BlackRock to use machine learning to gauge liquidity risk

Firm close to rolling out new models for redemption risk and market liquidity

Ripples in water

BlackRock is turning to machine learning to better understand liquidity risk.

Over the next two months, the asset manager will incorporate internal trade data into its existing market liquidity model, and apply machine-learning techniques to more accurately calculate the cost of liquidating fund positions in the case of redemptions.

“Liquidity is multi-dimensional and impacted by so many features. It is highly non-linear. So this is a typical [use case] for neural networks,” said Stefano

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@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact info@risk.net 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 Risk.net? View our subscription options

Register

Want to know what’s included in our free membership? Click here

This address will be used to create your account

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