of our competitor firms made decisions to divest some of those assets and they divested them to companies that more traditionally supply data analytics, the major platform providers and what have you.

As is very typical at JPMorgan, we decided that that was not for us. And actually, if you really think about why we built these in the first place, we're the number one provider, for example, in emerging market fixed income indices. Why is that? Well, we wanted to build the secondary trading market. How do you build the secondary trading market? You need to build transparency.

Clients need products to help them navigate. They need to be able to look at the investible universe that they can then select securities for in their portfolios. If we do a good job of providing that transparency as a huge market maker that has a presence in all of the relevant markets to construct that index. While it all might seem very benevolent, it's not benevolent. We built it for that purpose.

If you think about our pricing direct business that has its roots in one of our predecessor firms, we could have acquired that firm and dispensed with that. We did the opposite. We've massively expanded it, invested it to really cover all of the over-the-counter markets and derivatives. Why did we do it in the first place? To evaluate the price of assets that as a firm we were packaging and selling to our clients that they needed to strike now at the end of the day on.

In a sense, these things are extremely logical for a bank to own. It's just that the world had moved on, and I guess it's moved on where clients have a voracious appetite for data. And I think the grounding that we had because of those two businesses, well, we were already a professional data analytics company. We already knew how to serve clients. It was all done appropriately. It was all licensed appropriately. Clients had certainty of delivery of service.

And so, if we then turn our attention to the other content that we've taken from our trading desks, our structuring teams and other parts of the organization, we knew how to do it. And we've built on those foundations. And I think for me, that's been the key. We had a really big stake in investing, and it's just given us a much better foundation, I think, versus some of our competitors.

 

Simon Smith: 

Yeah. I think that story remains true for both the data and some of the system sides as well. If we think about you're supporting clients in their transaction of financial products in markets that could be through transparency in indices, valuations, in price indirect, in data and valuations and services in terms of the content and then some of our risk analytics, things of that nature.

 

Dan Dalton: 

I think that's a great point. And I think that what have we really succeeded in providing its market data, it's like a better categorization of the investible universe, pricing indices, yes, but also really root market data. We understand how a lot of the over the counter fixed income markets, for example, trade. We build a huge amount of intellectual property to support our traders. Not all of that intellectual property has a competitive dimension to it that we wouldn't want to expose it. With many people in our quant research teams, traders themselves cleansing that data state around what they do.

And so, our ability to take components of that, expose them, it means clients can save quite a lot of time. They can also do it with a very trusted partner because they know something very critical, which I think is where we're very differentiated from a lot of the data analytics platforms is we use it. It's our intellectual property, but we're using it actively. And so, I think the best stamp of approval or best alternative to an SLA or on delivery is that if there's a failure, it failed for JPMorgan, that's not acceptable. And so, you can put some trust and reliance in our ability to deliver some of this stuff.

 

Simon Smith: 

Yeah. And I think that's something that we've seen, as I say, across the content, the risk systems, everything that we provide in the front office space is very much on that basis.

 

Dan Dalton: 

I guess it's in a sense the legacy of what we built from a data perspective enabled us to go further. So, now we're exposing fully packaged risk capabilities. So, maybe you could touch on that.

 

Simon Smith: 

Yeah. So, on a similar point, thinking about our risk analytics there. So, particularly in exotic instruments where the independent instrument models are hard to come by, the market data are being of a good enough quality to value these very sensitive products is for hard to come by. Being able to provide clients with a service where they can simply upload positions and then retrieve back valuations, risk metrics, sensitivities, value at risk or P&L vectors is something that's extremely useful to clients.

It means that they can very quickly and agilely deploy it into a new asset class, a new area where they would otherwise not be able to do so, be it because of capital expenditure in terms of building systems, acquiring quants, acquiring market data, all of that time and effort and resources that would otherwise be spent essentially replicating the same thing that JPMorgan's built and does for itself is really something that we can shortcut for clients.

And so, a lot of our business as we think of it as really standing on and leveraging the spend that the bank has on technology and technology and data as a firm, and then really pivoting that to allow our clients to benefit from that same spend.

 

Dan Dalton: 

Makes sense. Makes sense.

 

Simon Smith: 

Thinking about the data and analytics business and how it's evolved, and as we've stood  now and maybe looking slightly further forward as well, what are the trends that you're seeing from our clients and the trends in our product suite as well as we respond to that client interest?

 

Dan Dalton: 

So, I'm going to put the word "AI" out there, but I'm going to park it for one second while I talk about a few others. Firstly, going back to that point on transparency, the same process that we employed 25 years ago in some of those businesses, we're having the opportunity to reemploy. And so, you're seeing the same challenges now from clients, for example, navigating alternative allocations, private assets.

And so, actually, how do you build transparency in those markets? You need pricing, you need indices, you need intelligence from experts. And so, in culmination, if you think about what we've built, there's a real opportunity to do the same again and to breathe some life into that, not just for ourselves, but for the industry at large.

I think if we flipped to data just a bit more broadly, I'd say that a couple of things are happening. One, you've had the very, let's say quantity clients that I've been in this. So, talking about AI, for us, it's not new. What's actually new is who can use it. A lot of our large clients have been using it for many years, and so we learned a lot in that process.

And I think a couple of things we learned. One, you need to be very good at systematic delivery. You can't fail because you break process. So, in a way that's given. We're a bit game ready for that. So, I feel quite prepared for that. Two though is just overwhelming amount of new demand for data that perhaps would only have been interesting to those clients historically.

And why is that? It's because a lot of these new tools that are coming out are democratizing a little use of some of the AI techniques, machine learning, et cetera, that really was the realm of clients that could invest very heavily in technology and quant resources. So, that's a big theme that we're seeing.

The second thing is what can they crunch? And so, what can they crunch? You've got much more interest in histories. And so, as we've said, we have some advantage because we've been doing this for a long time. I always say the long history is great because we've been doing it a long time. We've built up a good catalog of history, extracting it from legacy systems can be quite difficult, but we're doing a lot of work on that, as you know, but it's there. So, I think that advantage is clear.

The other point though is that what you actually define as data has changed and the structured content that we're accustomed to distributing through platforms that we can talk about isn't the total definition of data. Clients are now seeing, for example, textual content, written content from our research department, from ourselves and traders. Historically was going obviously through publishing systems or in the case of some of our sales and trading commentators through email. All of a sudden, this content can be transformed into something else. You can pull sentiment.

I think a lot of people are familiar with that, but again, the tooling that's coming out is democratizing that. More people can do that. So, there's been a spike in interest in things like data feeds of written content. And so, client's ability to process that, whether it be through a third-party vendor that's built technology or through their own internal technology, we've seen a huge spike in that.

So, what do we do? Well, the great thing is we have lots of content and we have the number one ranked research franchise, which is amazing. And so, the value of extracting that one is amazing, but also what else can we expose from that process? And so, we've been doing a lot of work in equities and really trying to bring out equity data offering up to par with our fixed income offering.

And so, actually, if you think about equities, a lot of the data that we've talked about, it's readily available, exchange listed. The index providers are very famous and well known. We don't want to compete in that space, but if you think about inputs into the alpha generating process, I think we can compete because we can not only give you a view. If you think about our research analyst process at the moment, you can read their content, you can look at the recommendations that they make, but actually what do they do inside JPMorgan to achieve that?

They consider the data that surrounds their sector, their industry. They curate it. They assess it. They might do a [inaudible 00:23:10] pervade of different providers and make decisions that these ones are the right ones for my coverage. And then they use that internally in their investment process. They build their own data sets to support their own investment thinking.

