Hedge Fund Huddle podcast
The search for fast and reliable data
Episode 7, Season 1
It is no secret that getting reliable data in a timely manner is becoming more important than ever. Now, also consider the speed at which this data and information is becoming available to hedge funds, and the race is on to turn it into something actionable. In this episode we are joined by Jimmy Karalis, Managing Director, Data Sourcing & Strategy, Balyasny Asset Management and Patrick Flannery, Head of Low Latency & Direct Feeds, London Stock Exchange Group to give us the low down on low latency.
Host: Jamie McDonald
Jamie: [00:00:05] Hello everyone, and welcome to another episode of Hedge Fund Huddle. I am your host as usual, Jamie McDonald. If you've been listening to this series, you’ll know that we're trying to peel back the curtain of the hedge fund industry and look at how these titans of the financial world keep putting-up these impressive results year after year. Now one of the reasons they can do this is because they get data fast and they get data that’s reliable, and that’s the topic for today’s episode. We're looking at Low Latency. Now, before we get going, just a bit of housekeeping. If you are enjoying this series, please do give us a like and a follow on whichever platform you use and if you have any ideas or comments we’d love to hear from you. So email us at email@example.com. That's firstname.lastname@example.org. Any ideas for future shows, future guests or any feedback on episodes so far. So as you know, I’m Jamie McDonald. I was a hedge fund manager at SAC Capital now Point 720.72 for many years, but that was about ten years ago. And the way in which portfolio managers are getting information today is very different to how we got it back then. Now, luckily, I have two experts to help me today on today's show about how portfolio managers in the hedge fund world are getting faster and better information. So let me introduce them. First of all, we have Jimmy Karalis from Balyasny Asset Management and Patrick Flannery, who's at London Stock Exchange Group. Welcome to the show, guys.
Patrick: [00:01:27] Thanks for having us.
Jimmy: [00:01:28] Great to be here.
Jamie: [00:01:30] So when I was back in the hedge fund world, it was all about getting data that was reliable, data that was monetizable and getting it fast. So with reference to that, and obviously we want to hear Patrick about May Street as well and how it folded into London Stock Exchange Group. Jimmy and Patrick, would you mind introducing yourselves and perhaps we'll start with you first, Patrick.
Patrick: [00:01:50] Yeah, my name is Patrick Flannery. I run the Low Latency and direct feeds group at London Stock Exchange Group. So I had started just over ten years ago. I was the CEO and Founder of May Street, which was a capital markets software company. We had a couple of products, one was a suite of software that interpreted the data that came out of exchanges. And as we were building that, we started to collect what turned out to be really high-quality raw exchange data. And we made it our mission to create what we thought was the highest quality data in the world, which remains our mission at the London Stock Exchange Group. This pristine historical data and the word historical every day is becoming a little less old, right? That historical data can become newer and newer and broader and broader. So, you know, we have all sorts of interesting things that we're doing. So that's why I'm here today and continue to do what we did at May Street as part of a larger company.
Jamie: [00:02:45] Thanks so much, Patrick. Jimmy, a little bit about your background and what your role is at Balyasny.
Jimmy: [00:02:49] Sure. Yes, so I've been with Balyasny for six years now. Prior to Balyasny was 17 years at Citadel. Balyasny is a multi-strategy hedge fund founded out of Chicago back in 2001, primarily focused on equity long, short. Today, we have over 1000 investment professionals covering equities, macro equity, credit, commodities. And more recently, we launched a private’s business BAM Elevate, so growth equity. My team specifically data sourcing and strategy, is responsible for managing all the third party relationships with our data providers. We work closely with portfolio managers across the various strategies to understand the requirements, be able to source best in class data for them and their strategies. We're part of a larger central data org data intelligence group, which effectively covers everything data, sourcing, onboarding, data management, data integration, support, etcetera. And our mission is simple. It's to give our investors a competitive advantage through data engineering and insights.
Jamie: [00:03:51] So, Jimmy, if we can just stick with you for a second. Back when I was running a portfolio, the main area of data sourcing for us was either sell side analysis, our own analysis, which involved effectively reading 10Ks and fairly droll stuff like that, listening to conference calls and basically trying to find out where the street in inverted commas was wrong. Where are estimates going wrong? If you could just walk us a little bit through how that's changed over the last ten years or so. You mentioned third party data sourcing. So what do you mean by third party data? Where are PMs getting their edge now?
Jimmy: [00:04:27] Great question. So if I may, I'll go back at the start of my career a little bit over 23 years, where the primary data focus, for example, on a stat arbup desk was around reference data, pricing, data, corporate actions. Do we have accurate shares outstanding? How can we apply these corporate actions? So it was effectively managing the data that was readily available but assuring that it was usable and ingested within our systems. On the credit front, we're trying to solve for better pricing transparency, within the convertible bond market. So, quotes were scribbled on a piece of paper. We then entered them in a quote entry system. We automated the process by parsing emails, building intraday curves, and then essentially working with third parties, third party data, i.e. the LSEGs of the world to procure fees that gave us this information directly from the brokers. So there has been an evolution across data. But also, if we fast forward to today, the proliferation of digital information, the availability of big data has increased dramatically, right? So portfolio managers have access to a vast amount of data, various sources, social media, satellite, credit card. What you'll hear across the buzzwords of alternative data. So this data provides unique insights into consumer behaviour, industry trends, company KPIs, etc. And it goes without saying for us in the industry, technology. The evolution, machine learning has allowed us to analyse large data sets quickly, identify correlations that translate into an investment decision. We have better tools for our portfolio managers to explore, interpret the data more efficiently. So, data visualization dashboards. So, your world then in the world today certainly has transformed, but leveraging technology and third party content has allowed us to move forward in a much quicker and different fashion than we have historically.
Jamie: [00:06:30] Jimmy you said something interesting just there, which was not only about different areas to source data from, but the quality of the data. I think just now you said, like think it was just a throwaway line, but just even checking the number of shares outstanding is correct. So how much attention do you pay to the quality of the data versus like where the data is coming from?
Jimmy: [00:06:49] It's critical. I mean, that's the critical element, it's amazing for years and over 20 years, there's still a struggle amongst data providers and firms to just identify best in class corporate actions, shares outstanding, just to be one. So quality is critical. And as a data organization within BAM, our responsibility is not only to source the quality data, but also to be able to ingest, to make the data usable, to arbitrate. So absolutely important in our process. And it's critical for all downstream applications.
Jamie: [00:07:21] Patrick let's turn over to you. Could you talk us a little bit about why you started May Street? Congratulations on building such a successful company, but why did you start May Street? What was the evolution of it and whaty does May Street do now today?
