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: 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
EU AI Act leaves agents in regulatory limbo
A new paper published by AI ethicists draws attention to a hole in the EU AI Act surrounding high-risk agentic systems.
CME to launch compute futures, agentic AI for capital calls, and more
The Waters Cooler: A recap of the major tech and data news from the past week in the capital markets.
APAC’s hidden opportunity is in the hands of wealth managers
Asia-Pacific’s financial firms have lofty growth ambitions that will come with high cost and complexity. To succeed, they’ll need a quality portfolio toolkit and a connected technology architecture, writes BlackRock’s James Verner.
FactSet’s vectorization service aims to improve agent accuracy
FactSet chief AI officer Kate Stepp discusses the importance of having AI-ready data in the agentic era.
DeFi and TradFi firms are borrowing each other’s benefits
The Waters Wrap: As blockchain tech gains a small foothold in market data, Nyela says the thing separating blockchain’s previous craze and its second wind is choice.
Hitting the Great Wall: Details scarce on China’s Xinchuang initiative
In a quest to learn more about China’s Xinchuang initiative, Wei-Shen finds trying to get information feels like running into a wall over and over again.
Anthropic builds finance agents, Osttra buys HUB, TMX mulls extended trading, and more
A recap of the major tech and data news from the past week in the capital markets.
Bootcamps and peer pressure: Goldman preps staff for AI future
Isda AGM: Tone from the top is not enough, says chief information officer Marco Argenti.