And so, actually unpacking that process a bit and starting to help clients with their own decision making allows us to do two things. You might have a competing view of how you can structure your investment thesis on a given company or industry, but you can learn from how we put that thesis together and then you can put your own inputs in. You can also save time because as we know, a lot of clients are, they have obviously constant coverage of certain companies, others they might not. They may stray in and out of it. And so, having that readily available as like additional data sources I'm seeing as a huge thing that our clients are asking for and that we need to deliver.

 

Simon Smith: 

Yeah. As we think about that knowledge map, as you say, that sits typically in an analyst's head, trying to get that into the hands of our clients has really been a lot of what we've been looking at in the equity space. So, as you think about obviously there's the written content, there's then all of the fundamental data that they generate, like estimates, all of their working models, all of their outputs from those models, the valuation metrics, et cetera.

And then increasingly there's some products that the research group generate, as you say, is that like intermediate state whereby they're often bringing together multiple external data sources or possibly some proprietary data that they've captured themselves and then commingling that to form an output which they then take a view on or they then use as a decision support tool.

So, then having that as something that they use, but externalizing that to our clients is really what we've had a lot of demand around. So, trying to extract that knowledge map from being part of the analyst's head and part of the desk content and making it a client service has really been a big ask in the equity space.

 

Dan Dalton: 

So, if you're like, this is all feedstock for like superpowered analysis. More people can do that superpowered analysis. Where do you think this AI journey is taking us?

 

Simon Smith: 

So, you mentioned, as you say, obviously a lot of our clients have been applying these same techniques maybe in slightly different iterations for a long time now. So, we have some of our quant clients who specifically see their edges being information processing and the extraction or the refinement of information from content. And so, I think as they continue to move up the spectrum, I think that's very exciting. I'm not quite sure where that ends up from their point of view, but they're continually pushing the frontier of what we provide.

So, for a long time, they've had machine-readable research feeds, as you mentioned. They're now getting their hands around ourselves and trading commentary. It's then asking for transcripts of all of our calls. It's then any analyst models in between. So, really thinking about like, they're really pushing us to provide a feed of everything that JPMorgan deems as content or generates as content to them such that they can push it through their process.

 

Dan Dalton: 

And I guess that leads back to that point on coverage and being able to aggregate the collective demand of some of those clients. And I think they have the benefit because we can create effectively some collaboration amongst those clients in terms of what we want to offer. And I guess what you're also saying is clients that are earlier in their data journey can benefit because we've done a lot of this already.

 

Simon Smith: 

Exactly. That cohort who are now taking our research feeds this year, let's say, who have using some of these new generative models are obviously starting with research because they're trying to build out a POC, they're trying to derive value, make sure that it's fit for purpose, be it systematic or discretionary. And then as we're working with those quant clients and getting pushed to provide more content in feeds that can go into these models, as these new cohort of clients who are embarking on this journey, move past research and onto the next pieces, clearly we'll have more mature products for them in that space and they'll benefit from the bleeding edge that is the quant investor who has extracted painfully in some cases information from us.

But then over time, we've then productized that and made that available. So, as they need to continue moving to make sure that they are getting an information edge and not just getting the same information in an easy to digest format that everyone else is, but those who are on the journey behind them are benefiting from what falls out behind that.

 

Dan Dalton: 

That makes sense. I guess the other thing I would say is that 12 months ago, AI was all about where is my data? How do I get it in a proper fit state, aggregate it centrally? How do I figure out the governance of that information, like who can access, who can't? And in a way readying yourself for that AI journey. I think fast forward to now, people are using it and actually some of that pre-work's been very beneficial, but we definitely see also clients struggling with having not necessarily got that bit right.

 

Simon Smith: 

Yeah. I think we obviously have a large function in house around chief data and analytics office, not purely focused around AI, but a lot of the work that actually goes on there is around making sure that the data that we want to use as feed into these models is in an appropriate, governed, labeled, described state such that it's not a garbage in, garbage out process, and you're actually deriving value from authoritative sources which have the right data and correct information that you're putting through.

 

Dan Dalton: 

And so, I guess talk me through, because I know our CDAO platform is called Fusion and we also have Fusion for our clients.

 

Simon Smith: 

Yeah. So, you can imagine that a lot of that work that we do as a bank is putting in the frameworks rather than specifically labeling the data. So, having a framework which allows you to have a data catalog, allows you to label all of your data, apply metadata, capture information around where it's come from, the lineage, et cetera. And so, a lot of that is really work that others could benefit from, going back to that same point around the text and that JPMorgan has and that being something that's a lot...

 

Dan Dalton: 

So, in a way, similar to the data journey.

 

Simon Smith: 

Exactly.

 

Dan Dalton: 

Exposing some of the capabilities we're building as a firm for ourselves, they don't necessarily have unique advantage, but we see that the industry is doing the same thing many times over.

 

Simon Smith: 

Exactly. So, it's like the common concept of a club good in the sense that if we build it once and build it at a level that is modular and customizable enough that it can be adapted to fit multiple use cases. Then obviously multiple people can benefit from the same stack essentially. So, really, we think of that as using those same investment dollars that we use inside the bank to then provide clients the support in this space because there's no economic benefit to the clients nor to society in everyone building the same thing in their own shop.

 

Dan Dalton: 

But to make that a bit more real, we're talking really about the normalization of lots of third-party information that clients use in the investment process, albeit the indices, the ESG data, their security master, their representation, their securities derivative investible universe and making connections to the industry data that you could use in that context.

 

Simon Smith: 

Yes. Exactly. So, thinking about all of that wrangling and normalization that happens when you have different sources of a common data domain, be it like reference data, ESG data, index data, all of those need to come together and be normalized and commingled in a way that you can use it uniformly. And so, in security master, that tends to be around reference data and some of that is obviously feeds from third parties. Some of that is you opining on that reference data as to which one you would choose or which source or how you would override a particular field.

Obviously in the ESG space, again, there's probably a less mature space. And so, there's less of a consensus as to the authoritative points or what are the correct points. And therefore, normalizing is quite a subjective exercise there. So, there's a lot of work that needs to be done to be able to utilize multiple ESG sources in your investment process. So, should a client really be spending all their time in that data wrangling phase or would they be better off spending their time on higher order problems?

 

Dan Dalton: 

Yeah. I like that description of higher order problems. I think what we're trying to do is get them a little bit closer to that because that's ultimately the alpha domain and where they really demonstrate to their own clients their differentiation.

 

Simon Smith: 

Yeah. We want to provide the modular building blocks of common duplicative or duplicative tasks. Anything above that is obviously the client's own expertise and own processes and choices.

 

Dan Dalton: 

I think the other thing I've definitely seen from that perspective in Fusion is when clients were earlier on in the journey, they saw a huge amount of opportunity to build some of this themselves. And actually, as more AI tools have come in, they've just realized how hard it is. And so, you're seeing it swing back a bit. And then all of a sudden, actually the ability to buy a common platform where clients are onboarding and the demands of those clients are being gathered. The tools are being built in a way that would serve multiple different types of clients, different use cases.

A lot of clients telling us that there's a real edge for us in playing in that space. I think the advantage we have is obviously we can build it for many clients. And so, there's scale in their ability to then buy a fraction of the price of developing it in house.

 

Simon Smith: 

That maybe comes back to a couple of points around like, A, why do JPMorgan think we're adapt or adept to playing in this space? Why would JPMorgan versus maybe a data vendor or an analytics provider be the kind of platform of choice? And then B, what is it in the existing client tech stacks that are deficient to allow them to benefit from?