Patrick: [00:07:35] So I had worked in a writing software for some equity derivatives firms. I started in 2003. And you know, ultimately, I felt a strong desire to build something. One of the things that you notice is that there is a lot of commonality across market participants. And by the way, going to something you previously touched on, it's a really, really high bar. So it's like there's a tremendous ocean of data out there, but actually turning it into something that not only has insight. For example, Jimmy just said, you have to find something where the consensus is wrong and it's both actionable and meaningful, right? So it's like, yeah, maybe they're wrong on a dividend projection, but it's not material or that's not what's driving the stock. So you have to be able to take data and actually turn it into something and that’s a really fascinating problem. And it's a really, really, really high bar, right? So, I started May Street in a lot of ways with a desire to be independent and to build things. So that's why I started it. What we learned is that we did have a unique approach in thinking that what quality meant, right? And so quality ends up mattering in that if you're making automated decisions, right? So there's not human judgment. You can't interpret things. Errors can be really catastrophic, right? They can be minor, but they can also have really serious, negative impacts. So think about it as the quality of a foundation, is it built on bedrock? So quality has been really useful for us. And, at LSEG, we want customers to feel that when they run their analysis on our data, that they get the right answer and that, in many cases, if you were to run the same analysis with different providers, you would get varieties of wrong. So we ultimately recognize that software and data go together. Most data businesses are really software businesses. And we built a software culture and we found these, really important, interesting problems to solve for firms, which is collecting the data and storing the data, transforming the data and delivering the data towards this end goal of saying, well, how do you use this to actually inform the investment process, whether that's trading or, portfolio construction or managing risk or hedging. There's all sorts of super fascinating and interesting problems. And I think one of the things that's most exciting about being part of LSEG is they're a global business. So there was a lot of stuff that was very hard for us to touch at May Street, and we've been able to start to take our template and I will say emerging and frontier markets, India and China, Vietnam. Markets that were really quite hard for a New York based, even a growth fintech just because of the way that capital markets work. So, we have access to a lot of data and we're able to take our template and help LSEG ultimately create better products by using this way that we recorded data that's lossless and multi redundant that's close to the source and that you have different ways to access it so that you can essentially try to remove bias, right? So that, our customers can say, well, what did this look like really when it came out of the exchange? So that's a long answer to why did I start it and what am I doing and how does it sort of fit together?
Jamie: [00:10:35] Well, I was wondering, Patrick, if one of the reasons why you started is because you were trading the markets yourself and you thought, I want to build a platform which is going to give me reliable data. So I mean.
Patrick: [00:10:43] I actually started for a Chicago firm, a wonderful firm and learned a lot. And I do remember walking around Chicago and the pits in 2003 and there was a lot of bravado for, I can use a less polite word from some of the traders on the floor. And it's like, what are these nerds doing here? And it's like, well, we're here to take your job. Now, it didn't happen right away. It actually took quite a while for that to happen. So it's a super fascinating problem. Today we're collecting well over five terabytes per day from some of the US derivatives markets, so tens of terabytes per day, petabytes nearly per month, which is a lot of data, right?
Jamie: [00:11:27] Extraordinary. I mean, I don't even know how you get your head around it. I got a follow up question for you, Patrick, and then Jimmy will come to you. One thing that has been a big rising source of data over the last several years has been social media. And I wonder, Patrick, frankly, Twitter, you know, if there's people listening to this show and they get a lot of their information from Twitter.? How do portfolio managers, how do they kind of filter what goes on in the social media world and find out what's relevant and what matters?
Patrick: [00:11:54] You know Twitter has been explored for quite a while. I think that the real advancement is, you know at LSEG, we have a number of products that are similar machine-readable news and similar products, in the QAC and other groups. The challenge in Twitter is that it produces a lot of content, but a lot of noise, right? Like you can't rely on it for extensive truth, which is not dissimilar to a lot of the generative AI models where it produces something that sounds incredibly true and it might be true, Right?
Jamie: [00:12:28] Well that's the problem is stuff that's even not true still affects prices. So you need to pay attention.
Patrick: [00:12:33] The not true stuff affects prices more in some cases quite a bit more than the true stuff. So how do firms deal with that? Well, it's been they've been able to ingest it for quite a while and there are meaningful and not to speak exactly to Twitter because I think the licensing on that varies. But in general, there's important licensing challenges, right? The Internet has grown up and it's kind of was in a lot of ways treated as kind of this open canvas, and you're seeing it with OpenAI and others where it's like they're using content that really belongs to lots and lots of originators, right? So I think that with things like that, it's like who owns it? How is it distributed, has been handled for a while. And then how do you really actually classify that data so that you can get the benefit? One of the other trends that I think is really important is like you have more and more data, but you're trying to reduce the latency, not necessarily the delivery of the data, but aggregating that into things that are cohesive basis's almost to look at different, whether it's a vector model or whatever you're trying to figure out like, okay, well how does this all fit together and what can I do with it? And so the amount of compute is making it so that, you're really trying to get to insight faster and faster and faster. If you find something that's really true and the bar is really high the top tier firms out there are very sophisticated, have incredibly intelligent, determined, hardworking people. So it's fun in that in that respect. But Twitter or anything like it becomes one of many data points where it's into a larger model where you're trying to, quickly assess the impact of that.
