Arnott, Harvey: machine learning dangerous when data thin

Experts warn ML should be used “for its correct purpose”

Wrong tool for the job
Machine learning as a tool must be used appropriately, say two experts

Rob Arnott and Campbell Harvey – two of the best known experts in quant investing – have warned investors against using machine learning to derive investment strategies from too-thin data.

According to Arnott, using sparse data to train “powerful” machine learning algorithms is akin to driving a Ferrari on an off-road dirt track.

“If you visit the data often enough and in enough depth [using machine learning], you’ll find all sorts of things that look marvellous,” Arnott says. “It doesn’t mean

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 [email protected] or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact [email protected] to find out more.

To continue reading...

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: