New breed of NLP model learns finance better, study finds

Models trained by looking at sentences beat conventional approaches that contextualize words.


A new class of natural language processing (NLP) models trained to catch the drift of sentences, rather than the meaning of single words, may beat the models many investors are currently using to analyze text data.

Academics found in a recent study that a sentence-based NLP model outstripped standard models, including those trained specifically to understand financial terms.

The exercise is a first test, the researchers say, with more work to be done. But the findings suggest quants might wish

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 or view our subscription options here:

You are currently unable to copy this content. Please contact 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

Systematic tools gain favor in fixed income

Automation is enabling systematic strategies in fixed income that were previously reserved for equities trading. The tech gap between the two may be closing, but differences remain.

Why recent failures are a catalyst for DLT’s success

Deutsche Bank’s Mathew Kathayanat and Jie Yi Lee argue that DLT's high-profile failures don't mean the technology is dead. Now that the hype has died down, the path is cleared for more measured decisions about DLT’s applications.

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