Jamie: [00:14:22] Jimmy, how about you? How do you deal with sort of the more nontraditional sources of data?
Jimmy: [00:14:26] Yeah, and I would just carry on their exactly what Patrick was highlighting. For the use case, it truly depends on the strategy. So when we look at casting a wide net, you have to take into account the major news agencies that are out there, social media platforms that are there as well, and bringing everything together in a cohesive environment to make it ingestible. How do I consume these streams? So on the market making side, I have to participate. I have to have breaking news from the large providers. And we kind of take that and peel it back and understand what are those providers and how can we overlay additional data on top of it. But back to Twitter specifically, it has been around for years, meaning, firms have been ingesting the ability to structure the incoming and filter the noise was key and critical. Some have found success using it as a new source. Other strategies is more of a defensive measure, what is happening around the world. And again, it's one of many sources to Patrick's point.
Patrick: [00:15:29] The one thing that's grown as an industry is the sort of tools that are available. So when you read about a ChatGPT and you actually see what it is and how it works and there's great explanations. I read one recently from Stephen Wolfram, so you're like, Oh wow, this is how this works. And then there's a lot that becomes a sense of belief that this is possible. And so there's been a huge growth in whatever you want to call it, you know, AI is maybe a little bit broad as a term and sounds a little fancier than it is, but the pattern classification and the methodology is like whether it's image or pattern recognition or text to speech and things like that, either using neural networks and there's a way to input data and train them. And so, for Twitter or other things like that, where there's a stream of semi-structured or unstructured data, there's been big advancements in tagging of the data, right? Where you can say like, is this talking about a particular corporate entity or a person within an entity or their affiliated entities. So the computers are doing a much better job of actually saying, What is this? And it's not at all dissimilar technology when you use your iPhone and it recognises people over time, even though those people are growing up and changing, changing the way they look as they as they grow older. So, I think that that commonality that basically advance in technology doesn't have to be invented by a lot of hedge funds, but their goal is to apply it effectively, right? And that's non-trivial.
Jamie: [00:17:04] Jimmy, you mentioned at Balyasny, you have, is it a thousand investment professionals?
Jimmy: [00:17:08] We do.
Jamie: [00:17:09] So can you explain how it works in terms of leveraging information within the firm as much as you can speak to it? When I was at now Point 72, we very much called it a pod shop. So we worked in our own separate teams. Many of us were we had the same strategy focusing on the same industry. Mine was financials and it was friendly and competitive between the different pods. But also, we wanted to share information that we thought was important. So to the extent that you can explain, how does it work internally at Balyasny in terms of leveraging data?
Jimmy: [00:17:40] Sure. So here at BAM, we have a very collaborative culture where PMs across different strategies are open to sharing ideas and insights. So as a central organization, we've built an internal data catalog which effectively exposes all our available data sets across the firm, as well as any exploratory data sets that are in our pipeline that we've discovered that might look interesting. So, this catalog allows our investment teams to search across a structured taxonomy. It gives them the ability to filter on asset class dataclass, data type, delivery, frequency. We'll give them code snippets, vendor documentation, and then in the end there's a request button. It's interactive. They request access to either a trial or access to use the data in production. So having this centralized catalog within a collaborative environment allows us to freely market various sector specific data sets, industry specific data sets, anything that the firm has internally as a resource. We provide that across all strategies, whether it's fundamental, long, short, macro, systematic and that open collaborative environment freely allows us to disseminate this information and it starts a conversation. And then there's usage on the back end. So yeah, that collaborative environment gives us the ability to freely expose what we have internally.
