Had a similar experience with meta. Extremely opaque decision making, terrible UX. Account permanently disabled ... did not follow community standards, literally on sign up to get a dx account.
It's difficult after an experience like that to see how they are so successful. Is it because their users are so addicted and ad sellers will do anything to get onboard ? It's probably just a few dark patterns here and there to bump up impressions at will.
Feels more and more like it's the best of times and the worst of times...
Things feel off,but you have (mostly well-off) people talking about how great things are going to be and are. I suspect a lot of this correlates to the stock market.
For the median millenial atleast, whatever you were taught growing up is just the wrong guide to understanding the world today.
I think social media by its very nature, has to be understood inverted. A high number of posts about how great things are suggests otherwise. Comparisons with Europe have gone exponential that tells you more about how the posters feel than anything else...
As I complete my travels through Argentina and a layover in Lima
I realize that differences between poor and rich are much less stark in my opinion in the US and these LatAm countries where the percentage of population living below the local poverty line is 3 to 5 times higher than that of the USA
People don't realize that being broke poor in the US is a luxury life in most developing countries.
I'm not from the US but I saw some video of of how some homeless people live in the US, and it's a lifestyle most of the developing world would kill for: dumpster dive for freshly made pizza or quality supermarket produce that's still good just not legal to be sold anymore, all free of charge. If you have a medical issue, go to the ER and get top quality healthcare. Dumpster dive for perfectly usable clothes, computers, etc. That's a luxury life for 80% of the planet.
Yeah they have, but being broke poor in those developing nations sucks much more than in the US. Their development is mostly reflected in the lifestyle of middle and upper class not of the poor. You have no idea how good the poor in the US have it compared to large parts of the world.
You said luxury life, that is different than saying the poor in USA are better off than the poor in for example China. American poor don't live as well as luxury life in China.
Why use China? I’d rather be poor in the US than rich in DRC, Sudan, Yemen, Libya, eritrea, Somalia, Bangladesh, Pakistan, Afghanistan, Syria, Haiti, etc etc
Using the second largest economy which has many mega cities you can tour on Google earth and plenty of millionaires and billionaires isn’t really the right comparison. Like using Japan or Germany.
Well, 71% of the world’s surface is ocean and about 10% is desert. So that’s over 81% of the planet. I think we can now both agree that you are completely wrong and I am completely right.
I don't think excel can be defeated :) I certainly do not expect financial professionals to switch to a chat interface.
When you login into capitaliq or factset or look at a bloomberg screen, you access data, you can then do the same here. Excel plugins go on top which can then get this data into excel to build models. The api that powers this app, can also send data into excel for instance.
Data can be copied already directly from the chat box into excel, maintaining the table format for e.g
Regarding standardization, I think data was standardized not to enable comparison but simply to fit into the same schema for every industry.
Most companies within the same industry report the same way. REITs do not report like SaaS, but the existing datasets put it all into one set.
Raw data from source is always better as you can convert it to standardized but you can't go back...
If this product is not meant for finance professionals then who is the target customer?
Retrieving numbers via ChatGPT and then feeding it into Excel via APIs seems like a very odd thing to do. Especially when most of these numbers are already available on Yahoo finance for individual investors and CapitalIQ for professionals.
:) I guess its not very odd to say investment research is a lot of reading and LLMs are already very good at reading. So there is little doubt that LLMs will change the investment research process.
Have you used CapitalIQ, do you use it on the browser or in excel. If you use it on the browser, the experience using beatandraise.com is already better for some cases. For instance, try getting Apple's Rest of Asia Pacific revenues which apple has been highlighting is their main growth region this year, you can pull that out easily. https://imgur.com/a/rRnrB4u
Can you do that on capitaliq, you would have to hope their standardized tables include this...
I think you are referring to the more hyped version where somehow LLMs can figure out how to get the most relevant information from 10Ks and do most of the investment process. My mental model is simply that we have an assistant, an amazing assistant that can read pretty well, and we can use them in the investment process.
Yes, information retrieval is hard :) A lot of people ask for 'can you get Apple's revenues by product category for all of 2020', how do you get the smallest piece of text that has the most information about product category and revenues ?I can get a lot of text, but that would mean I probably run over the context limit and so on :)
That's a good question, for e.g if you want to get Microsoft's revenues by product category or apple's revenues in greater China. https://imgur.com/a/LdQkt7j
You can also do this across time with constraints on context limits ...
What is a traditional method, would you search the string or would you find the named entity (using NLP) and look for the entity ? or do you mean a ctrl +F in the document ?
I guess the premise of LLMs, is that we have an intelligence that can read and write, so it can do this in an automated way in different applications. In this case, I am trying to automate and speed up the investment research process, along the way, we can create our own dataset of financial data that can be generated on the fly as necessary.
Also, how would you extract an income statement from the text, also in the same img using a traditional method ? I personally find it magical it can do that, it knows where it ends and so on...
Thanks for letting me know, i guess you use a different strategy of querying by company and by document. I see this when I try to get Apple's rest of asia pacific revenues for e.g
I guess it really depends on how your targeted user would use it... :)
Yes, they are definitely not primitive, but considering how the breakthrough in LLMs happened, i.e MSFT, GOOGL were working on it, even as late as 2021, Google's BERT was not really there (at least the one they showed the public in the blog from Google research). So the sudden jump from ok to great thanks to openAI would probably mean a lot of existing systems also need to do that.
As the answers are from the text, we can avoid hallucinations for the most part. I have not experienced made up numbers, instead errors are numbers that are typically misplaced, where it gives you revenues for the last 6 months instead of the last 3 months, when they are both next to each other in a table and so on... or the same numbers for different quarters and so on.
From my experience, GPT4 has been very good in following instructions and doesn't make up numbers which Bard is much more susceptible to. Bard relies heavily on snippets from the web search and completes the rest...
I am pretty sure if google wanted to train it, it would get all the answers right, but the way its designed, it gets one or two numbers and makes the rest up rather hilariously :) and even adds a breakdown which is also made up..
