Trading Tomorrow - Navigating Trends in Capital Markets
Welcome to the fascinating world of 'Trading Tomorrow - Navigating Trends in Capital Markets,' where finance, cutting-edge technology, and foresight intersect. In each episode, we embark on a journey to unravel the latest trends propelling the finance industry into the future. Join us as we dissect how technological advancements and market trends unite, shaping the strategies that businesses, investors, and financial experts rely on.
From the inner workings of AI and ML to the transformative power of blockchain technology, our host, James Jockle of Numerix, will guide you through captivating conversations with visionaries who are not only observing the future but actively shaping it.
Trading Tomorrow - Navigating Trends in Capital Markets
Unraveling the Intricacies of Data and Capital Markets with Scott Fitzpatrick
Dive into the intricate world of capital markets data in this episode. Host Jim Jockle of Numerix explores current and future data trends with Scott Fitzpatrick, CEO Tradition SEF & Global Head TraditionDATA. Discover why Tradition's data is in high demand, its extensive global reach, and how it remains impartial in the markets. During this episode, Scott delves into data capture complexities, from voice-to-text conversion to data watermarking. Don't miss this insightful conversation on challenges and advancements in the data industry.
Welcome to Trading Tomorrow, navigating trends in capital markets. I'm your host, jim Joggle. In my decade plus of working with Numeric's Global Leader in Capital Market's Risk Management Technology, I have launched our Thought Leadership Division, a place where insights, innovation and expertise converge, just like this podcast. Through my journey in the financial realm, I've had the privilege of witnessing firsthand how the capital market's landscape has transformed. The complex dance of market trends in innovative technology has redefined how the finance industry operates, with game-changing innovations just around the corner. We now stand at acrossroads, one where it is more crucial than ever to understand the interplay between these realms. That's what we do here. We talk about current and future processes and technologies you need to be aware of moving forward.
Speaker 1:Data is often referred to as the backbone of business. It teaches us things we could never hope to know on our own. In an era where every financial move is quantified, data-driven insights have become essential for strategic investing, from algorithmic trading to predictive analytics. Data is revolutionizing investing, and it is much needed. Recently, there's been even broader adoption of algorithms in investing. In the spot FX market alone, algorithmic trading is now almost 75% among financial customers. Today, we'll be discussing data, technologies and processes that will continue to help this space grow and thrive. Joining us today is Scott Fitzpatrick, ceo of TraditionSEF, a swap execution facility. Scott is also responsible for Tradition's global data business making traditions highly sought after multi-acid OTC data available to a worldwide consumer base. Before joining Tradition in 2012, scott spent 10 years as an inter-dealer broker at GFI Group. He holds a business and computer studies degree from Napier University in Edinburgh, scotland. Hi, scott, thank you so much for joining us today.
Speaker 2:Jim, thanks very much for coming along. It's been a while since we sat together.
Speaker 1:Well, it's definitely since before COVID.
Speaker 2:That for sure. It's amazing how time is flying.
Speaker 1:As I mentioned, it's briefly in your introduction, but perhaps you can explain to our listeners a bit further about how and why Tradition's data is so highly sought after.
Speaker 2:I think in essence because when you look at the scale and scope of our business 43 offices worldwide, 30 countries, 220 plus products across hundreds of currency pairs in all of the five major asset classes of listed OTC derivatives we're one of only three companies essentially that do what we do at the scale that we do it. We have no access on our pricing because we don't trade for our own book and purely as an intermediary in these markets. As a result, I would say that our data is extensive. It's coming from an aggregation of multiple sources. Most importantly, I think, and where the real value comes in, is that we are ignored, sticking impartial in those markets because we don't trade our own positions?
Speaker 1:Specifically, how do you make it available globally?
Speaker 2:Great question. We start internally. We have an internal aggregator because we are aggregating data from many, many places around the world. As I've said, we have an internal system that we call Integrated Term that we can use not only for the consolidation of all of that data, but also as a direct distribution channel in various customer-friendly formats like FIX, as an example, or JSON, but for the most part we distribute through the global vendor community. That's a source of data that still seems to be the primary entry point for people looking for data in financial markets.
Speaker 1:Now, data has been obviously under the radar for many, many years and data capture and distribution it's intricate and it's a delicate web. So you know, as technologies have progressing, what technologies are you looking at into to give your clients the best experience?
Speaker 2:possible. So let's separate church and state in that question. Let's talk about capture and then we can talk a little bit about distribution. To say that the capture and the collection of data in the financial markets is complex is probably an understatement. When you look across the asset classes that we mentioned earlier, whether you're looking at FX even within FX, you've got everything from spot FX through the NDF range of currencies and then into FX options and complex derivatives, which I know your firm at Numerics is extremely at O'Faith Just across the FX landscape you have multiple different kinds of systems that are required to capture multiple different types of data points.