 

Dan Dalton: 

Perfect. But maybe I split it. If I think about the Fusion platform, which as you know, we're very focused on our asset manager and asset clients and it's initial development. It's a very natural extension of our overall custody fund accounting, middle office relationship with those customers. Many of those clients had said to us, "You've taken over the administrative burden of a bunch of things that don't add alpha."

Again, same principles you can do at scale, but you've left me with something, which is this whole challenge around data. And I'm aware that I have a challenge. I'm spending quite a lot of money doing a bunch of stuff, but I can just see the challenge is going to get vastly more confidence. And so, it makes so much sense to me that you'd extend that overall administrative relationship to data. And so, really that's where Fusion can play a part. We can do it at a different scale. We have a pretty strong leverage with all of the data contributors in the industry. So, we can really represent the client and development of that.

 

Simon Smith:

So, that's the why JPMorgan. And then thinking more to like, what are the deficiencies and what is it that a platform like Fusion helps our clients with? What are they trying to solve for? What are the limiting factors?

 

Dan Dalton: 

It's to your earlier point on this idea of if you take the security master and simplify that, all of the securities that clients investing in, they need a whole lot of information around that. So, as I said, we're a big security services provider. You want to bring in your performance. You want to bring in your fund accounting data. You want to actually provide real-time performance analytics to your front office users. You've got massive reconciliation challenges as you go through that chain, but at least with a platform like Fusion, you can surface all of those differences and discrepancies and allow a client to manage through that process effectively.

You can stitch all of their core information, that investible universe to all of that third-party data that they might want to bring in and do that seamlessly because we've already integrated it. So, rather than you going out and making contact with 50 data vendors, we've probably already done it. And so, there's just a scale in our ability to do that. As with everything, you talked about our risk platform, the same is true of Fusion. We really built up the platform with the early adopter ethos. Picking partner clients. They were having these very notable challenges that we've talked about and then working with them because the best representation of the buy side is the buy side. And so, we're very focused in the development of any of these to have anchor partner clients that really inform the development, prioritize the roadmap.

 

Simon Smith: 

Yeah. And I guess for those guys having a new platform as opposed to their legacy platforms as these new domains of data come online. So, the requirement to integrate ESG as an example.

 

Dan Dalton: 

Great point. And it's where it is. We're building a cloud native platform. I know everyone's talking about that. You have no idea the number of client discussions I have where you talk about all of these concepts I've just talked about, nothing's cloud. If you're using a third-party vendor for some components of it, they'll charge you to move to cloud. So, we've really gone a cloud first and cloud provider of your choice, but really like built this in very modern infrastructure, the architecture's hyper modern. And to your earlier point, we're using it ourselves like JPMorgan. And so, to my earlier point around we use some of this in active trading situations, we're using this in active data management situations as well.

 

Simon Smith: 

So, I guess it sounds like at one end we've got the platforms where there's the full-blown platform offering, can be a managed service, can be software as a service provided as a full functioning component, if you will. We have then the data piece, which more often than not feeds into a client's existing platform or client's own platforms. And I guess there's that piece in the middle where we have like modular building blocks where we plug and play into the tech stack. So, is there anything, I guess as we think about that platform side?

 

Dan Dalton: 

If you look at a lot of the large hedge funds, a lot of their USP is building these big platforms, they have great scale. We could still provide them a lot of input, but it tends to be the data that active trading desk information that we've scrubbed and cleaned, very valuable and useful.

 

Simon Smith: 

Yeah. The market data.

 

Dan Dalton: 

And then they build up their own systems because a USP for them is giving their portfolio, managers, differentiated and best in class technology. We could make the argument though that centrally you can build some of that. And so, again, exactly the same as we've talked about with Fusion, our Neovest platform offers electronical to management capabilities, portfolio management capabilities all out of the box.

And so, actually if you are, as you were saying, a hedge fund may be going to a new asset class, you might not want to build up that technology [inaudible 00:38:32]. You might actually want to see whether you can perform and it's something that you actually want to invest in the long term. And so, sometimes we have clients that want to use us in that experimentation process, which we're very happy to support, but usually we win those clients for the long term because actually, again, there's a scale, just a raw scale in the commerciality of being able to do it for many. But the connection back to the bank and its capability mean it's pretty good out of the box.

 

Simon Smith: 

Yes. And they have that, as you say, they have that stack all the way from execution, order management, and all the way into portfolio management. And we see some clients picking up the full stack. We see some clients taking components of any of those layers within that, if you will, and really some of them taking it just for asset classes. So, to your point, we see people moving from equities and wanting to add some credit into their books and using a system, which means that they don't have to spend six months building, hiring, developing.

 

Dan Dalton: 

It's a great point. And I think while as a firm, we want our platforms and technology to be global and complete, but one thing we really, and this isn't where a lot of the industry is we do not want to force anybody to take the whole platform. Because by doing that, you miss sales opportunities, clearly, but you also miss expert client opportunities because that piece that they want, they might be really good at. And we want that input into the further development of the platform. We also want to land and expand all of our relationships. We want to get in with something that solves a problem for you right now.

 

Simon Smith: 

Yeah. Certainly, my team, as we think about solutioning, it tends to be exactly that. You come in specifically with a client pain point or a new client expansion plan or some specific point that you're solving for, and then the idea is always to try and support that client as they have further pain points, further needs and land and expand, as you say, either that platform, that data, the overall piece.

And so, we've seen this across a lot of the hedge fund space where traditionally they were just picking up a lot of our market data, now picking up some of our valuations in their fund control functions, picking up some of our indices when we think about the historical data, especially in credit that we have around a large investable universe of investment grade debt. It's an unrivaled ecosystem if you think of it that way in the sense that they get in to know us, we get to know them, we know where they're going and can point them in the right direction.

 

Dan Dalton: 

And I guess in some cases they don't even know they wanted something. Why would a hedge fund want a benchmark index because they're clearly not benchmarking the portfolio performance?

 

Simon Smith: 

Exactly.

 

Dan Dalton:

It's actually that enormous amount of data that we track as an index provider on those universal securities back in time because back-testing has become a big theme. And I guess on that topic, it'd be great to hear maybe about some of the developments in global research.

 

Simon Smith: 

Yeah. So, I think, as you say, in terms of the back-testing piece, that's been something that's very important for our clients, both within the market data side, and then more so as they're developing strategies on alternative data, not necessarily alternative data in the broader sense, but more non-market data. The research data that we provide being of a quality of a systematic nature that it can be back tested, it's representative of the information that was available to the market at that point in time.

And so, that's an area that the research function has really spent a lot of time and effort on over the last, I guess five years now. We have a client who we actually partner with to provide a macroeconomic information library, if you will. So, it's essentially a system which provides quantamental data for macro investing. So, whereas traditionally a lot of macro systematic strategies have been built out around purely market data, the use of macro data has been very difficult given it's often revised, it has seasonal effects, it's often rebased or that some of it is even survey based.

 

Dan Dalton: 

If I could maybe put that in a slightly simpler way, it's almost like if I was sitting at my desk in 2014, what would all the indicators in the macroeconomic space, if we take, I don't know, inflation numbers, country, payrolls, what would they have said with a predicted outcome?

 

Simon Smith:

Yeah. If you'd snapped this state of the economic indicators across a broad range of concepts, countries, regions, and areas in the same way that you snap prices at the end of day, then you'd essentially have created what we've created and then...

 

Dan Dalton:

If you know the market at that point would trade, their behavior would be driven by those point in time indicators, not by the ultimate release revised 15 times before it becomes official.

