Nate Silvers wrote an entire book on this subject called "The Signal and the Noise" [1]. Humans are so often taken in by people claiming to be able to make predictions by combining new data points. The more unusual, or unrelated to the subject matter, the better. They make good headlines but (not surpsingly) almost always turn out to be heavily flawed in practice.<p>You can basically measure how much a pundit/expert is going to be wrong in their predictions by how ideological they are in their analysis. The best indicator is when they use only one or two metrics as a basis of a prediction of an otherwise very complex scenario.<p>One example from the book is how a researcher became famous before the 2000 US presidential elections by claiming to predict races with 90% accuracy [2]. He claimed that by measuring a) per-capita disposable income combined with b) # of military causalities you can determine whether democrat or republicans get elected. He said historical data backs up his theory. He then proceeded to fail to predict that years election and faded into obscurity.<p>Nate did his own historical analysis and demonstrated it was only 60% accurate instead of 90%. Plus that was only if you ignore 3rd party candidates as the model assumes a two-party system.<p>Plenty of other examples are provided in the book which makes me highly suspicious of the value of the predictions made in this article.<p>The general idea is that we need to stop looking for simple one-off solutions to complex problems. Instead we should adopt multi-factor approaches which suffer from fewer biases and are better grounded in reality. Otherwise these predictions are just another form of anti-intellectualism.<p>[1] <a href="http://www.amazon.com/Signal-Noise-Many-Predictions-Fail--but/dp/0143125087/" rel="nofollow">http://www.amazon.com/Signal-Noise-Many-Predictions-Fail--bu...</a><p>[2] the "Bread and Peace" model by Douglas Hibbs of the University of Gothenberg <a href="http://query.nytimes.com/gst/fullpage.html?res=9803E5DD1F3DF937A35755C0A9679D8B63&pagewanted=all" rel="nofollow">http://query.nytimes.com/gst/fullpage.html?res=9803E5DD1F3DF...</a>
> CEOs whose faces during a media interview showed disgust [...] were associated with a 9.3% boost in overall profits in the following quarter.<p>I'm surprised I haven't seen anyone say "Regression to the mean" yet.<p>Suppose the CEO gets obviously-scowly whenever their last quarter was abnormally bad... Well, the next quarter will <i>naturally tend</i> to be better, purely because it's a return to a "normal" state of affairs.<p>In other words, perhaps they've simply found a way to detect the <i>PAST</i> performance by looking at the CEO's face, which is... rather less-useful.
Nice correlation study, but I'm really skeptical of any causation inference that might be possible. I mean, if the software gets to the point where it can identify the next Jeff Skilling[1] then great, but I doubt such surface level data has a lot of predictive potential.<p>I do find it kind of funny that the article cites the study mentioning 'negative' type emotional states aligned with ~9% profit boost, when one of the most interesting 'tells' in the Enron case was when Jeff Skilling got really bitchy at an analyst who was probing him hard on some difficult questions. The disgust was holding up a facade in that instance, and I don't doubt dishonesty might be a factor in the emotional state of others.<p>><i>“Fear is widely recognized as a powerful motivator. Thus it is not surprising to find that a CEO who appears fearful under interrogation is perceived by the market as a CEO who will work harder to increase firm value,” said the paper, which was co-authored by Steve Ferris of the University of Central Missouri and Ali Akansu and Yanjia Sun of New Jersey Institute of Technology.</i><p>This is a quite optimistic view of what one's behaviors might result in when driven by fear. I'm fairly confident fear of failure drives a lot of fraud. It sure seems a familiar story...<p>[1]<a href="https://en.wikipedia.org/wiki/Jeffrey_Skilling" rel="nofollow">https://en.wikipedia.org/wiki/Jeffrey_Skilling</a>
Create a hedge fund and run your proprietary algorithm. If you succeed over the long term and generate a consistent market premium for a given risk exposure, you've got a story. If you don't do this, you've got an unproven claim like many others in history, the vast majority of which were proven false when put into practice.
Paul Ekman talks about the "Desdemona Problem" (<a href="https://en.wikipedia.org/wiki/Othello_error" rel="nofollow">https://en.wikipedia.org/wiki/Othello_error</a>), which is always an issue with face-reading of emotions. Many facial expressions (especially those that express on the top half of the face) correlate very well with emotions. But you don't know the context of those emotions or what the person is thinking.<p>Hence the Desdemona Problem. She is fearful when accused by Othello, not because of infidelity, but because she's being accused. You see that already with the surprising finding that fear and disgust actually correlate with positive financial performance. Yet you see those same emotions in suicidal patients and they're undoubtedly negative.