Speaker 2:If you then extrapolate that out into interest rate markets, which are equally, if not more, complex in some places, then into fixed income markets, then into energy and commodity markets, which are again completely different from some of the financial markets, and then again into the equity markets, we have many different platforms globally that are designed very, very specifically for those markets, and then the capturing of that data is very different across those markets.
Speaker 2:We have typically in-house, proprietary, built platforms that we roll out across our global-broken business that are used to capture data. As I mentioned earlier, we then consume that internally. We take it into data warehouses. We separate stuff out that we are not going to distribute as part of data products, which customer entity names and stuff like that. We whittle it down to the pricing information that's required by the consumer base. We take it into our internal integrate platform and then we choose and we make that data available for distribution across multiple platforms, whether that be a Refinitiv or a Bloomberg, through firms like your own, as in data distribution partnerships, or indeed direct from API connectivity into some of the trading platforms, and or through our integrate technology and environments like FIX.
Speaker 1:So we covered the church. And now, in terms of distribution in technologies, are you seeing more cloud coming into place? Data lakes were all the rage, I want to say, five years ago. What kind of developments are you seeing there in the state side of business?
Speaker 2:So I think for us, and you're right, data lakes were a cool thing a few years ago and that's not to say that they're not cool anymore.
Speaker 2:But I think we see data lakes more at the moment as an internal architecture for data science projects and stuff that we run internally.
Speaker 2:Part of the problem is not really a roadblock per se, but if you look at the consumption of data and where the cloud has come into that and I speak solely from our business's perspective here, and I know it may be different across other industries there's an incumbency element to data distribution for us, which is the consumption element. On the other side, and to date still, the preferred route of data consumption tends to be the big global vendors. We do see and we have instances where we do cloud to cloud connectivity, so we have seen cloud come into it. But some of the issues you find with cloud, especially in fast markets where there's a latency issue in terms of data consumption you don't have the same level. I mean when you talk about the quality of data. You don't have the same quality there when you're maybe high frequency markets going over a cloud infrastructure versus a direct connect through a fixed API, for example. So cloud doesn't always lend itself to data consumption, but not yet anyway.
Speaker 1:So is everybody still call out in Jersey?
Speaker 2:Yeah, so that's a great question and it's part of that distribution. We're present in all of the data centers around the world, taking New York and London, for example. So we're present in the equinex data centers there and if people want to co-locate and do direct connects in the data centers, that's certainly something that we get involved in.
Speaker 1:Would you say the latency is the biggest barrier to cloud adoption, or is there something else going on?
Speaker 2:I'd say that side of the market. Certainly cloud has an issue there. But I think and again speaking, you know, selfishly, from a sort of an IDB or tradition data distribution into the financial marketplace that is looking to consume data like ours, I'd say probably the bigger issue that the cloud providers have come up against is just the incumbency of the big vendors.
Speaker 1:So, before I move on too far down the technology road, I am curious, specific as it relates to the data capture how people intense is the business?
Speaker 2:Wow, that is a good question.
Speaker 2:Well, the people intensity comes from certainly from the development of the platforms, testing the platforms, deploying the platforms and then the IT management around making sure that those platforms are up when and if necessary 24 by six and a half.
Speaker 2:So there's certainly a people intensity in terms of maintaining and keeping platforms like that up in global markets around the world all day, every day. When it comes to the individual markets, that's obviously a very different question because you get markets that are highly electronic and a lot of the contribution of prices into those markets is coming through APIs. So you don't have the same level of people intensity in terms of the, if you like, the incoming data, the management of that data, the collection, capturing and then the distribution of that data. But when you get outside of those highly liquid markets and you get into more, what we talk about is hybrid markets where you've got a combination of a voice broken environment operating in tandem or symbiotically with a technology platform or a trading platform for price distribution and execution, then obviously you know it's a well known fact that you know businesses like tradition are in those markets and the markets in general are heavily dependent on human beings and the form of brokers to facilitate and aggregate and manage all of that liquidity in the form of prices.
Speaker 1:So I do want to ask you a very tough question. You said 24 by six and a half. Why are you slacking off for half a?
Speaker 2:day, that's just roll over. No one gets a day off.
Speaker 1:Okay, I was concerned. Well, I don't. You're getting soft on me as we get older here. So you know when I think about technologies, you know, and just even in our corner of the world, you know it's everything is just changing so quickly. Is there anything that you're currently tracking that you know? Maybe it's interesting, maybe it's on top of the hype cycle, but it might not be ready for to be deployed or it's not ready for prime time.
