Twitter Mood Predicts The Stock Market
technologyreview.com
technologyreview.com
To assess the statistical significance of the SOFNN achieving
the above mentioned accuracy of 87.6% in predicting the up and down
movement of the DJIA we calculate the odds of this result occurring
by chance. The binomial distribution in- dicates that the probability
of achieving exactly 87.6% correct guesses over 15 trials (20 days
minus weekends) with a 50% chance of success on each single trial
equals 0.32%. Taken over the entire length of our data set (February
28 to December 20, excluding weekends) we find approximately 10.9
of such 20 day periods. The odds that the mentioned probability
would hold by chance for a random period of 20 days within that
period is then estimated to be 1−(1−0.0032)10.9 = 0.0343 or
3.4%. The SOFNN direction accuracy is thus most likely not the result
of chance nor our selecting a specifically favorable test period.
I'm not sure about the result not being chance. In particular, aren't they privileging the hypothesis by using the probability of 87.6% guesses being correct? They say the result only had a 3.4% chance of happening, yet we could assign a similar probability to a wide variety of other seemingly unlikely outcomes. I'd much rather have seen the predictor tuned on a subset of the data and then tested on the rest of the data - perhaps the authors will release their predictor and we can try it on 2009.Given that twitter and the stock market are both correlated to what is happening in the world (in fact, caused by what is happening in the world,) it is not surprising that they are correlated with each other. Somehow I don't think correlation in the other direction would get the upvotes though - stock market crash predicts sadness on twitter? You don't say.
In short, I don't follow all of their statistics, but I am highly dubious of their model having any predictive power.
February 28, 2008 to November 28, 2008 is
chosen as the longest possible training period while
Dec 1 to Dec 19, 2008 was chosen as the test period
That said, their split exhibits enormous bias: Dec 1 to Dec 19, 2008 was chosen as the test period
because it was characterized by stabilization of DJIA
values after considerable volatility in previous months
and the absence of any unusual or significant socio
cultural events
I don't understand why they would think this is okay.Perfectly efficient markets. Which, as a theory, has been under rather serious siege for the better part of the last decade.
You can certainly make a behavioral argument that would reach more or less the same conclusion, however.
Since they got great results, it'll be interesting to see them extend the analysis and see if any interesting dynamics show up.
Can't we somehow look and see if the author's lifestyle suddenly became more lavish. That should be a good predictor of stock market success, but then ...
Now every douchenozzle on earth will be onboard the bandwagon, and render it useless.
EVERY TIME I HAVE A GOOD IDEA SOMEONE BEATS ME TO IT! WHARBLEGLARBLE!
HULK SMASH! BAD SCIENTISTS! BAD!
Because you can still beat them to that.
Now I have zero time to build capital before it's leveraged away. FUDGE!
I am righteously pissed off now. I had investors on the hook. now it will all be arbitraged away from me.
It's just like quicksilverscreen when Rupert Murdoch litigated it away (one of the senators who voted for the DMCA was one of the lawyers defending Fox).
I am ANGRY.
What if you were sleeping in someone else's garage, it was less than freezing temperature outside, someone had stolen your car a few weeks ago, and you had put your own kneecaps on the line as collateral? What would it be then?
Frankly, I think I'm holding together rather well given my circumstances.
i GUARANTEE you that people are already trading based on twitter data. i doubt you're really as bad off as you think.
..or maybe you are. yes. actually, your position is hopeless. might as well go open source with it now. sourceforge link pls?
Interpreting mood trends from Twitter data assumes some very advanced semantic and linguistic analysis of messages, which even my human brain fails to understand about 25% of the time.
A good way to test it would be to see whether the accuracy of the twitter indicator varies based on types of market activities, such as: does it completely fail on crashes, but always predict run-ups?
Does twitter's API offer the ability to see all incoming tweets? Glancing at their API documentation seems to suggest that they turned this ability off a while ago.
Third, these results are strongly indicative of a predictive correlation between measurements of the public mood states from Twitter feeds, but offer no information on the causative mechanisms that may connect public mood states with DJIA values in this manner.