
Software “detects CEO emotions, predicts financial performance” - phabian
http://blogs.wsj.com/digits/2016/02/17/software-detects-ceo-emotions-predicts-financial-performance/?mod=trending_now_5
======
dmix
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.

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.

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.

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.

Plenty of other examples are provided in the book which makes me highly
suspicious of the value of the predictions made in this article.

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.

[1] [http://www.amazon.com/Signal-Noise-Many-Predictions-Fail--
bu...](http://www.amazon.com/Signal-Noise-Many-Predictions-Fail--
but/dp/0143125087/)

[2] the "Bread and Peace" model by Douglas Hibbs of the University of
Gothenberg
[http://query.nytimes.com/gst/fullpage.html?res=9803E5DD1F3DF...](http://query.nytimes.com/gst/fullpage.html?res=9803E5DD1F3DF937A35755C0A9679D8B63&pagewanted=all)

~~~
hammock
The law of averages will tell you that the more metrics (however random) you
throw into your prediction engine, the closer your prediction will be to the
actual result. But it's not very remarkable and it will never put you ahead
consistently.

------
Terr_
> CEOs whose faces during a media interview showed disgust [...] were
> associated with a 9.3% boost in overall profits in the following quarter.

I'm surprised I haven't seen anyone say "Regression to the mean" yet.

Suppose the CEO gets obviously-scowly whenever their last quarter was
abnormally bad... Well, the next quarter will _naturally tend_ to be better,
purely because it's a return to a "normal" state of affairs.

In other words, perhaps they've simply found a way to detect the _PAST_
performance by looking at the CEO's face, which is... rather less-useful.

~~~
ucha
I have never heard of regression towards the mean in profits. It certainly
doesn't exist for stock prices. You would actually tend to observe trends -
the opposite phenomenon. For example, Google would have a 20% increase in
profits on one quarter and another similar increase the following quarter but
not a sudden loss caused by a regression to the mean.

~~~
lamontcg
In this case, "regression to the mean" is probably the wrong phrase.

Generally, though, when a CEO is looking stern and fearful and declaring
writedowns and layoffs and erasing the 'goodwill' off of their books one
quarter along with huge losses and financial penalties, etc then the next
quarter usually isn't quite as big of a shitshow...

So the point here would be to buy a stock that has been overly punished and
become unfashionable, while the overall business is still sound and will
eventually rebound and the stock price should perk up.

If true, though, reading the negative emotions of the CEO would be correlated
with past performance and it wouldn't be useful to determine if the company
really was sound or if the company was actually heading to zero.

------
6stringmerc
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.

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.

> _“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._

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...

[1][https://en.wikipedia.org/wiki/Jeffrey_Skilling](https://en.wikipedia.org/wiki/Jeffrey_Skilling)

~~~
solipsism
It doesn't seem to me that whether there can be shown to be a causal
relationship between this data and future performance would have any bearing
on whether the relationship has predictive power.

------
IkmoIkmo
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.

~~~
cylinder
You don't need to run a hedge fund to perform research. What a horrible
mindset you have.

~~~
chocolatebunny
The problem with pure research in this field is that your decisions will
influence the market. How much you invest will influence your returns and how
successful you are will influence the behaviour of others in the future.

~~~
lazaroclapp
Which is actually why you shouldn't invest based on this algorithm if you care
about the research. I think, ideally, you should:

1) Generate a few hypothesis algorithms, including one that invests at random.

2) Publish a cryptographic commitment for each algorithm.

3) Never actually invest any money. Alternatively: let someone else invest
your money for you, without knowledge of your hypotheses.

4) Run your algorithms privately, without updating them at all. Capture the
data the algorithms use (including random choices taken).

5) 5, 10 or 20 years later, publish all your algorithms, the data they had as
input and their results, see if any of them would have predicted the actual
performance of the market in an statistically meaningful way.

I imagine the main reason most researchers are unlikely to do that is the 5-20
years project requirement. It is a lot easier and faster to just take
historical data from the market and then produce algorithms that would have
predicted performance after year X, based on information before year X. Of
course, the problem is that you run into over-fitting and survivor bias (in
that only positive results are generally published).

Btw, having your algorithm be run by a fund and having that fund succeed, then
publishing the algorithm, would also be susceptible to survivor bias.

