We live in an era of 2.0s. This is probably going to turn out to be Phrenology 2.0.
It isn't even a settled debate whether CEOs know what is going on or are a particularly important driver of outcomes. It is unlikely that NLP models can foresee the future, even if bankrolled by hedge funds.
Of course they can not predict the future in the strong sense, but they may be able to extract some fraction of a bit of information about the distribution of possible outcomes, slightly reducing the variance. This is enough for a hedge fund to make money. Similar to knowing something like how full a department stores parking spots have been over the last quarter. It won't give you an accurate prediction, but it can give you a slight edge over the competition.
It isn't even a settled debate whether CEOs know what is going on or are a particularly important driver of outcomes.
It's pretty clear that a negligent/criminal CEO will lead to the bankruptcy of a company and a loss for investors. So yes, you can say at the extreme end that CEO's are drivers of important outcomes.
Now whether it's effective I think is another subject all-together. My own opinion is that micro-expressions, body language, etc. have been studied and used by the FBI/CIA for their field work. The idea being that once you establish a baseline behavior, you can notice "clusters" that deviate from the baseline based on verbal+non-verbal cues. So I don't see why an AI couldn't do the job.
Warren Buffett has a famous saying: "I always invest in companies an idiot could run, because one day one will".
There are many companies where it's likely that a negligent CEO (even a criminal one) wouldn't lead to the bankruptcy or even close. Many companies might be better off with a negligent CEO rather than one actually trying to do much. Right now Google could be run by a person on the beach sipping margaritas. Many other big public companies with very strong competitive positions probably could too.
A lot of things have been studied, the question is what evidence they have that it actually works. It seems similar to one of those things where people have made a lot of claims with only dubious evidence.
This sounds like AI hucksters justifying their existence… perhaps we’re moving into a later stage in the hype cycle.
I suppose you could have a portfolio of AI tools that would alternate between telling you “stonk go up” and “stonk go down”. Blame the customer for choosing the wrong one if they are unhappy.
It feels like people have been predicting the end of the "hype cycle" for the last decade.
I agree that this particular application seems... questionable, to say the least, but finance is probably the last place where data scientists need to work very hard to justify their efforts. Statistical modeling has been a core part investing for a very long time, and ML is just a subset of statistical modeling.
Speaking of phrenology when I was still in the investment business we were trained in the “facial action coding system” which purportedly teaches one to read the facial ticks of your conversation partner to see if they are being honest. I’m not really sure there is a signal there, but it was fun to learn, and fairly trendy in the industry at the time.
Of course we also did nlp to identify companies that were using language associated with negative returns in their quarterly conference calls. This was very successful, mostly by identifying pieces of shit we had not heard of yet. Once identified there were usually much stronger red flags. But the performance of that strategy was good. It definitely was not phrenology. Around 2001-2005.
Your CEO brain calipers don’t need to be worth a shit in order for you to make money with them. If the market moves predictably every time you publicly announce your findings, that’s all you need.
The example in the article was a case where information was being hidden. There was a mismatch between content and tone "Everything's fine!" [I know everything is not fine]. Humans can already pick on a mismatch between content and tone. We call it intuition. The things that trigger intuition are quantifiable. Using Machine Learning to systematize things we already do is pretty sane and reasonable.
Next step: CEOs to stop attending quarterly earnings releases. IR meetings to become boring, with just the CFO reading numbers.
If I recall: Steve Jobs rarely attended IR events, Jeff Bezos just sent a letter in advance (note: beautifully written), but barely made live comments. Future guidandes are often released as `something between +30% increase or -30% decrease`, meaning useless.
My impression is that nowadays most PRs and releases are already meticulously reviewed. Quite an interesting paradox: The more regulations, rules, controls and surveys are introduced less detailed and exciting releases and disclosures are..
> Quite an interesting paradox: The more regulations, rules, controls and surveys are introduced less detailed and exciting releases and disclosures are..
Interesting example of what I see as counterintuitive behavior, which brings up a serious question.
If companies are prevented from making anything other than dry financial disclosures every quarter, how is a normal individual investor who is not an insider then supposed to judge investment opportunities?
Personally, I feel like the markets would operate better if companies, big shareholders, etc were always 100% free to make public comments without worrying about the SEC breathing down their neck micro-analyzing every statement. As an investor, I want to hear more from companies, not less.
One option is, ICOs -- they are not regulated by anyone and they seem to be making announcements all the time on Instagram with wads of cash -- I don't understand all this but somehow, cash and rappers are part of the "proof of consuming stakes" algorithm or something...
This reminds me of work done with the Enron Corpus. IIRC, it was used to cross reference statements made in court by the same people. When the emails revealed the lie made in court, there were often differences in linguistic structure between other statements. The example that sticks out in my head is that use of the passive voice was a key indicator of the likelihood of a statement being a lie.
