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time – data sets and to make predictions about where a bond is likely to trade and how much liquidity there is likely to be in the marketplace . AI is able to produce signals which can help inform decision points for traders , with data essentially being a vital part of the decision-making process in the execution workflow .
“ What we ' re able to do with AI is feed into the model a very , very broad set of data points - including data points that may not be about the specific bond that we ' re trying to solve for ,” says Gareth Coltman , global head of trading automation at MarketAxess . “ The machine learning model can then iterate and select which of those data features are the most reliable signals of liquidity when they ' re tested against the market . What we ' re seeing today is very broad adoption of these machine learning tools , and that ' s because their ability to predict reliably and consistently is really benefiting the traders that are using them .”
The role of passive inflows and ETFs The evolution of the fixed income ETF space has brought more automated market makers into the space , due to the ETF market based around equity market structure . This has seen fixed income markets translate more of their activity in the ETF market into the single lane market , allowing the ability to stream firm pricing on single names and respond to RFQs in a more automated fashion .
“ It ' s really raising the bar for all of the dealers out there to say look , if you ' re not able to algorithmically price a certain part of the credit market then you ' re not going to be fast enough to be able to respond to RFQ inquiries ,” adds Murphy . “ Someone else is going to respond quicker , with a more accurate price and your market share is going to be eroded . Via an indirect mechanism , the growth in the ETF market and growth in passive is actually changing the rules of the game and raising the table stakes for you to be in the flow credit space .”
Managing high levels of flow In terms of helping larger firms manage flow , AI can be extremely useful in detecting outliers by monitoring previous history of how traders approach their accounts including when certain inflows come in and how much is
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