 

Simon Smith: 

Exactly. And then adding on some of the, or encoding some of the economic know how around that as well. So, knowing around when an inflation measure that the market looks at changes from being RPI to CPI or knowing how to compare the right measures across different countries in what are some case idiosyncratic indicators. And so, things like that is where we've made macro information investible or tradable as the group likes to say, and it's something that as a lot of our clients are amazed to have access to because it's something that you just wouldn't be able to staff and resource yourself. We have 20 people working on this project, specifically like quant economists, financial engineers, building these indicators such that a large range of our clients can benefit both from the data as well as the financial products...

 

Dan Dalton: 

So, I guess the same thread of better information, better understanding of the present and the past, better ability to navigate.

 

Simon Smith: 

And a resource spend that you just couldn't justify yourself.

 

Dan Dalton: 

So, that's all centralization.

 

Simon Smith: 

Exactly. That club good concept coming back around. And if you think about how our clients use our data, there's obviously that systematic side of back-testing and strategy creation, be it like deriving signals or be it conditioning preexisting strategies and signals. But also, on the discretionary side, we've done a lot of work applying our more quant data to discretionary managers as well. So, helping them build decision support tools.

 

Dan Dalton: 

So, it may not be programmatically put into the investment strategy, but it gives them a kind of [inaudible 00:44:55] information.

 

Simon Smith:

It quantifies and shows them information that they wouldn't necessarily have at their fingertips. So, be that for relative value analysis, be that just for general insight, be that for the macro conditions in which their models are operating in or they're investing around. And so, that's something that's been very important on that large universe as well. If you think the discretionary manager base is still a large proportion of the investment managers that we deal with.

And then as we think about the post-trade side, obviously we then have all of the valuations, the mark to market and the risk that also sits on top of our market data, but also some of our research data as well. So, increasingly a lot of our research content feeds into that side as well. So, it's something that's really, it's put a different focus on our research groups as they think about not just the written content and the models that they've generated from the equities side, but the content that we generate from the broader strategy group there as well.

I think you mentioned that previously the origins of a lot of our products are around providing transparency or around helping our clients operate in markets. As we see this shift into private markets and our footing in that is maybe slightly different than it is in traditional public markets, what are the common problems we're hearing from clients in that space and how can we help? Where are we positioned to help with that?

 

Dan Dalton: 

I think if you ask some of the pricing direct team or the index team, it reminds them of the beginnings of much of what they did in fixed income and macro. And while they're public markets, they can be opaque and intelligence often only available end of day and what have you. And so, I think that the challenge is the same in the private market space, but it's unique because a lot of the information that you have around your investment in a private company or private security is actually unique to you in the terms that you negotiated with that client. And so, when we look to the traditional spaces where we would gather information at JPMorgan to help create some of those things, they're not necessarily available.

 

Simon Smith: 

So, it's not a one to many.

 

Dan Dalton: 

And we're obviously building up our secondary trading capabilities in the space, which means we're building up quantitative research in the space, which means we're starting to get our own ability to price on the desk, as you know with pricing direct, completely independent, but has a one-way access to some of those quantitative models. So, that's helping us think about the strategy there. And we've had some good success, as you know, in the build out of private placements, private structure credit.

When you look to things like private equity, while we would normally mine our trading desk, maybe even our research department for information to build some of these products, we can look beyond. We have a large banking business as well, and we're at the start of some of those transactions. And so, there's a level of intelligence within the firm that we know exists, but candidly, that's a journey that we've just started.

In fact, we just launched a beta private equity model. And I think what's unique is the valuation tend to be a bit more bespoke because the client has to feed us information. That information can be vast amounts of legal documentation, financial information, and actually new technology. So, if I turn the use of AI on ourselves, we can interrogate that information actually quite efficiently and we can spit out valuation for that client. But what we won't do and what we can't do is make that a common valuation if it's based on that client's specific information. And so, it's very different, but we think we can get it going.

 

Simon Smith: 

Yeah. It sounds like it's a bit of a blend between the securities valuation that pricing director and the derivatives piece where clients are required to...

 

Dan Dalton: 

A lot more information in that process. So, I think on the pricing side, we're good. And then on the index side, again, if we can build those transparency products, we think we can make it more accessible. And so, we're also in the process of evaluating where to put some investment on that side in the build out of some of those products.

If I look then to the platforms and Fusion specifically, there's many clients build their own security masters, some clients buy them from vendors. They normally do not have a single security master that goes public, private, and that's very much at the core of what we're trying to do in Fusion. And so, to give clients a common layer that allows all of that to be connected and fed into their investment processes as one thing overall, because ultimately, as you said, the allocation's going up is really quite fundamental.

Jason Mirsky: 

Hi, I'm Jason Mirsky, Head of Data Solutions in Security Services.

 

Ashley Peterson: 

And I'm Ashley Peterson, Global Head of Fusion Sales.

 

Jason Mirsky: 

So Ashley, how long have you been with J.P. Morgan? Where were you beforehand? What were you doing? How did you come to be here?

 

Ashley Peterson: 

So I joined J.P. Morgan in 2023, actually working on the Fusion product. Previously, my experience had always been in markets. So I spent 13 years in markets covering institutional asset managers and hedge funds and saw a shift around the time that MIF had happened that clients were taking some of their spend and putting it towards data. So that started me on this data journey. And when I met with the Fusion team and they were talking about the innovative process and the journey that they were on, I got so excited to join the product team. And then shortly thereafter, I joined the broader data and analytics sales team, which is where Fusion sales sits.

 

Jason Mirsky: 

So as a kid growing up, it was data?

 

Ashley Peterson: 

Originally it was being a vet, but I guess that's very data orientated too.

 

Jason Mirsky: 

Close enough.

 

Ashley Peterson: 

What about yourself?

 

Jason Mirsky: 

Before I came to J.P. Morgan, I've been at J.P. Morgan about three years now. I joined in 2022, so just a little before you. And before that, I spent 23 years at an analytics firm. Analytics and indices is what they're known for. And so most of my career has been in analytics.

 

Ashley Peterson: 

So data management is critical to our clients. How do you think about the data management journey and why is J.P. Morgan making the strategic decision to go into data management?

 

Jason Mirsky: 

We'll go to the data management origin story. Every superhero or super villain needs a good origin story. And so I'll let you choose whether we're the hero or villain.

 

Ashley Peterson: 

Hopefully hero.

 

Jason Mirsky: 

Hopefully hero. But in terms of the origin, we are doing this because this is what our clients need help with. And they're all on their own data journeys. A lot of them have decided they're moving to the cloud in some way. And a lot of the ways that security services deliver data is by FTP, secure email or other channels like that. And our clients were saying, "Well, could I get my custody data directly into Snowflake or my fund accounting data directly into Databricks or AWS or Google BigQuery or Microsoft?" And so we want to be able to put the data where our clients are going to use the data.

That's what really started the journey. But then once you have the data where you want it, you realize there are other problems that you need to tackle. It's not just about the pipelining of data to the destination. You want the data in a similar shape and format, you want common identifiers in the data, you want the ability to express your opinion on the data. And so that starts to really get into the heart of data management and what we've been building with Fusion by J.P. Morgan in terms of an enterprise data management platform.

So you've been speaking to clients a lot. What do you see as problems that our clients have and are there differences in terms of problems in different regions?

 

Ashley Peterson: 

Absolutely. We've seen demand across all the regions, which is nice to see, but there's different market maturity in each region. So APAC is one that surprised us the most. Originally going in there, thinking about the technology journey, there are a lot of asset managers and asset owners that are very sophisticated, but what surprised us is that there was a good chunk of clients that were still slower to adopt some of those new technological advances. The EMEA region, the European region seems a little bit further along in that data management process. Our first solution was an ESG solution. So obviously in that region, there's a lot of traction there with the regulatory reporting that's required.

