StarZero All shows Try the podcast clipper →

Amanda Stent

Researcher, Bloomberg · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

Amanda Stent is a natural language processing researcher at Bloomberg. She specializes in text analytics and language applications designed for the finance sector.

14statements → 6claims → 5claims resolved → 60%fully supported → 4.07/5average certainty → 1.57/5average debate potential →

3 supported 1 partly supported 1 contradicted 1 not checkable as stated how the 6 claims stand · each chip opens the sources

6 assertions · 1 opinion · 2 insights · 5 disclosures · every statement was checked. The predictions and assertions are the 6 claims: statements the public record can support or contradict. 5 are resolved, and 1 names no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Amanda argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Stent: No generic text analytics platform can handle financial language
“There is no on the market generic text analytics platform that can handle this type of language. No way.”
Amanda Stent Mar 2, 2018 ▶ 6:51 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)

Their most notable contradicted claim

Assertion Contradicted
Stent: Bloomberg sentiment feed outputs probabilistic buy or sell signals
“So the Bloomberg sentiment feed is not just positive or negative. I like it right. It's buy or sell, and it's with probability so that you can use those in your own models.”
Amanda Stent Mar 2, 2018 ▶ 23:30 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
none yet certainty 3
67% certainty 4
75% certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

How they sound: speaking style how? →

256 words/min while actually speaking · 13.1 um and uh per 1k words

No argument clarity score for Amanda Stent: only 1 usable question→answer exchange on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,736 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Amanda Stent said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Supported
Stent: No generic text analytics platform can handle financial language
“There is no on the market generic text analytics platform that can handle this type of language. No way.”
Amanda Stent Mar 2, 2018 ▶ 6:51 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Disclosure
Bloomberg can automatically generate news articles without humans in two minutes
“And this kind of article we can now automatically generate. So you can get this with no human intervention within two minutes, and then within five minutes a human journalist can come along in art color.”
Amanda Stent Mar 2, 2018 ▶ 8:29 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Assertion Partly supported
Generic NLP mistakenly linked Bill O'Reilly news to O'Reilly Auto Parts
“Okay, so Bill O'Reilly said something, and there's a small and innocent company called O'Reilly Auto Parts somewhere in the Midwest, and a bunch of generic text analytics engines linked Bill O'Reilly to O'Reilly Auto Parts, and the O'Reilly Auto Parts stock dr…”
Amanda Stent Mar 2, 2018 ▶ 9:48 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Disclosure
Bloomberg filters social media manipulation using account metadata and supply chain data
“We do a lot of historical monitoring, so we can know how old a social media account is. We also have a very, very large collection of pieces of information about entities in the world, so we can know how closely associated a particular handle is. With a decisi…”
Amanda Stent Mar 2, 2018 ▶ 16:15 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Disclosure
Stent: Bloomberg shifted data labeling to deep learning and decision trees
“And that's something that historically has been done mostly with rule-based systems, but today we do it with deep learning and a lot of decision trees.”
Amanda Stent Mar 2, 2018 ▶ 13:49 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Insight
Stent: Earnings call cues cannot reliably predict financial performance
“So we can't always predict based on the non-linguistic or even the linguistic features of a, of an earnings call or an earnings release.”
Amanda Stent Mar 2, 2018 ▶ 23:13 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Assertion Supported
Stent: One-minute news reporting delay corresponded to 3% stock price move
“The distance between 10 38 and 10 39 Was about three percent in the stock price. The distance between the SEC headline and 1055, 20 minutes later, was a 12% absolute difference in the stock price.”
Amanda Stent Mar 2, 2018 ▶ 3:13 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Assertion Supported
Stent: SEC declared Twitter valid for official corporate disclosures
“SEC has declared that Twitter, in particular, can be used to provide company, official company announcements.”
Amanda Stent Mar 2, 2018 ▶ 3:38 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Insight
Stent: Vertical NLP faces key hurdles in domain language, speed, and precision
“Here are three key challenges that we face that would be present to a greater or lesser extent in any other highly highly vertical industry. The first is financial language, idiomatic language. The second is the need for speed. Remember, I said a minute. And t…”
Amanda Stent Mar 2, 2018 ▶ 5:41 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Assertion Not checkable as stated
Bloomberg NLP models feed over 100 Bloomberg Terminal screens
“And they feed over a hundred different screens within the Bloomberg terminal, which provide company trend information, company tweet information, sentiment information, sentiment alerts, tweets on mergers, tweets by the president of the United States, et ceter…”
Amanda Stent Mar 2, 2018 ▶ 9:17 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Disclosure
Stent: Bloomberg Terminal contains between 15,000 and 40,000 functions
“There are, nobody has told me the actual number. I've heard numbers from between 15 and 40,000 individual functions inside the Bloomberg. Each of them is fed by at least one data feed, and in many cases, several.”
Amanda Stent Mar 2, 2018 ▶ 14:12 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Opinion
Stent: Bloomberg's human-written stories are much more informative than automated ones
“Our manually generated stories, which are frankly much more informative”
Amanda Stent Mar 2, 2018 ▶ 21:12 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Assertion Contradicted
Stent: Bloomberg sentiment feed outputs probabilistic buy or sell signals
“So the Bloomberg sentiment feed is not just positive or negative. I like it right. It's buy or sell, and it's with probability so that you can use those in your own models.”
Amanda Stent Mar 2, 2018 ▶ 23:30 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
Disclosure
Stent: Bloomberg NLP aims to inform trading decisions, not track sentiment
“At Bloomberg, we're not trying to identify net promoter score. We're trying to really understand what's going on so that someone else can decide whether they should buy or sell this company.”
Amanda Stent Mar 2, 2018 ▶ 5:31 Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)

Appearances (1)

EpisodeDateSpeaking time
Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven) Mar 2, 2018 17m
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 400 conversations transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.

Try the podcast clipper Learn more