Unfortunately much of earnings responses is written after the fact. If the stock price goes up you can point to some positive parts. If it goes down, you can point to some negative parts. I see that all the time. "Earnings beat estimates but analysts were worried about declining growth in app related earnings and looming regulatory threats". Sometimes they even switch! The stock goes up, reporters write their pieces, then it goes back down and switch over to another explanation.
I'm reminded of the headline "More than 16M Americans have lost jobs in 3 week, dow's best week since 1938"
> I'm reminded of the headline "More than 16M Americans have lost jobs in 3 week, dow's best week since 1938"
That's likely a significant contributor behind the November election results in the US and across Western countries in general. People aren't dumb, they spot through politicians and media claiming "good economy numbers" while their own paychecks don't come close to keeping up with inflation.
The economy numbers may be at record highs, but way too much of the wealth ends up in the pockets of a very select few.
> The economy numbers may be at record highs, but way too much of the wealth ends up in the pockets of a very select few.
It's not that at all. Things like Russia spending all its money on the war effort boosts GDP, but doesn't help prices in the long term. The measures often don't measure costs, other than inflation.
It might kick out 2 or 3 "good idea" trades a year that you could milk, but the real value in this data is getting it quickly into the hands of people who manage sector portfolios.
Say for example a filing happens that a company will be impacted by trade sanctions. You could short the stock as a naive investor and maybe make a few bucks. Someone with sector knowledge will rebalance their portfolio knowing that customer demand for widgets will shift to sprockets which in turn require doodads. AI isn't there yet because it requires a lot of non-public knowledge to really make money off of.
The value is in scaling this beyond what any individual could do. Yes, the data is public, but building reliable tooling around it, continuously validating the analysis, and expanding coverage across all filings creates far more interesting opportunities than solo trading.
We've already seen how just having basic sentiment and risk analysis has helped our users catch significant changes they would have missed - like subtle shifts in supply chain disclosures that preceded market moves. As we add features like more filing types, earnings call transcripts, and cross-company competitive insights, the system becomes more valuable for everyone.
Plenty of after hours trading happens around these releases so there’s certainly the potential for them to be useful to people engaging in that trading.
It's this. Just doing basic analytics on SEC data for free provides a lot of value to the niche looking for it. A few players have realized that ingesting and normalizing the data is half the battle and charge for very small subsets of data, for example cybersecurity breaches.
Most of the data is unstructured, or worse Edgar xml, adding yet another barrier.
I suspect this case is mostly about sentiment analysis and summary using LLM. The claims about reacting before the market seem egregious.
I think this tool surfaces information not previously immediately digestible by retail investors.
Institutional investors have had systems like this in place for decades. LLMs might improve parsing the data in some ways, but this is (and was) completely doable before the LLM era, meaning there’s probably not a huge amount of secret sauce here worth protecting.
If anything, a project like this simply improves the information asymmetry between retail and institutional investors.
Not true. I can’t speak to the situation in the US but, in the UK, there are certainly platforms that enable after hours trading on US markets even for retail traders.
