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Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

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61–70 of 109 posts

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#61
> For example, our research shows it can take 12-18 months for corporate malfeasance to be incorporated into stock price after clear warning signs appear in financial text.

One thing I noticed about "Efficient Market Theory" is that it is unfalsifiable. It isn't a scientific theory and it also cannot be proved true or false, only useful when convenient. It relies on magic, rationality, and the assumption of omniscience by large investment banks.

Nothing is priced in.

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#62
Given your example boilerplate disclosure, is one of your key conditional statements simply categorizing boilerplate? Like instead of simply putting a sentiment score on the wording, you first isolate it as boilerplate or as a unique potential aberration and then assign weights?

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#63

> For example, our research shows it can take 12-18 months for corporate malfeasance to be incorporated into stock price after clear warning signs appear in financial text. One thing I noticed about "Efficient Market Theory" is that it is unfalsifiable. It isn't a scientific theory and it also cannot be proved true or false, only useful when convenient. It relies on magic, rationality, and the assumption of omniscien…

I love that. I agree until the "nothing" bit. Some things do get priced in. You can see specific stocks "react" to news, adjusted for market returns etc. ESG factors do appear to be getting "priced in" as well

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#64

Given your example boilerplate disclosure, is one of your key conditional statements simply categorizing boilerplate? Like instead of simply putting a sentiment score on the wording, you first isolate it as boilerplate or as a unique potential aberration and then assign weights?

You've hit the nail on the head (mostly)...but I'm going to say no more because its part of our secret sauce

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#65
post #47

How would you address would-be filers from using your own product from iterating on their wording until the red flags are removed?

Realistically they probably don't need to do that. The filings are usually pretty heavily lawyered up and the lawyers are pretty lazy on language updates.

Yeah, people have been doing sentiment analysis of press releases etc for years now and very few corporates bother trying to use software to test/counteract it

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#66

Congratulations! I played with that database in 2017 when they opened up the text. I'm so happy to see it's finally been picked up. I built a thing which recreated the Income statements and then flagged for non-conformance. I found Babcock & Wilcox Enterprises "Good will impairment charges" as an anomalous line in their Nov 8, 2017 filing just prior to when their CEO resigned in 2018 and they had to make so many adju…

Which database?

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#68
post #66

Congratulations! I played with that database in 2017 when they opened up the text. I'm so happy to see it's finally been picked up. I built a thing which recreated the Income statements and then flagged for non-conformance. I found Babcock & Wilcox Enterprises "Good will impairment charges" as an anomalous line in their Nov 8, 2017 filing just prior to when their CEO resigned in 2018 and they had to make so many adju…

Which database?

I assume they are talking about EDGAR - https://www.sec.gov/edgar/search-and-access

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#69

Given your example boilerplate disclosure, is one of your key conditional statements simply categorizing boilerplate? Like instead of simply putting a sentiment score on the wording, you first isolate it as boilerplate or as a unique potential aberration and then assign weights?

You've hit the nail on the head (mostly)...but I'm going to say no more because its part of our secret sauce

Yes, I figured! I found your synopsis to be inspirational

An additional tool I always wanted was to match external - even macroeconomic - events to company risk factors

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#70
As someone who has looked into alternative data business models for the finance industry, this is really awesome to see someone doing this as a company. I was interested to understand how you think about your revenue model? I feel that if your data provides alpha (i.e. selling before other people are aware of the problematic disclosures), as your models become validated within the industry, someone/some firm is going to use it to generate alpha. But then you have a problem where, that one firm that captures most of the value, and takes it from other participants who now lose the value-add of your product.

How do you balance those two sides? I mean it as a potential customer who would love to pay for your product, but want to understand how you prevent this becoming a alpha-generating NLP strategy for one hedge firm who pays the most for it.

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