Speaker 2:Yes, I'll take you. I'll take you in a couple of directions there, one of them being around the capture of data, and then also we'll talk about the distribution of data and, probably more relevantly, the management of data use. So on the first one, voice to text conversion, and there are a few firms out there that you know and, interestingly, in the south side of the business, part of the CFTC regulation for CEPs is that we actively monitor capability around the translation of voice to text so that you can create a sort of seamless chronological flow of price formation and creation which ultimately leads to data and more data going into the system. So that voice to text technology is something that we follow, have deployed in some places, but it's not widely used. And there's things that drive that market. Speak nuances on any given nest. You know, have multiple languages in play at the same time. Some of the traders are French in a market that's a Latin American market, for example, so you've got French, spanish, english all going on at the same time, and then you've got all the sort of nuances around terminology that's used within markets. So it's a very complex thing to do, but we do monitor that technology because we're almost obligated to do so, but it also would yield a lot of interest in data as well for the consumers.
Speaker 2:And then the other technology that we kind of look at and keeping an eye on is data watermarking and being able to track data through an organization on both sides of the fence. We are a huge data consumer. In the businesses that we do, we consume data from many places for analytics purposes, etc. So we're constantly under scrutiny as well by vendors of data for where we're using the data, how we're using the data under the license agreements that we have. And I know this is something that a lot of the big major institutions are focused on too, because when data enters an organization, as the contributor of that data, you want to know where that data is going and how it's being used. But also as a consumer of that data, you want to make sure that you can track that data through your infrastructure. So that's another technology, or set of technologies, if you like, that we're kind of monitoring in the background as they start to develop.
Speaker 1:What are three things that the audience needs to be tracking when it comes to data in capital markets?
Speaker 2:I think for me, consistency, quality and relevancy are key elements of any data service that anyone would choose to consume. Consistency I say that because typically, clients are not signing up for a one-day service. They're signing up to long-term contracts, to three years plus, so you want to be sure that that service and the provision is going to be there long-term, which speaks to the institution that you're consuming that data or data service from. Quality, again, you want to be sure that the organisation that you're signing up to receive this data from has got all of the technologies in place that assure a quality product is delivered to you. And then, finally, relevancy you want to be sure that where you're receiving data from in any particular market whether it be FX or rates or energy and commodities or fixed income or any other markets and equities that the firm or the business that you're consuming that data from understands those markets, is a part of those markets and therefore the data that you're getting is relevant and the people providing that are relevant to the subject matter.
Speaker 1:A lot of people say this, but I'm not going to throw this term around loosely. Tradition is considered a leader in the market. What are you doing to contain your edge going forward?
Speaker 2:I think it's a bit of all of the above. It's constantly enhancing our systems internally for to improve the amount of data that we're capturing, the quality of the data that we're capturing and the scope of data. We have a hybridisation project within the group which is to put as many systems on as many touch points to the markets that we can, whether that be analytics platforms or whether that be price distribution platforms or trading systems. Data quality managing the quality of data at source, meaning at the point of capture. The most obvious example of that would be fat fingers Someone hits 20 instead of 2.
Speaker 2:Data quality technologies, so that we check it at source and then all the way through our infrastructure and ultimately, just before it exits our infrastructure and goes into client infrastructure, those technologies and then ultimately, just business process and business efficiency, because, at the end of the day, the customer experience is what really matters. The quality of product is key in core to any company's business, but customer satisfaction is where it really the rubber hits the road, so to speak. I think, ultimately, making sure our customers are happy with what we're delivering is what will keep us in that leadership position.
Speaker 1:I think that's a refreshing perspective, especially you think of the fuel of the markets, if you will. So, scott, we've made it to our final question of the podcast and we call it our trend drop. It's like a Desert Island question. If you could only track one upcoming technology or process within capital markets data, what would it be, and why?
Speaker 2:I think I would go back to my comments on the data watermarking stuff. I think that's one of the biggest challenges of the industry. I think it touches every aspect of what our customers are trying to achieve, and that is the efficient use of data within their organization. I don't think it's wrong to say that probably most big consumers of data out there continue to pay license fees for data that they're not sure whether they really need it or not. I think one of the biggest challenges out there is everyone wants to spend money in the most efficient and constructive way for their business. I think one of the biggest challenges facing the data industry from a consumer and producer or provider is really knowing where data is going, how that data is being used and, ultimately, is the value there for the dollar that you're spending. I think for me, that's something that I think is probably the next big move in the data industry is improvements in that area.
Speaker 1:Scott, thank you so much for your time today and your insights. We really appreciate your joining us.
Speaker 2:Thank you very much. It's been great being here.
Speaker 1:Coming up. Next week, we're joined by AWS to discuss the cloud, how it has transformed scalability, cost efficiency, flexibility and collaboration across industries far and wide. But why has it been met with some hesitance in the financial sector? You don't want to miss this episode, but first, if you enjoyed the podcast, make sure you hit the subscribe button, leave a comment, a like and check out our other episodes. Thanks for joining.