Model Misfires Raise Questions Over Training Data

Quants wrestle with how far into the past their machine learning models should peer.

Training-machine-learning-bots-with-data montage

Wisdom accumulated over many decades is highly prized in most cultures. Less so for machine learning in investing, it would seem.

Algorithms that use long histories of data to build their understanding of markets flopped during the Covid-19 pandemic. Such models failed “pretty spectacularly” in the extreme events of 2020, says Michael Heldmann, head of multi-factor equity investing for North America at Allianz Global Investors, citing research conducted by the firm.

“They have been hammered

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:

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

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Waterstechnology? View our subscription options

If you already have an account, please sign in here.

You need to sign in to use this feature. If you don’t have a WatersTechnology 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: