Zoi Fletcher: Welcome to this podcast series titled Yield Curve Chronicles: mastering fixed income in volatile markets. These four short podcast episodes examine fixed income investment strategies against a backdrop of economic uncertainty, potential rate cuts and market volatility. My name is Zoi Fletcher, and I'm joined today by Unmesh Bhide and Andres Gomez of LSEG for our fourth and final episode, AI Edge: revolutionizing fixed income trading. Andres, I'll start with you today. How can artificial intelligence and advanced data analytics enhance decision making processes and risk management in fixed income trading, especially in a complex, rapidly changing market environment?
Andres Gomez: Thank you. Zoi. AI is the new frontier for all of business and finance. In my opinion, we're in the early innings of this game, probably still in a first inning. Everyone is exploring on how they can leverage AI to make their business more productive. Some of the projects that we're working on is leveraging AI to make our analytical and pricing content more powerful to our clients, where we're providing more value. And I have a couple of examples to try to cite this. So, we have Lipper insights, for example, where we're working on a chatbot like capability based on an API which will enable clients the ability to interact and make requests on data or analytics content. If we look at the current journey, or process a client takes today, first, they need to access a GUI or an interface, they need to learn how to navigate the desktop, make the request, and then, based on the response they receive, the client may receive and may need to make several iterations of that request to get the required content needed to progress with their work. Also, our clients may not be fully aware of the full breadth of content that is available. They may only access a small subset of this content for them to continue with their workflow activity. So, with what I just outlined, AI and Lipper insights will help address several issues that I just highlighted. First, it can make the client more productive, where they'll extract the answer and the content needed for their workflow the first time, they'll be more productive where there's no learning curve, where they don't have to learn and take the time and effort to learn how to use the interface. And then third, more effectively, deliberate insights with a chatbot capability will provide you with the full breadth of content to address your initial requests, so you can easily see where AI can make our clients more productive, better informed, and LSEG provides an increased value on our content to our clients. So, we're working on several fronts across our platform for leverage AI to make it more productive and for our clients to utilize our analytics and pricing more effectively. So, we're collaborating between LSEG analytics and our pricing partners, PRS, on a historical analytical and pricing service, where we have systematically been working to backfill prices and analytics across a wide range of fixed income assets from the present back to 2002. Currently, this comprises of corporates, governments and taxable munies. We got about a total of 3.8 million securities calculated. And we're also developing our ability to calculate US securitization, such as mortgage pools, agency CMOs, production-year generics, TBAs and cohort spec stories, and we should have this ability in the fourth quarter to go back, initially five years as a phase one deliverable. But how does this help with AI? We believe that producing these valuable analytical and pricing content sets will be used in multiple ways, in AI development and machine learning and other model purposes and applications. The content alone is extremely valuable, but with the possibility of marrying this content with other data sets like economic data, financial news and trading flows and volume data, there will be critical data sets which clients will need for multiple use cases, for risk models, trading algorithms, portfolio and index replication, validating research and trading strategies, and for other research purposes as well. So we want to participate in that full AI cycle where we can produce valuable content for AI and machine learning purposes, as well as develop AI capabilities to empower clients to use our platform for risk management and investment decisions. So Umesh, I'll turn it over to you for your opinion here on this topic.
Unmesh Bhide: Yeah, I would agree on how AI is going to change from a valuation perspective, I would say it's a very big thing. From a valuation side We are looking at so much data, and how do we filter out the noise and focus on a signal from the data, right? I think that is the important part. That's how I see at a very high level. And now, for example, you are getting so much information, and when there is so much information, we can use machine learning specifically to kind of use that data specifically to price information, so that's, I think, just one example. The other part, I would say, is, if you think about it, some of the valuation techniques that now AI can provide is very important in terms of, how do we react to the dynamics of the market, or how are we looking at certain uncertainty, and how do you factor in different probabilities in terms of valuation prospect. That was much more challenging if you want to compare to the past, they're in the scope now. So I think that's the second part of it. And the last part, I would say, is LLMs can also help in terms of looking at some of the prospectus information in some of the things that you want to very quickly look at and react to. And that, I would say, is a very important aspect, because some of the securitized products, there are the minute details that you need to kind of focus on, and the amount of time in the past versus now would be much less now. And that, I would say, is a pretty big gain in my mind.
Zoi Fletcher: Fantastic, that's a great point on which to wrap up this series. So, thank you Unmesh, and thank you Andres for your participation, and also to LSEG for sponsoring this podcast. Thanks for tuning in. Bye for now.