SF quant firm uses 'nearest neighbor' machine learning for equities predictions
Creighton AI is using a regression-based approach to machine learning to help make predictions about the excess return of a stock relative to the market.
One of the bugbears for quantitative analysts and data scientists working in financial firms is the low signal-to-noise ratio in raw financial data. A lot of the work in this field is the unglamorous task of cleaning data and adjusting standard algorithms to fit financial use cases.
Developing models that can work the randomness and uncertainty associated with financial data has been core to the career of Jim Creighton, founder and chief investment officer at San Francisco-based Creighton AI
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: http://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
Banks urged to track vendor AI use, before it’s too late
Veteran third-party risk manager says contract terms and exit plans are crucial safeguards.
Market data woes, new and improved partnerships, acquisitions, and more
The Waters Cooler: BNY and OpenAI hold hands, FactSet partners with Interop.io, and trading technology gets more complicated in this week’s news round-up.
Waters Wavelength Ep. 306: Reykjavik and market data
Reb is back on the podcast to talk about her trip to Reykjavik, as well as two market data reports released this month.
BlackRock tests ‘quantum cognition’ AI for high-yield bond picks
The proof of concept uses the Qognitive machine learning model to find liquid substitutes for hard-to-trade securities.
JP Morgan, Eurex push for DLT-driven collateral management
The high-stakes project could be a litmus test for the use of blockchain technology in the capital markets.
For AI’s magic hammer, every problem becomes a nail
A survey by Risk.net finds that banks are embracing a twin-track approach to AI in the front office: productivity tools today; transformation tomorrow.
On GenAI, Citi moves from firm-wide ban to internal roll-out
The bank adopted three specific inward-facing use cases with a unified framework behind them.
How a Chinese AI firm shook the tech world
DeepSeek’s AI model is the very ethos of doing what you can with what you have.