Jamie: [00:19:02] A question to you both, which is, is the pace at which we are getting data and the quality of data that we're getting now, too good to be able to make money? And what I mean by that is you know, five, ten, twenty years ago we would put a trade on a hedge funds because we had worked something out a day earlier than we thought the market did or even an hour earlier. But now it's like seconds or even milliseconds. And that was our edge. We had basically found something. We had digested some news flow faster than everyone else would, and they were playing catch up. But now is the market essentially is it just is it getting harder to make money in the market short term? And do hedge funds now have to pivot a little bit and just have longer term time horizons because that's the easier place to make money?
Patrick: [00:19:53] I think it's a really great time for many, many hedge funds. There is certainly a macro element of kind of there's a high, an overall elevated volatility, although the VIX is not super high today. So the kind of the volatility of the volatility gives a really good environment for a lot of really active traders. And the more data there is, you know, it's like if you had an infinite amount of data and you somehow had possession of an infinite amount of data, you couldn't overfit, like you could create a basis at a moment in time that reflects like all knowledge that's maybe, maybe a little bit fanciful. But the amount of data gives opportunities in, I don't know if it's a Warren Buffett line, but kind of like in the long term, it's a weighing machine. But in the short term, it's kind of a beauty contest. Well, figuring out how other firms evaluate the data and their perception and weighting of it is actually kind of between those two things. It's like you may very well say, well, this is what it really will do for value, but it's like, how is the market going to react to that? And so, you have seen lots of stylized data driven trends and markets recently, and I think that's very, very beneficial for many hedge funds that are able to internalize that and pick out the most dominant factors.
Jimmy: [00:21:16] Yeah, and I would add to that, from a sourcing perspective, we're always trying to identify a faster delivery. So moving from a weekly cadence to a daily from daily to intraday, in doing so by identifying either different sources that are much faster and more granular in terms of the data they're providing or even leveraging some of the technology that's around us today. So Jamie it’s going back to your days as a portfolio manager reading through these filings today, you have technology that overlays on top of a filing on top of an earnings transcript and might give you highlights and summaries and sentiment where you're not spending your entire day looking at the details, the details are provided to you in a summary fashion. So, our goal is to identify if a vendor gives us a product or a data feed in what we feel is untimely, where we feel we can identify the underlying source and be able to use that directly as opposed to waiting for aggregation or a vendor to disseminate more broadly, we'll do so. So speed is critical, speed is key and there are ways that we try to unearth this information by working with vendors and also working with our internal teams, mainly across technology and our data organisation to be able to better provide for our investment professionals.
Jamie: [00:22:36] Again, a question to you both. Let's turn to the topic of AI. We have talked about it a little bit. We've touched on it a little bit. But I have seen headlines in newspapers saying hedge funds now are turning towards AI in terms of analysis, so to what extent, Jimmy, perhaps we'll start with you. To what extent are PMs using AI and how are they using it? Are you excited about this technology? Is it going to make it more difficult for the industry?
Jimmy: [00:23:01] Yeah, there's no bigger topic these days. So this is an area where BAM has focused and aligned across our key partners internally. So we're working closely with technology, compliance, InfoSec, whether it's ChatGPT, co-pilot, any other relevant LLMs. Discussions are fluid, discussions are daily, there's big buzz around it and we want to be prepared. Our approach here is one of innovation and assuring that we continue to stay in front of this technology wave. So my mandate specifically with data sourcing is bringing more content to the table. So we talked about textual content, we talk about news, we talk about filings, broker research, how can we ingest large amounts of data? Leverage the technology that has evolved over the years and put that in front of the investment team. So absolutely a very big topic for us. It's a collaborative effort internally and there's more to come there. And I think it's certainly here to stay and we're definitely in the forefront and working with all our partners to make sure that we can provide that edge to our teams.
Jamie: [00:24:21] Patrick?