 

Jason Mirsky: 

Sure.

 

Ashley Peterson: 

And then lastly, in the Americas, it's a bit different, very similar to APAC where it's fragmented. So we have Canada, we have the US and we have Latin America. And what we see is that there's a large incumbent of data management platforms that exist. So typically with a data management platform, you're not shifting your data management platform frequently. It's usually every 5 to 10 years. So with that, where there are incumbents, we don't see that shift happening as quickly.

 

Jason Mirsky: 

Right. So the destination used to be just to get into the cloud, and more and more now that destination is the cloud and AI. So what does that mean for clients?

 

Ashley Peterson: 

So all clients come to us with challenges around flexibility, scalability, data governance, data quality. So we hear that from both asset managers and asset owners. We like to think about it in a triangle. All clients are asking for better, cheaper, faster. And when we think about better, so that's basically how we're doing operational efficiencies for clients. When we think about cheaper, that's cost management and focusing on that. So most of the time is spent around those two angles of the triangle, but now with AI, the faster component is being driven very quickly.

 

Jason Mirsky: 

Faster, better, cheaper. I love it.

 

Ashley Peterson: 

So one of the other challenges we see is around private assets. So data being trapped in term sheets, PDFs, emails. So extracting that unstructured data and making it usable. So a lot of clients who are investing both publicly and privately want to make sure that data's extracted and then they're able to see that whole of portfolio view.

So regarding clients in their cloud journey, we see clients typically sit in three buckets. One, they're either on-prem, two, they're in a hybrid, either on-prem and on the cloud, and then finally on the cloud. So from that perspective, what do you view as success in that journey and those transformation steps for the client?

 

Jason Mirsky: 

It's a good question. When you think about success in that journey, you are not just trying to get your data into the cloud, you want the data easily discoverable. You want the data easily accessible. You want the data entitled so that only the people who are supposed to have access to the data can get access to the data. You also don't want to impose a lot of work on the end user after they get access to the data. So there's a process in the industry called shift left. Can we do the normalization of the data upon entry into our data ecosystem rather than after exit from the data ecosystem?

Another aspect is a robust encompassing data model. So can you model all of the relationships that are within the data in the data architecture itself? That way it makes it very easy to extract that information with the relationships intact. And so when you think about that as a cloud set of goalposts, it naturally flows into AI goalposts in that when you have that data accessible, discoverable, and titled appropriately in a robust data model that's normalized, then you can start to think about exporting the semantic layer, the ontology of that data along with the data so that your LLMs can make use of it and generate high quality results.

 

Ashley Peterson: 

And I've heard you use this analogy before, but it's very similar to changing the wheels on the bus while it's being driven. So it's something that's very complex to solve for a client while making sure not to disturb their day-to-day operations.

 

Jason Mirsky: 

Absolutely. It's really difficult to change while data is in play. And so really what you need is the in-house expertise or the third party expertise on people who have been through that journey before in order to do it.

 

Ashley Peterson: 

And I think that's where J.P. Morgan is a very strategic player. We have great assets, but let's talk a little bit about how J.P. Morgan can strategically use those assets for something like this.

 

Jason Mirsky: 

Yeah. I mean, when I think about what we make use of in terms of the assets of J.P. Morgan, it's really what we call SMEs, subject matter experts. We have subject matter experts who work in our custody business who help us design our data model, subject matter experts from fund accounting who help design the data model for fund accounting, middle office trading services. And so we're pulling from the expertise of the bank when we're designing the data model that goes across the data within our architecture.

The other thing we do is we subscribe to a lot of data. When you think about all of the big data providers out there at Bloomberg or Refinitiv and ICE, like we have all that data at J.P. Morgan, we're creating our own security master at J.P. Morgan. So we are familiar with that process of mastering data from multiple sources. And while we can't give away the J.P. Morgan data to our clients, the data that they're licensed to receive, we can distribute to them. And so we make use of that expertise of handling and merging and mastering all of that data on behalf of our clients.

So after the data is normalized and modeled, how are the clients using it?

 

Ashley Peterson: 

So typically we would bucket it into two different categories. There's the technical user and the business user. So the technical user, those would be the data scientists, the data analysts, and they are using it for very specific, unique use cases to support the overall business. And if you look at leaner organizations, they might not have the capacity to go out and hire so many technical users or employees that are native in APIs, Python, Jupyter Notebook, et cetera. So we also need to cater to that business persona. So making sure that the tooling is there, whether that's the UI where you can access the data in a low code, no code way, or making sure that they have the dashboards and the visualizations to do that.

So one of the largest challenges we hear from business personas is the linking of that public and private data. So can we dive into private assets and how data management can help solve that?

 

Jason Mirsky: 

So before we get started, within this same market series, Montserrat, Larry did a great episode on private markets. I suggest you watch it if you haven't yet already. But going to the problem with private markets data, inherently it's private data. So you just can't subscribe to any vendor and get your private markets data. The data is hidden away in GP portals, it's unstructured, it is often in PDFs. It's often in emails. And there are technologies out there, particularly with gen AI, that are starting to extract that data from unstructured documents and make it structured. And so we make use of some of those technologies. But the second problem is that often your private market investors are using more than one fund services firm. And so each of those fund services firms is going to provide fund accounting data in a different format with different data points.

And so the second problem is aggregating that data across fund accounting firms. And then the third part of that is aggregating across public and private data. So even within a firm here at J.P. Morgan, we have a separate department that does private fund accounting versus public fund accounting. And so clients need to aggregate that to get whole of fund reporting. And so the data model that we've built up with Fusion helps to aggregate the data consistently across fund accounting vendors, but also across public and private.

The next problem in the private markets is entity resolution. There's no real identifiers for securities in the private markets. Often all you have is a name. And so we've built out a whole lot of machine learning to try and match names. So you can think of it as a whole complex set of rules where we take data from one vendor and data from another vendor, and we just try and match the name. That works sometimes. Let's capitalize the name on both sides. Now we get a little more matches. Let's expand out all abbreviations that are within the names. Let's remove punctuation.

And so you do more and more rules until you can come up with high fidelity names. And for those that you can't match, maybe there are other points like country of incorporation or revenues that you can extract from the datasets that'll help you in that matching. So we use that same entity resolution matching process across all different kinds of entities, whether that's securities, companies, counterparties. And so the matching process, what we call spining, is a key element to our data offering.

So look, we've talked about private markets. What about regulation? How's regulation affecting our clients and how's it driving their data needs?

 

Ashley Peterson: 

Regulation's always a hot topic.

 

Jason Mirsky: 

Always.

 

Ashley Peterson: 

So globally, we see a lot of countries looking at data leaving their country. So in Europe, we're looking at GDPR, same issue in Saudi Arabia where they want to make sure that when data either can't leave their country or when it does leave their country, that it still abides to the same regulation of where the data originated.

We can also think about regulation in two separate buckets. So one is where the regulation is driving the client. So an example of that is the new reporting regulations out of Australia. So we hear a lot of Australian clients looking for datasets around modern day slavery and supply chain because of those regulations. And the other bucket is where the client's trying to set their own internal goals before a regulation is rolled out or potential audit. So we have a client in Singapore that looked at their data management platform, wanted to create a more robust governance and audit, and that's what drove them to change their data management platform.

Analytics is your bread and butter. So maybe we pivot from speaking about data management specifically and talk about how, now that we have the data managed, how we're leveraging it from a Fusion analytics standpoint.

