THETRADETECH DAILY
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What technologies are you most interested in? Broadly, everyone ' s talking about AI. Specifically, large language models. You have a whole variety of these hyperscale technology companies that are producing new models and evolving them and fitting them, but the key question remains: how do you combine data assets from companies like LSEG with those models? How do you make this work for many of these large financial services firms?
What are some of the key use cases for AI in capital markets? Portfolio management is a good example where there ' s a lot of formulaic work that ' s ripe for automation. Being able to combine data across pricing, valuation, risk, more complex concepts like correlation and the concepts that are used in building portfolios is really useful. AI is fascinating in that it can help construct portfolios across strategies more effectively.
There ' s a stream of information that ' s happening in the world including news and prices and all these things will influence what ' s in a portfolio. It’ s the input data that drives these models and the best, highest-quality data makes for the best quality output. Portfolio construction and portfolio evolution are incredible use cases. AI has the ability like Netflix to say,‘ you may like this movie’. The modern version of this is you may like this bond or this stock, and here ' s how much of it you should buy.
The question for a lot of firms is how much work are they doing to create their own models and then how are they integrating them so that they ' re getting better answers faster with fewer resources?
Writing code is another phenomenal use case. If you had to take a research paper and map it to a piece of code, there are concepts that can be embedded in those large language models which help people quickly do so. This could aid a researcher with a lot of experience to be able to map those concepts.
Building the case for AI in capital markets
As new artificial intelligence and automation solutions continue to come to market allowing firms to be better trained, more flexible, and more efficient, Group head of low latency at the London Stock Exchange Group( LSEG), Patrick Flannery, unpacks the key use cases for these technologies and how firms can best leverage them to improve their workflows.
What are the key benefits of utilising AI? When you think about index funds, you can pay a small amount of money and receive market returns. The idea that you could have ever so slightly better returns over a long period of time is incredibly powerful. In capital markets, the idea that a firm could be a little bit more efficient compounds over time. It ' s more money to invest, it ' s more money for talent. The benefits of AI and automation are holistic. It ' s not one thing.
Some firms have become extremely successful and their revenue per employee is dramatically larger than the previous generation of banks and asset managers. Think of the large electronic liquidity providers. They produce half as much revenue as some of the banks with one tenth the number of employees. That ' s a direct effect of automation and efficiency gains. That same thing is going to happen in asset management.
What are some of the challenges to adopting these technologies? The hardest part is that a lot of the data is disparate and lives in different databases and formats. The key inputs to these models are data – AI needs high-quality, trusted data with significant coverage. Our PCAP coverage, for example, now has over 400 feeds covering multiple asset classes and geographies. Storage and data management is another issue which can become costly and cumbersome; being able to access all of our data in the cloud eliminates the need to store petabytes of data on site and allows capital market firms to focus on where they can add the most value – the model.
Not everyone will be a winner in AI adoption, but it will diffuse out into the ecosystem. A lot of large asset managers have significant financial resources for their own internal development. However, some will have technical challenges and they don ' t necessarily have the right technology management to feel confident in building these sorts of platforms. In certain areas of finance, you ' re going to see firms that are well positioned to utilise these technologies but there ' s probably not very many firms that are well positioned to build
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