Quants use AI to shush noisy order-book data
Signals from clusters of seemingly informed trading perform better, researchers say.
A team of academics has used a simple machine learning algorithm to filter out so-called noisy trading in stock exchange data and say they have generated more-powerful versions of signals already popular with quants.
The researchers—from the University of Oxford, University of California Los Angeles, Queen Mary University of London and Memorial University of Newfoundland—used an unsupervised machine learning algorithm to group trades into clusters based on information only from message-by-order
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@waterstechnology.com or view our subscription options here: https://subscriptions.waterstechnology.com/subscribe
You are currently unable to print this content. Please contact info@waterstechnology.com to find out more.
You are currently unable to copy this content. Please contact info@waterstechnology.com to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@waterstechnology.com
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@waterstechnology.com
More on Emerging Technologies
Standard Chartered’s AI Factory applies the ‘repeatability’ principle
The AI Factory will be a library of transversal services of AI capabilities for the bank to use, like a tree that continually bears fruit.
Cboe to exit Japan equities, S&P/Barclays partner, LSEG mulls 24-hour trading, and more
The Waters Cooler: Regnology acquires Wolters Kluwer’s FRR unit, and storms ahead for the $500 billion Stargate project.
SS&C sets early roadmap after Calastone buy
CEO Bill Stone also said on SS&C’s earnings call that Blue Prism, AI are helping to push growth.
ViaNexus aims to bring data entitlement control to MCP, agentic AI
The startup believes that Anthropic’s Model Context Protocol marks a major step forward for agentic AI, but the market data industry has its own complexities that haven’t been addressed—yet.
Academic warns of systemic risk from AI-powered trading
Strategies generated by LLMs exhibit “very strange, correlated trading behavior”, says Lopez Lira.
Anthropic partners with S&P, TT to build AI hub, Talos acquires Coin Metrics, and more
The Waters Cooler: Bloomberg adopts agentic AI, DSB report homes in on fairer data costs, and asset managers are coming for your utilities in this week’s news roundup.
The Model Context Protocol brings agents to life—along with risk
Waters Wrap: From chat to infrastructure modernization, Anthropic’s MCP offers a ‘bridge’ to agentic AI, but its early days may prove disillusioning.
BofA ramps up AI deployment, patents
The bank has 1,400 patents in AI and machine learning, either granted or pending, alongside a growing portfolio of 250 models.