 

Jason Mirsky: 

Sure. So the most important element is high quality data to go into your analytics. But our clients are asking, if you think about security services, we start, we custody the assets. Once you know what assets you have, you want a valuation on those assets. So we strike a NAV with our fund accounting process. And then the natural question is, well, what's the performance of my portfolio? And so we help our clients with performance, but also performance attribution. Why is my performance in a certain way vis-a-vis these benchmarks?

The same is true on the risk side. We help our clients with ex-ante risk, where we can understand what's the value at risk. We can stress test those assets. We also do risk attribution. We help our clients with liquidity analysis, understanding how many days it might take to sell a particular asset or portfolio, or what's the cost if I needed to sell within one day, like those sorts of analyses.

As you mentioned in Australia, there's ESG regulation in Europe, there's also ESG regulation. So we help our clients with ESG analytics, creating pillar scores for their portfolios, carbon footprints for their portfolios. We do post-trade compliance to make sure they're not violating any of their policies or any of their regulations. And lastly, when we think about the private assets, we want to do look-through analysis. So understand what's within the portfolio underneath that fund name and be able to aggregate that across funds, both publicly available companies, so mutual funds, but also private funds.

So in addition to analytics, what other things are our clients asking for with the data?

 

Ashley Peterson: 

Even though we're talking about a post-trade world, clients still want to access their data at speed. It's not just the accuracy, but getting that data right and in a timely manner. And second to that, we get a ton of demand for dashboards, right? So that business persona, even the technical user, wants to make sure that they're able to visualize all of that data in one place. And then lastly is really that audit trail. So making sure when there's a change to that piece of data, that they could go back, see who made the change, if it's a different team, who's approving that change, and that audit trail's becoming increasingly important for clients.

Clients are asking to backtest their data. So we want to make sure that we also give that data in a bitemporal way. But I would be remiss if we were having a data conversation and I wasn't asking about AI and what you're seeing from clients from an AI perspective.

 

Jason Mirsky: 

Yeah. I mean, that's what everybody talks about today. And since ChatGPT was released, it's very easy to put together some sort of pilot using an LLM. And a lot of clients have embarked upon this and they see initial success, but what's difficult is doing that at scale in a repeatable, robust manner with good governance and the proper entitlements so that you're not releasing data you're not supposed to be releasing. And so having a data platform that can work side by side with your governance policy, your entitlements, and allow you to create AI at scale in a robust, repeatable manner is really the goalpost here.

 

Ashley Peterson: 

That makes a lot of sense. Fusion sales had over 500 client conversations last year and every client that we spoke to has released some sort of ChatGPT or LLM suite type functioning within their organization. And what we hear and talk about with clients is J.P. Morgan, because of the breadth and the depth of our business, we are looked at as thought leadership. So I know Jamie's mentioned that we spend two billion in AI a year. So while the buy side and the sell side might approach AI very differently, they're looking at J.P. Morgan and also our chief data and analytics office to kind of guide and shape that conversation.

 

Jason Mirsky: 

Yeah. And when you think about it, there's really three pillars here. There's the data, there's the platform, but then there's the operating model. And people neglect the third one, the operating model, but that's the critical element for success in your endeavors.

 

Ashley Peterson: 

Which goes back to the bus analogy.

 

Jason Mirsky: 

So one of the keys about having a successful AI project is having high quality data. High quality data generates high quality results, so you need to have the pipeline, have the technical data quality, the business data quality, what's called TDQs and BDQs in the business. And you have to have that data normalized. You have to have consistent identifiers. You need a data dictionary associated with that data. You need a semantic layer and you need to be able to deliver that semantic layer out to the LLM so the LLM can understand what the data means, and that will generate higher quality results for you.

 

Ashley Peterson: 

So what's something that is not standard now that we expect in the next 5 to 10 years to be standard, and where is the puck going?

 

Jason Mirsky: 

When you think about the three phases of AI that we see, the first is just play with the LLM itself. The second is, can we give the LLM a knowledge base to work off of? It's called RAG, but the end result here is that you want the LLM to be working off of your data, your data as a strategic asset. And then the next phase after that is agentic AI. What I see really in five years that we're going to take for granted is that chat for data with your own data, your own strategic asset will just be ubiquitous.

Mohit Bhargava: 

Hi, I'm Mohit Bhargava. I look after the global credit structuring team here at JPMorgan.

 

David Rademeyer: 

And I'm David Rademeyer. I lead America's equity structuring.

 

Mohit Bhargava: 

So, David, what do you think equitization of credit really means?

 

David Rademeyer: 

Well, over the course of my career, I've seen a starting point where equities and credit really had very different market dynamics where credit kind of traded by appointment or point-wide, equities were trading on exchanges. They were already decimalized by the time I started. And what I've seen over the last 10, 15 years, and I imagine you've seen much more closely, is a lot of convergence with daily trading volume, market debt for market completeness, really converging the ways that people trade. My sense where it really started was the ETFs, which obviously being an equities person, we were looking at very closely and we were users of in the derivatives business going back to 2010. I remember having conversations in the early 2010s with EM traders, we were asking if you wanted to trade the EM bond index, how much could you trade in a day in like $25 million?

And I was like, "How would you do $100 million?" And looked at me, startled and said, "Well, there's a price for everything." But little did he know we had strategies that were trading $600 million of the EM bond ETF in a day, and we were able to do that for six bips of slippage. Obviously, the credit trade has caught on pretty quickly.

 

Mohit Bhargava: 

Yeah.

 

David Rademeyer: 

Obviously you've seen that. Love to hear from your side.

 

Mohit Bhargava: 

Yeah. And I think it's been a journey, as you said. I started in 2007 at JP in London, and the way we used to trade credit back in the day was through dealer chats. You would engage with multiple dealers, try to find the best price and execute. That's obviously evolved massively. I think it started with what I would call electronification of credit, not so much equitization to start with because when it came to derivatives, we started moving away from those dealer chats and we went on to SEF, which is swap execution facilities. And we started clearing those trades, especially indices like CDX iTraxx. When it came to bonds, we have APIs and electronic trading platforms from dealers and other parties that are out there to trade cash bonds more easily than through chats and so on. So, it became more sort of a, how can I make it more sort of an electronic platform and make it more efficient that way? But it's now taken a shape of its own.

And I think with the growth, as you said, of ETFs, which has led to the growth of portfolio trading and vice versa, because portfolio trading leads back into ETF growth as well. And customers have become used to trading long lists. We've seen more and more volumes going through credit, which has meant lower costs, more liquidity, and ultimately a more efficient market, which arguably is now attracting more different types of investor clients who were not part of the credit market before.

 

David Rademeyer: 

Yeah, absolutely. I think when you look at equities, electronification happened on the derivative side in the 80s, happened on the cash side in the early 2000s, around the time of the peak of the dot-com bubble. And by the time I joined already, people were executing through algos because the people that they were facing were algorithms themselves and you had to keep up. But also they had different ways of being able to trade. You could trade a portfolio of equities where you're really just targeting aggregate statistics in terms of average market cap and other factor concentrations, and banks were able to show a price. And obviously the credit ETFs created a lot of price discovery on the credit side, which were really helpful, but also they created a need amongst the market makers and the credit ETFs to be able to trade bonds the same ways in order to be able to satisfy the create/redeem.

And although I was observing this from a distance, really saw our colleagues in credit trading step up to be able to facilitate that so that the market makers could find a basket that fit the dealer's inventory and also fit the ETF provider's requirements. And that was technology that wound up being incredibly useful, not just for the market makers, but also for a lot of credit portfolio managers, a lot of insurance companies and other clients that I've spoken to. As a specialist, I'm curious, what did that look like up close?

