I used to work for a human that did this (sits mostly on the classical therapeutics side). He actually started a business where he was reviewing and auditing the submission processes outlining approvals but he had been around the game enough to know where the next submission would put them in the approvals process for a number of agencies.
Looks like he's still on top of everything given the most recent blog post is from 6/2/2026.
I believe the insights here could be useful given he has sense of when the penultimate submission has occured (but I'm not entirely sure what that is on a % basis nor as a basis for if the stock of the company reacts)
I used to work at a private investment fund as a data engineer for building in house models to evaluate drug programs and biotech companies. We took a pretty varied approach with catalysts, investment data, people data, trial data, but also analyses on the molecule and drug itself. It was a lot of work and I really don't think we made a dent into understanding what succeeds and what doesnt. Also investors in biotech are really underwriting the biology. Its why they mostly invest in fast follow or me toos rather than new technology or new therapies. The work was a bit sad and less exciting than I thought.
That's interesting. I am curious, what kind of analyses did you work with on the molecule and drug itself? Was it like mostly reading papers/patents or did your team do anything experimental?
Interesting, biotech stocks have been notoriously hard to predict because their business model revolves around science, and it’s hard to know when the science is right. Depending on the situation, I think sentiment could potentially be a misleading/confounding variable here…
Sentiment is crucial - if you know sentiment is incorrectly oriented, you can capitalize on it. If you know it's correct, you can identify mispricing, and strategize accordingly.
Why do you think that LLMs would do any better than monkeys throwing darts?
I am raining on your parade but this is another in a long succession of ways to loose money.
The publicly available information in markets is priced very efficiently, us computer types do not like that and we like to think that our pattern analysis machines can do better than a room full of traders. They cannot.
The money to be made in markets is from private information and that is a crime (insider trading), is widespread, and any system like this is fighting it and will loose.
Our initial goal with this project actually wasn't trying to get an edge in terms of better evaluating information, but rather, we wanted to see if an LLM can perform similarly to a human analyst at a lower latency. The latency for the market to react to catalysts is actually surprisingly high in biotech (at least in some cases) compared to other domains so there may be some edge there.
Appreciate the comment though! I generally agree with your sentiment!
I used to work for a human that did this (sits mostly on the classical therapeutics side). He actually started a business where he was reviewing and auditing the submission processes outlining approvals but he had been around the game enough to know where the next submission would put them in the approvals process for a number of agencies.
https://maestrodatabase.com/
Looks like he's still on top of everything given the most recent blog post is from 6/2/2026.
I believe the insights here could be useful given he has sense of when the penultimate submission has occured (but I'm not entirely sure what that is on a % basis nor as a basis for if the stock of the company reacts)
Yes we know of a few. Honestly, it was pretty hard to even find a good catalyst calendar for this space.
I'll give it a read to learn more. Thanks for the note!
I used to work at a private investment fund as a data engineer for building in house models to evaluate drug programs and biotech companies. We took a pretty varied approach with catalysts, investment data, people data, trial data, but also analyses on the molecule and drug itself. It was a lot of work and I really don't think we made a dent into understanding what succeeds and what doesnt. Also investors in biotech are really underwriting the biology. Its why they mostly invest in fast follow or me toos rather than new technology or new therapies. The work was a bit sad and less exciting than I thought.
That's interesting. I am curious, what kind of analyses did you work with on the molecule and drug itself? Was it like mostly reading papers/patents or did your team do anything experimental?
Interesting, biotech stocks have been notoriously hard to predict because their business model revolves around science, and it’s hard to know when the science is right. Depending on the situation, I think sentiment could potentially be a misleading/confounding variable here…
Sentiment is crucial - if you know sentiment is incorrectly oriented, you can capitalize on it. If you know it's correct, you can identify mispricing, and strategize accordingly.
Why do you think that LLMs would do any better than monkeys throwing darts?
I am raining on your parade but this is another in a long succession of ways to loose money.
The publicly available information in markets is priced very efficiently, us computer types do not like that and we like to think that our pattern analysis machines can do better than a room full of traders. They cannot.
The money to be made in markets is from private information and that is a crime (insider trading), is widespread, and any system like this is fighting it and will loose.
Our initial goal with this project actually wasn't trying to get an edge in terms of better evaluating information, but rather, we wanted to see if an LLM can perform similarly to a human analyst at a lower latency. The latency for the market to react to catalysts is actually surprisingly high in biotech (at least in some cases) compared to other domains so there may be some edge there.
Appreciate the comment though! I generally agree with your sentiment!
efficiency is not a given. also this is an eval set - they acknowledge the challenge themselves.
imho this is v cool