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
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