Patrick: [00:24:09] There's a lot of fascinating angles. I do think that the term AI, what large language models do is they're effectively just trying to guess what fits best for any series of texts.
Jamie: [00:24:21] Patrick just to be specific, what do you mean by large language? Because I think you’re right and too many people use the expression AI and it can just encompass so many different things.
Patrick: [00:24:33] So yeah, there's lots of types of so you can say AI and there's things like neural networks and there's all sorts of statistical methods you can use and there's pattern matching and Bayesian methods. If you say AI, it has this kind of fanciful sort of way that it sounds. So a few things that I think are great. One is there's a lot of big tech companies that have been working on different models. So it's called large language model. It's a model of language like the human mind works in a particular way and is associative and highly parallel and has billions of neurons. It's better part of 180 billion parts to ChatGPT, which are the tokens. And so, the way that generative AI tends to work is if you have any series of text, it's trying to guess the probability of the next word, right? And it's generated those probabilities by looking at a large, really large body of text on the Internet. So it has this generally it sounds like the things that have been written on the Internet, but it doesn't have any method for text. Now, there's different ways in which it expands on that and passes through these different layers of the neural network. So, it's done something that actually feels really impressive. It has a long way to go, and the way in which it estimates the probability of next is a model, right? It's different than an actual learned human language, right? So it's not like it's sentient that just says, well, it sounds right. So what this does for firms is in a lot of ways it showed people what is possible, right? It hasn't necessarily given them the use cases. And by showing the world something's possible, it can just like the way that the iPhone opened up these millions of different applications ranging from Uber to food delivery and on and on and on, now you're saying like, hey, this is actually something that we can use. And then people have to figure out how they can use it. So for hedge funds, it's super exciting, because you're getting handed this really big canvas. And you have a lot of the ingredients and now you're figuring out how to apply it. And I think maybe we're already back into a bubble with the valuations of some of these AI companies. And because there's so many of them, only a few of them will work out. But it deserves a lot of real excitement and you've lit the fuse. The world is now in a point where it's like we see that these things are possible and we're going to figure out different use cases for these. So I think it's 2000 as when you look back 20 years, it's like look at the great businesses that are going to be built 20 years from now. People are going to be really interacting with computing in a totally different way.
Jamie: [00:27:16] And how about the cloud? How does this change things?
Patrick: [00:27:19] I have a cloud belief, which is I love the cloud. And at the same time, when firms have a certain amount of scale and their utilization is very high. So if they're using a computer around the clock and they have a certain amount of sophistication that there will still be plenty of on prem infrastructure. So I don't live in a world where everything is going to be in one of the cloud providers. So I see a lot of opportunity for sharing data in the cloud on demand access to data. There are a lot of use cases where and I can think of one bringing on a new portfolio manager that maybe needs access to high volumes of obscure data. It's like cloud is really great for that you're not going to be buying the storage and downloading it. Your cost basis per gigabyte or per terabyte may be higher than if it was fully deployed internally. So cloud is great. It again, was one of these technology changes that showed what was possible. But you will find, I think, that many sophisticated firms have quite a bit of on prem forever.
Jimmy: [00:28:25] Yeah, I would add to that as well. I think we are a cloud first organization. So, when we talk about Snowflake, AWS, Databricks, there are legacy use cases as well. Certain vendors are still pushing data on Sftp. There's certain strategies that require on prem resources for us to have here. So cloud first, certainly working closely with our data providers and leveraging infrastructure and technology. But we are a cloud first firm.
Jamie: [00:28:51] Jimmy, if we stick with you for a sec, let's bring it forward to today. What are your what are kind of conversations are you having with PMs now in terms of new ideas for ways of sourcing data and those kinds of conversations?