The fact that the article presents different metrics in response to different inputs (CEO expressions of disgust correlated with 9.3% boost in overall profits over the following quarter, CEO expressions of fear correlated with 0.4% rise in stock price the following week) makes me strongly suspect excessive data-mining.<p>I haven't been able to find the actual paper in question; it looks like this is the abstract: <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2533615" rel="nofollow">http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2533615</a>. The lead author's page (<a href="https://web.njit.edu/~akansu/journal.htm" rel="nofollow">https://web.njit.edu/~akansu/journal.htm</a>) lists it as "Journal of Behavioral Finance, to appear, 2017."
:) I have to admit I haven't heard of a fund started to use this idea but I guess it was just a matter of time.<p>There are always funds you hear about that are created based on some previously unexplored data signal like this, twitter sentiment is an example that was popular circa 2011.<p>The problem that most of these signals has is that its really not a predictor on its own and it becomes just one of the 100's of signals that is consumable by financial models.<p>This means that you need to go through the trouble of collecting, cleaning, calibrating and discretizing this signal only to have it feed into a model where it might get a weighting of 0.5% of the overall signal.<p>> However, accuracy is an issue. Dr. Ekman claimed 90% accuracy for his emotion-coding system, but software inspired by his work hasn’t been tested independently.<p>This seems a bit dubious. Is this 90% accuracy for predicting stock movements? Or 90% accuracy for predicting emotions based on facial features? I doubt its the former or someone like two sigma would have just hired the author before he published. If its the later then its really unclear just how accurate their system is.
With all due respect, this sounds like data dredging mixed with a bit of astrology, filled out on in a PR release sent in by the authors and reprinted by the wsj.
I think picking up a micro-facial movements and body posture is where this software analysis can shine, the minute details that can indicate emotions.<p>Adding sentiment analysis, not just CEO facial analysis, is an interesting tool that can be used by traders / investors.<p>This is a short paper, may be interesting: <i>Trading Strategies to Exploit Blog and News Sentiment</i> - <a href="http://www3.cs.stonybrook.edu/~skiena/lydia/blogtrading.pdf" rel="nofollow">http://www3.cs.stonybrook.edu/~skiena/lydia/blogtrading.pdf</a><p>Also, Scutify, a financial social network, has a sentiment analysis of its members. - <a href="https://www.scutify.com/sentiment-rankings.html" rel="nofollow">https://www.scutify.com/sentiment-rankings.html</a>
And in the end, a drop one quarter can be followed by a rise the next. Hell, a company should be allowed to take a loss over a few years if it means they are working on something internally that will bring it back to profitability afterwards. But shareholders these days rarely have the icy stomachs for that kind of play.
I would be interested to know how he trained a fear detector.
There are a lot of companies that develop software that track facial expressions.<p>It is conceivable to use Affectiva's SDKs to automatically annotate data for facial expressions and then use that data to develop models that correlate facial expressions or facial expressions of emotions into things like performance prediction ...<p><a href="http://developer.affectiva.com/metrics/#facial-expressions" rel="nofollow">http://developer.affectiva.com/metrics/#facial-expressions</a>
"He's a narcissistic sociopath hoping no one exposes him and his web of lies"<p>"He's a narcissistic sociopath hoping no one exposes him and his web of lies"<p>"He's a narcissistic sociopath hoping no one exposes him and his web of lies"<p>"She's a narcissistic sociopath hoping no one exposes her and his web of lies"<p>"He's a narcissistic sociopath hoping no one exposes him and his web of lies"
If the founder and CEO sells all his stocks 20+% of "his" public company like some did the other Friday, you definitely can guess his emotion (eg NEWR). Then the other day, when the company stock drops to half the value, you can guess his emotion and different emotions from surprised stock owners.
The empirical data is weak. Stock prices change for a myriad reasons, CEO emotion and behavior being only one of them. Dr. Cicon's data may be sufficient for day traders but rigorous real world testing is needed to confirm if the tech is any good.
If this works on CEOs, then it would work on, say, the chairman of the Federal Reserve, or the heads of other central banks. Predictions gleaned from that data should be much more lucrative.<p>With higher stakes come greater incentives for counter-measures.
From now onwards we have to force all CEOs be autistic incapable of expressing emotions, or they need to be replaced by Geminoids built in their own image.