So the history of this type of research as I know it was that we
- started to diff the executives statements from one quarter to another. Like engineering projects alot of this is pretty standard so the starting point is the last doc. Diffing allowed us to see what the executives added and thought was important and also showed what they removed. This worked well and for some things still does, this is what a warrant canary does, but stopped generating much alpha around 2010ish.
- simple sentiment. We started to count positive and negative words to build a poor mans sentiment analysis that could be done very quickly upon doc release to trade upon. worked great up until around 2013ish before it started to be gamed and even bankruptcy notices gave positive sentiment scores by this metric.
- sentiment models. Using proper models and not just positive and negative word counts we built sentiment models to read what the executives were saying. This worked well until about 2015/2016ish in my world view as by then executives carefully wrote out their remarks and had been coached to use only positive words. Worked until twitter killed the fire hose, and wasn't very reliable as reputable news accounts kept getting hacked. I remember i think AP new's account got hacked and reported a bombing at the white house that screwed up a few funds.
You also had Anne Hathaway news pushing up Berkshire Hathaway's share price type issues in this time period.
- there was a period here where we kept the same technology but used it everywhere from the twitter firehose to news articles to build a realtime sentiment model for companies and sectors. Not sure it generates much alpha due to garbage in, garbage out and data cleaning issues.
- LLMs, with about GPT2 we could build models to do the sentiment analysis for us, but they had to be built out of foundational models and trained inhouse due to context limitations. Again this has been gamed by executives so alot of the research that I know of is now targeted at ingesting the Financials of companies and being able to ask questions quickly without math and programming.
ie what are the top 5 firms in the consider discretionary space that are growing their earnings the fastest while not yet raising their dividends and whose share price hasn't kept up with their sectors average growth.
I have no window into this world but I am curious if you know anything about the techniques that investors used to short or just analyze Tesla stock during the production hell of 2017-2020? It was an interesting window in ways that firms use to measure as much of the company as they can from the outside. In fact was there any other stock that was as heaving watched during that time?
Looking back at that era it seemed investors were too focused on the numbers and fundamentals, even setting up live feeds of the factories to count the number of cars coming out and thats the same feeling I get from your post. It seems like dumb analysis ie. analysis without much context.
We now know from the recent Isaacson biography what was happening on the other side. The shorts failed to measure the clever unorthodox ways that Musk and co would take to get the delivery numbers up. For example: The famous Tent. Musk used a loophole in CA laws to set up a giant tent in the parking lot and allowed him to boost the production by eliminating entire bottlenecks from the factory design. There is also just the religious like fervor with which the employees wanted to beat the shorts. I dont think this can be measured no? It helped to get them past the finish line.
Most companies aren’t obsessed enough with shortens to try and hide poor results from analysis that will be exposed in 3 months anyway. There’s always ways around them - number of cars registered, number of delivery trucks visiting, time for delivery on website, how much overtime is being worked etc.
Markets aren't sports teams, i.e. bimodal camps with us vs. them drama. Twitter discussion of markets, maybe, but not markets.
I've been on both sides of this trade, regularly.
Bear thesis back then was same as now. In retrospect, I give it a few more credits because Elon says they were getting close to bankrupt while he was posting "bankwupt" memes and selling short shorts.
Being a pessimist, and putting your money where your mouth is in markets, is difficult because you have to be right and have the right timing.
- started to diff the executives statements from one quarter to another. Like engineering projects alot of this is pretty standard so the starting point is the last doc. Diffing allowed us to see what the executives added and thought was important and also showed what they removed. This worked well and for some things still does, this is what a warrant canary does, but stopped generating much alpha around 2010ish.
- simple sentiment. We started to count positive and negative words to build a poor mans sentiment analysis that could be done very quickly upon doc release to trade upon. worked great up until around 2013ish before it started to be gamed and even bankruptcy notices gave positive sentiment scores by this metric.
- sentiment models. Using proper models and not just positive and negative word counts we built sentiment models to read what the executives were saying. This worked well until about 2015/2016ish in my world view as by then executives carefully wrote out their remarks and had been coached to use only positive words. Worked until twitter killed the fire hose, and wasn't very reliable as reputable news accounts kept getting hacked. I remember i think AP new's account got hacked and reported a bombing at the white house that screwed up a few funds.
You also had Anne Hathaway news pushing up Berkshire Hathaway's share price type issues in this time period.
- there was a period here where we kept the same technology but used it everywhere from the twitter firehose to news articles to build a realtime sentiment model for companies and sectors. Not sure it generates much alpha due to garbage in, garbage out and data cleaning issues.
- LLMs, with about GPT2 we could build models to do the sentiment analysis for us, but they had to be built out of foundational models and trained inhouse due to context limitations. Again this has been gamed by executives so alot of the research that I know of is now targeted at ingesting the Financials of companies and being able to ask questions quickly without math and programming.
ie what are the top 5 firms in the consider discretionary space that are growing their earnings the fastest while not yet raising their dividends and whose share price hasn't kept up with their sectors average growth.