メインコンテンツに移動

How Bloomberg got liquidity seekers to trust its machine learning models

Recent liquidity squeezes have proved the worth of advanced models, argues the tech giant. Now the task is to explain their inner workings to machine learning sceptics

machine learning

Being able to explain how machine learning models work has been a point of contention since the technology’s inception. Bloomberg is set to release further empirical metrics, at the end of this year, to bolster its liquidity models’ explainability. The metrics will initially be available via Bloomberg data feeds and featured more prominently on the Bloomberg Terminal liquidity screens later in

コンテンツを印刷またはコピーできるのは、有料の購読契約を結んでいるユーザー、または法人購読契約の一員であるユーザーのみです。

これらのオプションやその他の購読特典を利用するには、info@risk.net にお問い合わせいただくか、こちらの購読オプションをご覧ください: http://subscriptions.risk.net/subscribe

現在、このコンテンツをコピーすることはできません。詳しくはinfo@risk.netまでお問い合わせください。

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

無料メンバーシップの内容をお知りになりたいですか?ここをクリック

パスワードを表示
パスワードを非表示にする

Most read articles loading...

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

ログイン
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