 

Mohit Bhargava: 

I think there are two factors with technology in credit. One is when it comes to making markets on bonds, we don't have a single ticker. We have even take Apple, for example, it has a single stock that you can trade. Whereas on the bond side, you have more than 50 bonds of Apple itself. To make markets on those many bonds, to be able to see the two sides of the market, to be able to anticipate whether you can exit that risk or not, was really, really difficult. Technology helped in that. We are able to build those platforms that can cross these flows. There's algo trading, which is coming to help market makers take position on one bond and maybe sell a different bond, which is highly correlated and take that, extend that from bonds to lists and portfolios. Similarly, with the advent of ETF and the expansion of ETFs, market makers were able to expand the growth of portfolio trading and hedge with ETFs and that ecosystem. So, the whole continuum between algo trading, electronic trading, all the way to portfolio trading and ETFs has been enabled by technology.

 

David Rademeyer: 

So, we've already touched on ETFs, but the next front in the centralization of liquidity in these standard products has been in futures contracts and some of the corporate bond index futures. Can you tell us more about that?

 

Mohit Bhargava: 

Sure. So, I think the corporate bond index futures, I have been there for a while, but they really came into popularity sort of 2023. So, last three, four years, we've really seen the volumes increase. The advantage of those is that you don't need an ISDA. You can trade with the exchange, you post an IM, which is generally fairly efficient, and you can net that against other trades. So, what that's doing is basically bringing a lot more participants into the credit markets, which has been difficult in the past because of these huddles.

 

David Rademeyer: 

So, we've talked a lot about the centralization of liquidity that the ETFs have created, but another really interesting product that's grown up in the last few years has been the credit index futures. Could you tell us a little more about that?

 

Mohit Bhargava: 

Yeah. Credit index futures have been around for a while, but they grew in volume over the last three or four years. The advantage of credit index futures is basically they trade on the exchange. So you don't need an ISDA to trade. You post an IM, which is fairly efficient. So, it's attracting a lot of people into the credit markets to express their views in this manner which they couldn't before. So, David, from your perspective, now that we've talked about what equitization means, how do you think from a, let's say an outsider's perspective or client's perspective, how do they see the credit markets today?

 

David Rademeyer: 

I think it's pretty different. Obviously there's been a growth in the number of issuers, number of issues, but also I think the market really used to be one where the dominant strategy was to try to buy new issues and then hold them to maturity. And so, building up a portfolio could take a really long time. We were at a conference which we co-hosted with Vanguard talking about their new actively managed bond funds. And the head portfolio manager there was talking about how seeding a fund used to take a month and now you can just identify what the characteristics are and you can just trade that with very low turnover. You're much more able to source secondary market liquidity and really trade it like an asset class as opposed to by appointment.

 

Mohit Bhargava: 

And I think even from the issuer's perspective, it's probably much more efficient to issue in the public markets because some of that risk premium is probably going away with more liquidity. So, David, we've talked a lot about how credit is becoming closer to equities in terms of higher volumes, better liquidity, lower costs. Where do you think that goes? Do you have any blue sky ideas around where it could go in the future?

 

David Rademeyer: 

Yeah. So, in equities, we started out with SPY, then we got the Qs, and then we've seen a real proliferation of the different types of trading tools that people can build with ETFs. And I think we've seen similar things on the ETF side. I think 15 years ago, credit ETFs meant HYG and LQD, maybe JNK, a couple of others. And obviously there are a lot of ETFs that are trying to compete directly with that, but we've also seen liquidity moving into more targeted, more tailored products. So, the short-term, medium-term, long-term credit ETFs, some of the target maturity ETFs. And I think the next evolution is probably going to be in terms of things like sector ETFs or maybe even for some names, single issuer ETFs to allow people to express more tailored positions, factor ETFs and such, which have been very popular on the equity side.

 

Mohit Bhargava: 

Yeah. And I think that probably makes sense to talk a little bit more about the different products on the structures. So, given you from the equity structuring, I'm from credit structuring, what kind of products do we think are things that we should be working on and or are working on already?

 

David Rademeyer: 

Yeah, so we're always kind of the flea on the dog of the market. And so, we really rely on the liquidity and the underlying categories. But on the pure derivative side, it's really exciting to see these increases in liquidity because it allows for a lot of different kinds of derivative products to be created that would be impossible in less liquid markets. There are a lot of volatility derivatives, different types of option payoffs which have existed for a really long time on the equity side, 25, 30 years, barrier options. Options that knock out if the volatility goes up, which can provide cheaper protection for a specific view, which really were the domain of the most liquid equity indices. And increasingly we're starting to see clients trading those on credit ETFs.

 

Mohit Bhargava: 

I agree with you. I think on the payoff side, there's a lot of innovation that we can achieve in credit now. There's obviously been a lot of innovation so far on the derivative underlyings with iTraxx, CDX indices, and now we are seeing more on the bond side as well.

 

David Rademeyer: 

Yeah.

 

Mohit Bhargava: 

And a lot of what we look at on my side, when we look at new payoffs, we look at what was done in equities before, because for many years, you just had so many variables to play with and we had limited. So, as we get more and more liquid points around different parts of our market that are tradable, I feel it gives us more on different payoffs.

 

David Rademeyer: 

Yeah.

 

Mohit Bhargava: 

Apart from payoffs, though I also think there is things like relative value between credit and equities where credit is sort of considered as a similar to a put option on a stock, right? So, take a single company, for example, you could either buy a CDS protection or you could do a put. Do you think that is becoming, from your perspective, more of a dynamic in terms of relative value now that credit is in the same category as equities in terms of liquidity?

 

David Rademeyer: 

Yeah, absolutely. I think definitely a lot of macro hedge funds now have a derivative expert who is thinking about, "Well, given the house view on what is going to happen in markets, what is the most efficient way of expressing that view?" And so that could be an actual outright relative value trade, but also it's just that there was a point in time where if somebody had a view, even if it was a credit cycle view, they would've had to express it through foreign exchange or interest rates or an equity option. And increasingly they're able to just directly express it through credit, which is very interesting development.

 

Mohit Bhargava: 

Yeah. And as you said, it not just lends itself to relative value, but also looking at different asset classes to see which is the cheapest option, for example, like if there was a war hedging against the next economic cycle and so on. In terms of developing strategies, systematic strategies, I think there's a lot of... So, talking about my experience in credit, because when we started, I was still back in London in 2013 when I looked into, or we started looking into QIS strategies in credit. And at that time it was very difficult to create something which was a systematic algo in credit because you really had only few points that were tradable and had an observable third party price. And those were just the main CDS indices. Now that we have more points along the tenors, along different points on the curve in CDS indices, and also for single-name CDS and bonds ETFs also, it lends itself to a bigger space in the credit QIS and systematic space, right?

 

David Rademeyer: 

Yeah. I mean, I think one of the things which I'm watching with great interest, which is on the pipeline, is that we're seemingly very close to being able to bring to market swaps on indices of actual corporate bonds for clients who want to express specific sector factor or just hedge out aggregate beta in a more cost-effective manner. It's not just the ETFs or the futures now. How far away do you think that is and what do you think are going to be the most popular ways of using that technology when we have it?

 

Mohit Bhargava: 

So, again, we've taken the equity playbook on this one. Equity Delta One has been very, very successful. We've taken that playbook in credit. We have thematic baskets or basket which are essentially sleeves of the indices and that are out there already today on Bloomberg. So, you can actually go and trade those standardized baskets on the click any day, anytime. What we plan to do is build on those baskets because right now they're designed as sleeves of the broad index or fairly topical baskets. And the next step in this evolution is going to be systematic strategies along the lines of what's happening in equities using these baskets.

 

David Rademeyer: 

The other thing that we've seen in terms of convergence is one of our most successful products and one of our top priorities on the equity structuring side going back a decade has been our Nexus platform, which is used for a lot of different applications for clients who want to be able to manage their own strategies and absorb them synthetically. But one of the products that is called Nexus Prime RMAR, which is a product that allows you to create a managed account that clones your master strategy and then deliver the performance of that account synthetically, very useful for liquid alt strategies, for example.

And one of our first clients, I believe our first client with the US hedge fund as allocator, was a client who wanted to be able to do a relative value strategy, equities and corporate bonds. And we were able to deliver that in a single managed account, deliver that as a single swap, which is obviously a trade that we were very excited to work on because we love to be able to solve our clients' problems, but also very exciting to be an innovator in terms of being able to straddle different asset classes.

 

Mohit Bhargava: 

Absolutely. And I don't think that's a one-off, right? Because we see a lot of pods on the buy side that are looking at credit and equities together now because you have in credit similar indices and similar platforms as well like Nexus you mentioned where you can exercise your strategy and the building blocks are very similar to equities. So, combining different building blocks and putting them together in Nexus is becoming easier and easier.

 

David Rademeyer: 

Yeah. So, obviously this is all really exciting and as structurers, we're always thinking about how could things go more right? But I'm curious, where do you see the limitations? What is as far as we can get based on the current trends?

 

Mohit Bhargava: 

So, in my view, and I think you and I have talked about this before and you might disagree with me, but I think in my view, there is a limitation to how far this can go. Naturally, credit has a lot of... Like for each corporate, there are lots of different bonds with different terms, different maturities, coupons and so on. And that just makes it harder inherently. We can help with technology, ETFs have helped and portfolio training have, but that inherent nature of the credit market will remain. So, there is a limit to how far we can take this.

The second thing in my opinion is the liquidity on the ETF, credit ETF market in particular, has not been tested many times. It was tested during the crisis that we just went through in April, we saw the crisis during COVID. In both of these crises, what we saw is that it was a very useful tool for hedging if you are hedging on day zero, but after that, it was trading at a very large basis to NAV.

 

David Rademeyer: 

Yeah.

 

Mohit Bhargava: 

So, it is a very useful tool to have, but you want to have other tools in your arsenal as well that you can deploy based on where the ETF ends up versus the rest of the credit market.

 

David Rademeyer: 

Yeah. And I think that that gets... Ultimately, the disagreement is the credit guy thinks that NAV is reality, the equity guy thinks the ETF price is the one that's real. And that I think is going to be the thing which is going to be the challenge here is as credit markets become more liquid, I mean, it's possible that it's just the ETFs went up trading closer to NAV with the same volatility as NAV has. But my bias is to think that it's more likely that just as prices become more transparent, as they become more real time, we're just going to see a lot more volatility in the credit market in the same way that we do in the equity market. And as you and I know, that's not always what clients want. They say they love transparency, they say they love liquidity, but then when their portfolio marks to market 10% in their face, they look wistfully at the private markets and think, "Isn't it nice that this price goes down so little and so slowly?"

I'm curious, even though it hasn't equitized per se, but obviously the big story in credit these last few years, other than the equitization is private credit, do you think that there's any overlap between those two things?

 

Mohit Bhargava: 

So, if you think about equitization as making things more liquid and more transparent, then definitely private credit is just the opposite. But the way I think about equity markets, and again, maybe this is from an outsider's perspective, is equity markets have two parts. One is the deeply liquid equity markets that have been around for so long, and there's also a private equity market that has been around for a very long time. So, the growth of private credit is not a contradiction to equitization of credit markets, it's just completing that there are two parts of the market.

And again, private credit has been there for a while, it's not new, but is growing. And what the two phenomena of making public credit markets more liquid, what we started off by saying that's the equitization of credit. And the other phenomena of the growth in private credit markets is basically giving you this sort of a barbell for both investors and issuers in terms of two very different options to exercise for the investor's perspective, they could trade the public markets much more efficiently to have that view, or they could go to the private credit markets for an extra premium if they can understand the companies and have a longer term views. And similarly for the issuers, it's about getting a much more efficient execution in the public market because of the compressed premium arguably, or they could go to the private credit markets where they get customized terms or speed of execution.

 

David Rademeyer: 

So, Mohit, we've talked about why there's more participation, there's more different types of strategies that people are implementing. Why didn't people do this before?

 

Mohit Bhargava: 

I think pre-GFC, there was a focus around derivatives. And a lot of my people in my seat were building complex CDOs and other structured products on the back of corporate credit, and that was actually big volumes. That was selling a lot. It was reaching the retail clients as well. After the Global Financial Crisis, I think was a reset in view of how should we think about credit. Now, the CDS and CDS indices exist, but from a mass acceptance perspective, there was a desire to have a more cash-based product and focus more on cash liquidity than just synthetic derivatives. So, over the last 15 years or so, there has been push in that direction, but a lot of this is up to the acceptance from investors at large. And once they accepted ETFs as a way to express their credit views and list trading, portfolio trading as ways to express views on credit in large sizes, that just sort of snowballed into a big phenomena.

And now we see the structural change in the credit markets. There was a bit of a reset in 2007, 2008, and it took a while to get back to a different approach. And I feel like with the cash market, both on the single bonds, ETFs, portfolio trading, that whole continuum is now complete with algo, and now there are more and more hedge funds who are looking into the high frequency trading in credit, which was something which is only a domain on the equities side in the past.

 

David Rademeyer: 

I mean, I'd say from my vantage point, it feels like the credit markets have always been around, they've always been really big, but historically they were really an investor's market just because with the lower liquidity, if you tried to be more tactical, bid-offer would eat all of your alpha. And now with all of these different tools, it feels like there's actually scope for an actual trader's market for people to be able to generate alpha net of transaction costs and have much more dynamic strategies. Would you agree with that?

 

Mohit Bhargava: 

Absolutely. There are two things that come to mind when you ask me that question. One is the absolute level of cost itself. And we've seen data where the costs have actually come down tremendously, in some cases, 20% to 25%. The other thing that's happened is what we look at is the cost-to-VaR ratio. And I'm not saying that credit markets have become more volatile, but necessarily, but I think you're able to go in and out more easily and the cost that you pay versus what you can achieve by just the movement in the underlying prices makes it much more palatable for our customers to come in as a tactical view rather than just a long-term hold to maturity sort of view.

 

David Rademeyer: 

Is it also easier with the technology to actually screen for ideas to express a view as opposed to just having to look at what are the bonds that your dealer has an inventory and wonder if they fit what you want to do?

 

Mohit Bhargava: 

Definitely. Like I said, previously, we had a situation where you would look at prices from dealer runs, and if you go to the dealer and ask, "Where does this bid or offer?" It would be very different to where the run was sent just a minute ago. So, it was not possible to have a screening of all the prices real time to look at where the arbitrage was or look at where you can go in and out and try and have some sort of short-term views. With a lot of this going into the e-trading platforms and click to trade, even for JPMorgan, the platform on algo trading has increased massively. We have now larger and larger size tickets that are going through e-trading rather than having an actual trader pricing that. So, that parsing of bond data and trace and all the other data that's now available makes it easier for customers to have a holistic view on what's going on in the market, better pricing transparency, and therefore they are transacting more.