Of course these insights are statistical in nature, not definitive.
No way this hedging tone was imperceptible to the human audience. All executives do when responding to forward-looking questions about earnings is to hedge.
Everyone has been using sentiment analysis for a while with any recorded call with executives. Frankly it’s often not exceptionally helpful and having longer duration knowledge about a company and its executives can be better for understanding when something in their posture changes. The fun alternative analyses I’ve heard of are when people start tracking executives’ travel to see who they’re meeting with.
Hey, we used to do that one, too! ACARS data to see which executives were golfing together. It's hard to figure out who is buying whom, though. It's more useful for pure entertainment. The only way I actually made returns from CEOs on airplanes was when one happened to sit next to me on a commercial flight and proceeded to edit a powerpoint with "BUY XYZ CORP FOR $M.N BILLION" in 100-point bold letters on his gigantic laptop.
Reminds me of countless cases where in preparation of an investors-call, the CEO had to be quickly briefed on the status of a suddenly important topic he wasn't following for the last quarter. If he's not familiar enough with a matter, I'm sure him talking about it will add noise to this AI analysis as well.
The fantastic aspects of such "solutions" for the stock-market is, that they're probably as reliable as most other solutions, since they all try to conclude something from incomplete and ambigous data-points.
Their purpose is probably not to be fully accurate, but just to give more comfort when taking the final buy/sell decision.
In other news, CEOs will use an AI to send press releases. We already have AIs writing articles anyway, so at some point it will become useless to check the news as its all gonna be corporate b$. What are we gonna do then? Rediscover local journalism?
Readit News
It isn't even a settled debate whether CEOs know what is going on or are a particularly important driver of outcomes. It is unlikely that NLP models can foresee the future, even if bankrolled by hedge funds.
It's pretty clear that a negligent/criminal CEO will lead to the bankruptcy of a company and a loss for investors. So yes, you can say at the extreme end that CEO's are drivers of important outcomes.
Now whether it's effective I think is another subject all-together. My own opinion is that micro-expressions, body language, etc. have been studied and used by the FBI/CIA for their field work. The idea being that once you establish a baseline behavior, you can notice "clusters" that deviate from the baseline based on verbal+non-verbal cues. So I don't see why an AI couldn't do the job.
There are many companies where it's likely that a negligent CEO (even a criminal one) wouldn't lead to the bankruptcy or even close. Many companies might be better off with a negligent CEO rather than one actually trying to do much. Right now Google could be run by a person on the beach sipping margaritas. Many other big public companies with very strong competitive positions probably could too.
I suppose you could have a portfolio of AI tools that would alternate between telling you “stonk go up” and “stonk go down”. Blame the customer for choosing the wrong one if they are unhappy.
I agree that this particular application seems... questionable, to say the least, but finance is probably the last place where data scientists need to work very hard to justify their efforts. Statistical modeling has been a core part investing for a very long time, and ML is just a subset of statistical modeling.
Of course we also did nlp to identify companies that were using language associated with negative returns in their quarterly conference calls. This was very successful, mostly by identifying pieces of shit we had not heard of yet. Once identified there were usually much stronger red flags. But the performance of that strategy was good. It definitely was not phrenology. Around 2001-2005.
If it works well enough, firms will put their CEOs speeches through models to ensure that their speeches rank well, and that will cause it to fail.
CEO ML team reverses model used by Investors and uses it to write own speech
what matters isnt what their intent is, what matters is how the greater fools will react.
as such, figuring out what the fools will do can lead to successful pumping and dumping, adding more liquidity skimming.
they did that successfully with trump
If I recall: Steve Jobs rarely attended IR events, Jeff Bezos just sent a letter in advance (note: beautifully written), but barely made live comments. Future guidandes are often released as `something between +30% increase or -30% decrease`, meaning useless.
My impression is that nowadays most PRs and releases are already meticulously reviewed. Quite an interesting paradox: The more regulations, rules, controls and surveys are introduced less detailed and exciting releases and disclosures are..
Interesting example of what I see as counterintuitive behavior, which brings up a serious question.
If companies are prevented from making anything other than dry financial disclosures every quarter, how is a normal individual investor who is not an insider then supposed to judge investment opportunities?
Personally, I feel like the markets would operate better if companies, big shareholders, etc were always 100% free to make public comments without worrying about the SEC breathing down their neck micro-analyzing every statement. As an investor, I want to hear more from companies, not less.
Of course these insights are statistical in nature, not definitive.
The fantastic aspects of such "solutions" for the stock-market is, that they're probably as reliable as most other solutions, since they all try to conclude something from incomplete and ambigous data-points.
Their purpose is probably not to be fully accurate, but just to give more comfort when taking the final buy/sell decision.
We're probably already in the first stages of that journey, complete with the unreliability and all.