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In October, we covered CBOE Europe and Aquis Exchange’s bid for the European equities tape. We also reported on Bloomberg’s increasing focus on the quant community and how it is hiring traders and analysts to help it build products for that segment. Let’s get to it.
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WatersTechnology broke the news in early October that CBOE Europe and Aquis Exchange are teaming up in a joint bid to become provider of the European equities consolidated tape. Both firms publicly confirmed on their respective websites at the end of the month. If you’re a regular reader of our site, you saw it here first.
The European Securities and Markets Authority plans to open a tender process to choose the consolidated tape provider (CTP) for equities in June 2025. The joint venture between the two exchanges, named SimpliCT, is the second public contender for CTP of the equities tape after EuroCTP, a company set up by 14 European exchange groups.
Aquis Exchange and CBOE Europe declined to comment for this article.
“Eventually, it always ends up in a commercial debate, with companies on different sides of the aisle arguing for lower data prices or higher data prices.” – Graham Dick, Aquis Exchange, in a previous article.
The data giant is stepping up its focus on the quant community and is hiring practitioners to help build tailor-made products specifically targeted at quants.
According to its recruitment website when this article was published, there were 42 open positions for quants specifically—with some roles paying up to almost $300,000 per year—or for individuals to work on products targeted at quants.
Algorithmic trading’s popularity is fueling a desire among European dealing desks to use the strategy to execute trades of exchange-traded funds (ETFs).
ETF trades in Europe are mainly executed through the request-for-quote protocol, but in the interest of best execution, some firms are seeking out alternatives to that process and mirror the use of algorithms in other asset classes. But there are historical reasons why that could be more complex to do.
The Frankfurt-headquartered bank has been working on an experimental project called Project Aggie. Aggie is a generative AI text-to-text tool trained as a multilingual regulatory domain expert assistant.
Aggie’s purpose is to analyze, summarize, compare regulatory text, and then infer sentiment. From there, Aggie generates a document that distills the information in regulatory publications.
The principal benefit of Aggie is productivity gain. The experiment aims to see what Deutsche Bank can do with?GenAI, and learn from it, with the view of potential production.
“Large language models are good at natural language processing,?but?they are not good at statistical classification, regression analysis, or predictions. So the problem statement I want to solve, the ‘I do not know what I do not know, please inform?me’?is what?it’s good at.” – Boon-Hiong?Chan, Deutsche Bank
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