This doesn't seem that difficult to implement into a story. There are 125 earnings reports today [1], a retelling of the headline numbers with competitor analysis should fit the bill for reporting on most of them.
==Trained accountants and analysts can miss details when reading through those, so how is AI going to parse it any better?==
How could we expect a journalist to parse it better? Is that really the goal of news on financial reports?
Also, journalists are supposed to provide the "facts", not analysis. They aren't financial experts so even if they provided analysis, I wouldn't put much stock in them.
Data quality varies from vendor to vendor. Additionally, speed is a factor in how profitably some strategies can be executed. When firms are examining bits on the wire to guess whether earnings were good or not (before the full headline arrives), you can’t necessarily wait for the vendors to update their releases, especially since all of your competitors will have exactly the same data.
As 'structured data' goes, it's nowhere near where it needs to be to support 'instant article generation' beyond anything but the shallowest headline numbers. The consolidated financial statements all have appendices (notes) with relevant details. The case study you linked to simply indicates only that reduced man-hours significantly by automating the process of manually picking numbers and putting them into an article template. They may as well be putting out a press release.
Although the topic of the post is how automation affects financial journalism, it bears mentioning that an analysts' job is to reverse engineer the report to see how they arrived at those numbers. The vendors' auto-generated reports never include formulas, so you'll have to be doing your own calculations as part of your due diligence.
For instance, if they're trading at a high P/E ratio, how much of that is due to positive investor sentiment and not related to recent buybacks? The headline numbers won't reveal that, but past data and the notes to the financial statements usually will.
If their cash balance says $x billion, how much of that came from convertible bond issues that are coming due in the next 12 months?
[1]http://ir.tesla.com/sec-filings?field_nir_sec_form_group_tar...
As an accountant in a prior life, I can tell you that this approach won't provide anything near "the most pertinent facts". That's because public earnings reports are written specifically to circumvent automated analysis. Wall Street firms have tools in place to scrape the data tables from those PDFs, and even then the technology isn't perfect, because layouts aren't standardized (merged cells, table built in InDesign, etc).
At best, it will get the headline numbers right, which are meaningless without context. What does a quarterly profit of $3.3m for MSTR mean without benchmarking against its competitors, or taking the macroeconomic environment (interest rates, etc) into account?
The headline numbers on the balance sheet, P&L and cash flow statement don't say nearly as much as the notes to those statements, which often contain minute details that are extremely relevant for investors and analysts. Trained accountants and analysts can miss details when reading through those, so how is AI going to parse it any better?
Unfortunately, this smacks of "quantity of articles" over quality of analysis, the latter of which represents why journalism is valuable and necessary.