
Twitter Trends Beat Analysts In Predicting Wall Street - ColinWright
http://www.escapistmagazine.com/news/view/112932-Twitter-Trends-Beat-Analysts-In-Predicting-Wall-Street
======
hugh3
_Following its first month of public trading, a new Derwent fund based on
Twitter reported 1.85 percent return on investment. During this same time
period, the S &P 500 index fell 2.2 percent, "and the average hedge fund made
only 0.76 per cent."_

An algorithm that outperforms the stock market for one month is not news. If
they have an algorithm that can do it consistently over decades I'll be
impressed.

Many funds have been trying to mine this sort of data, and presumably we don't
hear about it when the results aren't so good, so this is just cherrypicking.

I can train ten chimps to pick stocks, and the best-performing of them over
the first month will no doubt outperform the market significantly. But if I
try to sell you my stock-picking chimp then beware.

~~~
gammarator
Here's an extensive takedown of the paper; it has lots of flaws:
[http://blog.someben.com/2011/05/sour-grapes-seven-reasons-
wh...](http://blog.someben.com/2011/05/sour-grapes-seven-reasons-why-that-
twitter-prediction-model-is-cooked/)

------
olihb
I'm sure that plenty of hedge funds were using these feeds (blogs, twitter,
etc.) for years.

They just kept their mouth shut because it's foolish to broadcast that you
have an edge on your competitors... I bet that since it's out in the open,
it's now obsolete...

~~~
hugh3
I heard about a startup which was doing this a couple of years ago via the VC
who had invested in it. It's no big secret.

~~~
olihb
I heard about that on a forum a while ago, but my point was that when you find
a novel way to profit from the stock market you keep your mouth shut.

------
iam
This reminds me of the movie Limitless, where the protagonist devised computer
algorithms that would scan forums and other social media sites to get people's
sentiments of what would happen to the stock, then successfully made 40x+
returns within months.

------
jaredmck
Small sample size.

