Related: I wrote some code that attempts to extract the latest SPAC data (which you can filter/export) from SEC filings as they're filed: https://docoh.com/spacs
We've been building the freemium website for a few years. We launched paid plans in Feb ($19/m for additional features and higher limits), which grew slowly over the last 6 months, but not to a point where they were covering costs, or likely to any time soon.
The site is popular though - we get about half a million pageviews a month, and have about 7k subscribers to a daily newsletter, and growing every month. We just weren't getting Pro signups - the value prop wasn't there.
We could have put more of the free features behind the paywall, but the industry is getting ultra-competitive, with all our competitors reducing their subscription prices. So with the trajectory of "the value of the data is going to zero" together with "the best way to help everyday investors is to give everyone access to the same information", we decided to change business model and switch to ads/sponsorship (to be implemented soon), which estimates seem to suggest will allow us to much more easily cover our monthly costs.
It's not perfect – XBRL is complicated and companies file with lots of proprietary metrics that aren't included in GAAP/FASB standards – but we hope to do more with it in the coming months.
Still a long way to go, but trying to make it easier (and cheaper!) to research public companies / stocks - with both quantitative and qualitative data. https://docoh.com/
One further thing, not sure how easy it would be to do, but it would be very interesting to see (graph and/or table) a history of different financial ratios - e.g. what is the historical P/E of the business etc...
We're looking for a Customer Success Manager (Account Manager) to join our growing team in central Manhattan.
We're a growing, VC-funded fintech SaaS company building a modern platform for investment professionals (mostly billion-dollar-plus hedge funds) to manage their research and make better investment decisions.
As a Customer Success Manager (CSM), you will be entrusted with the relationships, strategy and well-being of Bipsync’s fund customers.
You need commercial experience with account management / customer relationship management, and an understanding of the internet technology and finance industries.
We offer a competitive salary and significant benefits (stock options, bonus, flexible working, travel) in an exciting and friendly environment. We are an equal-opportunities company that values diversity, and welcome all qualified applicants.
Bipsync | New York, NY | Senior Software Engineer | Onsite | Full-time | To $115,000 plus equity package, healthcare, dental, vision and flexible working
Bipsync is a fast growing, venture-funded SaaS startup with a product obsessively designed to help financial organizations manage their research. Most of our customers are multi-billion dollar hedge funds based in New York. As a Senior Software Engineer you’ll use your full-stack skills to develop the product on a range of platforms, including web, desktop and mobile.
Interviews will usually be a quick telephone call followed by an in-person meeting. We don't do crazy problem-solving whiteboard stunts.
I'm not convinced the "evidence" is suggesting anything without further data, and this article is glossing over "correlation != causation". Without a baseline (a situation where, over the same years, tuition fees weren't introduced/raised), how do we know what the equivalent situation would be? Perhaps there would have been even more people from poorer backgrounds applying.
It is evidence. No need to put it in scare quotes. We can debate the correct interpretation and applicability, but it's certainly evidence of a sort.
It's impossible to run the kinds of controlled large scale socio-economic experiments you describe. If we ignore the evidence that doesn't meet your high standards, we'll have no real world data.
Recently it was discovered that working 20 hours a week while going to school results in worse grades - after decades of believing the exact opposite. When they did better studies - comparing like students (same school, same background, and other characteristics), they discovered that all the prior studies were just showing the success of students who really should have been even more successful. So I prefer to think of most studies such as this as a datapoint, rather than any type of evidence.
You might want to look up the definition of evidence. My reading of three definitions just doesn't quite match your definition. But no big deal. I think we agree, and this has become about word interpretation.
* No comparison with other time periods, and their respective tuition fees.
* No comparison with other countries, and their respective policies.
* No data on where these students graduate from, or in which subjects, or with which grades, or what their average salary is X years later, or whether they'll actually be able to pay back the loan.
It's missing a huge amount of important information; you really can't justify the conclusion "tuition fees are good" from this alone.
