Interesting interview on the excellent TWII pod from a few weeks ago
https://www.youtube.com/watch?v=4IpImtzYLF0&list=PLmX3nul4ishIIEZ2_lchlg9hUSYor0Yoy&index=1
This guy has spun up an AI analyst for US (only) companies.
Works for dual listed like RMD too.
Just put in the US ticker and your e-mail address and it sends you a 40+ page PDF of the result.
Free to use (while in beta), will likely be unaffordable for retail investors when they monetise it later so get in now if you’re interested.
https://www.veritasalpha.com/frg
Very comprehensive starting point for analysing a business – includes SWOT, Porter’s 5 forces, etc.
Has me thinking about how to replicate / modify for ASX businesses…
Anthropic has a new product specifically for financial analysis:
I haven't tried it yet, but apparently the Norwegian Sovereign Wealth fund is using it (see here)
I like it both ways...
I tend to think that there are broadly 2 ways different ways to use AI for research and analysis.
Open - where a Deep Research or similar model will trawl the internet to conduct research and analysis like an analyst (or 10) would to respond to your prompt. This is generally what people mean when they say LLM I believe and is the default you would get when using ChatGPT, or Google's Gemini, Perplexity, etc.
Closed - where you load the information you want like Annual reports, books, documents, investment checklists, websites, etc and ask it to interrogate ONLY THOSE SOURCES for whatever you want to know. Eg - Changes over 5 Annual reports in the Rem report, KAM, related party transactions, strategy, etc, etc.
Most providers have both options, the standard entry level is usually Open, then Closed are typically within that like CustomGPT, Perplexity's Spaces, Gemini Gems, and Google's NotebookLM (standalone).
So what to use?
Best to use more than one model if you can.
I use Google as my primary AI for a few reasons.
For the standard US$20 per month you get both Gemini (Open) and NotebookLM (Closed).
Gemini's Pro 2.5 is one of the top models according to benchmarks and I am used to it now and like it a lot.
NotebookLM has a lot of capacity to upload PDFs, URLs, etc, etc and a lot of ways to output - audio summaries / podcasts, Summaries, mind maps, etc. I like this even more.
One generic approach I use when looking at a new business is to load a standardised prompt into both Gemini and Perplexity Pro for a particular company. Then generate PDF outputs from the responses and load theses from each into NotebookLM and generate an audio summary.
That way in the space of about an hour (1/2 hour to run + 1/2 hour to listen) you get a podcast style run-down of the most important results from your prompt to steer your next steps in a more informed direction.
If you like the pod you can download it, ask notebook to generate a transcript, etc to archive for reference.
You can also customise the pod with more detailed prompts, make it longer, or shorter and even interrupt with your own questions during (interactive mode). Amazing!
Health warning
There is emerging and growing evidence that AI makes you stupid (paraphrasing).
It seems outsourcing your thinking to AI diminishes your ability to do it for yourself. Makes sense to me.
So it probably depends on how you use it?
A bit like your phone or social media, AI is designed to make you dependent on it, so you need to guard against letting it or it will reduce your ability to DYOR just like social media on your phone has shortened your attention span...
The story in today’s Fin Review “Will equity analysts be replaced by AI? That’s a billion-dollar question” opens with “Equity analysts are on the precipice of being hugely disrupted by artificial intelligence.”
Reminds me of the saying AI probably won’t take your job but someone who knows how to use AI might.
Gary Mishuris recently published an interesting article showing his AI use cases as a Portfolio Manager / Analyst - How AI Is Enhancing My Investment Process— and How It Can Help You
https://behavioralvalueinvestor.substack.com/p/how-ai-is-enhancing-my-investment
A more detailed presentation of Gary's approach is here - https://drive.google.com/file/d/1i-22YGdka0FATSFE_Svk9io3NaRG22dd/view
Of course any AI use cases should be aligned to your individual strategy but you could do a lot worst than starting here.
One strategy that should work for all in equity analysis and beyond is start by trying to get AI to help you with real world problems and tasks, be prepared for it to surprise you and your curiosity will eventually turn you into someone who knows how to use AI.
If you’re still not sure which AI to use for your purpose… ask AI. They (almost?) all have free versions to try.
Start with that real world problem and you’re on your way.