Jimmy: [00:29:04] Yeah, and depending on with the systematic business, it's always about throughput and identifying and having a pipeline, right? I need history. I need broad coverage. I want to back test, I want to see if it's additive to my process. Can I add another signal on the discretionary fundamental side? iIt's on a sector specific basis. What KPIs are we trying to solve for? How can we leverage existing data sources or possible exhaust data that might exist that we can provide clean tag and make usable? So depending on the strategy, it's always that drive to source best in class data, differentiated data, leveraging our internal data enrichment capability to assure proper tagging, cleaning of the data, to give the teams the ability to monetize. So our focus is to provide data insights to the PMs and build products for multiple teams. And it's a dynamic environment, right? And we have to adapt. And there was Covid, there’s retail trading phenomena, Ukraine. So we quickly pivot and try to solve for the now and that's the approach that that we've taken and that's the approach that we have in supporting our teams more broadly.
Jamie: [00:30:18] Yeah and Patrick, just finally, what's kind of exciting you right now? What are the kind of aspects of data sorting that you're working on?
Patrick: [00:30:25] Well yeah, we have a lot that's really exciting. So we have, and just to go to that point where Jimmy and I overlap, I think I was saying the areas that are less cloud native tend to be more in the real time, low latency. From a research point of view, we're moving all of our data to the major cloud providers and we have so at LSEG, we've been working to capture all of the data that I'd say by and large, all the data that that LSEG has with the May Street process, delivering it in a more timely way to the cloud so that the data is available not only in raw format, but pre-built index formats like parquet open source formats. And then so customers are able to run almost their own on demand data science stack on top of tens of petabytes of data. So we really thought like where do we fit within the ecosystem? It's collecting this data perfectly, storing the data so customers don't have to pay for storing this data redundantly and then organizing the data so that it's accessible, searching it and indexing it. And so they can you think about market participants, It's like, what's the purpose of the data and analytics business? And in my thinking it's like, well, how do we help deliver data that helps inform the investment process at the time they need it and the way that it needs to be consumed by computers and computers like you have other data vendors who have great delivery of information to humans, things that you would look at. And we think about what is the future of delivering data to computers look like. So what am I excited about? It's like, well, how do I collect and organize all this data so that things like generative AI can actually go in and grab the part that they like. What is the continuation of this? And that takes the shape of and this is an aspirational thing, but it's like, how can we help people figure out how best to construct portfolios and hedge and manage risk and select assets and the quantities to buy or sell. And those are super fascinating problems where data organisation and data access and the right examples can really help our clients.
Jamie: [00:32:29] And just finally, Jimmy, I've always wanted to ask, is there friendly competition between all the hedge funds? Do you look at the performance of the Millenium’s and Point 72’s and Citadel’s and try and see who’s doing better?
Jimmy: [00:32:39] Friendly? I think certainly, everything's relative to the competition. But look, it's a small industry and think for us just having the relationships with my peers at the other firms, it's again, it's refreshing. But yeah, there's always friendly competition when it comes to performance.
Jamie: [00:32:59] Yeah, that's what drives us to do better every time. Patrick, Jimmy, I want to say thanks to you both. This has been a really fascinating conversation. I know people can go online and look you guys up if they want to find out more about you. But if you want to make any final comments, I just want to say thanks for me. But if you would like to make any final comments, go for it.
Jimmy: [00:33:18] Thanks for having us, and certainly this is a small industry. I've worked with Patrick. Patrick's been very helpful. What he's providing, what he's building at LSEG, very excited for what he's done, and we're certainly connected and small industry and like to say thank you, Jamie, for the opportunity to chat.
Patrick: [00:33:34] Yeah. Thanks for taking the time. And anyone who would like to talk more, feel free to reach out and happy to keep going.
Jamie: [00:33:42] Patrick Jimmy, thank you so much. That's another episode of Hhedge Ffund Huddle. And of course, you can find us online and listen to the recordings there or wherever you get your podcasts. Jimmy, Patrick, thank you so much and see everyone next time.
Jimmy: [00:33:55] Thanks for having us.
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