~~~
jabgrabdthrow
This only works under the assumption that your activity would have had
absolutely no impact on the market. The real world is complex and
interconnected, and everyone is monitoring each other closely.

------
arafa
Paul Ekman talks about the "Desdemona Problem"
([https://en.wikipedia.org/wiki/Othello_error](https://en.wikipedia.org/wiki/Othello_error)),
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.

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.

------
jleader
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.

I haven't been able to find the actual paper in question; it looks like this
is the abstract:
[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2533615](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2533615).
The lead author's page
([https://web.njit.edu/~akansu/journal.htm](https://web.njit.edu/~akansu/journal.htm))
lists it as "Journal of Behavioral Finance, to appear, 2017."

------
chollida1
:) 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.

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.

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.

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.

> 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.

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.

~~~
harryjo
Whenever someone claims a one-dimensional measure of "accuracy", you can know
they are lying. They cherry-pick one detail in their pipeline, not report the
overall lift in performance vs other known methods of predicting the important
variable.

------
ACow_Adonis
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.

~~~
c-slice
^ Best comment of the day ^

------
dpflan
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.

Adding sentiment analysis, not just CEO facial analysis, is an interesting
tool that can be used by traders / investors.

This is a short paper, may be interesting: _Trading Strategies to Exploit Blog
and News Sentiment_ \-
[http://www3.cs.stonybrook.edu/~skiena/lydia/blogtrading.pdf](http://www3.cs.stonybrook.edu/~skiena/lydia/blogtrading.pdf)

Also, Scutify, a financial social network, has a sentiment analysis of its
members. - [https://www.scutify.com/sentiment-
rankings.html](https://www.scutify.com/sentiment-rankings.html)

~~~
awqrre
Maybe not as useful but you can also detect pulse using a simple webcam and
some software. That combined with speech might give some more insight.

[http://www.extremetech.com/extreme/149623-mit-releases-
open-...](http://www.extremetech.com/extreme/149623-mit-releases-open-source-
software-that-reveals-invisible-motion-and-detail-in-video)

[https://www.youtube.com/watch?v=3rWycBEHn3s](https://www.youtube.com/watch?v=3rWycBEHn3s)

------
digi_owl
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.

------
ahamino
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.

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 ...

[http://developer.affectiva.com/metrics/#facial-
expressions](http://developer.affectiva.com/metrics/#facial-expressions)

------
toomuchtodo
Software-defined "Lie to Me" [1] Awesome!

[1]
[https://en.wikipedia.org/wiki/Lie_to_Me](https://en.wikipedia.org/wiki/Lie_to_Me)

------
cowardlydragon
"He's a narcissistic sociopath hoping no one exposes him and his web of lies"

"He's a narcissistic sociopath hoping no one exposes him and his web of lies"

"He's a narcissistic sociopath hoping no one exposes him and his web of lies"

"She's a narcissistic sociopath hoping no one exposes her and his web of lies"

"He's a narcissistic sociopath hoping no one exposes him and his web of lies"

------
frik
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.

------
pmille5
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.

------
winestock
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.

With higher stakes come greater incentives for counter-measures.

------
wnevets
does this work with sociopaths?

------
plasticxme
I envision a future where CEOs don Guy Fawkes masks before doing press
releases.

~~~
paulmd
And damage their branding? Nah, they'll do motion capture and render their
mascots doing it.

Picture Ruby giving AMD's presser.

~~~
AstralStorm
Or the timeless trick of employing a body double.

~~~
pc86
Who was just told (perhaps falsely) that we beat expectations!

------
mvidal01
Maybe Psychopaths CEO's will be even more common in the future.

~~~
sremani
or Stoic CEOs.

~~~
toomuchtodo
Or Honest CEOs?

------
bitL
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.

------
newsignup
There was an article some time back saying CEOs are basically psychopaths,
then this software should not work that well.

------
conjectures
Perhaps they could also incorporate data on CEO horoscopes, and which coffee
outlets they frequent.

------
juskrey
BS

~~~
gruez
care to elaborate?

