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Author Topic: Price vs Media Attention  (Read 727 times)
BTCThousandaire (OP)
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June 17, 2013, 04:07:15 AM
Last edit: June 17, 2013, 04:28:47 AM by BTCThousandaire
 #1

I think price is predicted by media attention based on audience size. Remember when it was on the good wife? The price anticipated that move and then it took about 6 months for the price to stay about the same before it rose again.

The main media coverage with the biggest audience for the last rally seemed to be Alex Jones and Max Keiser with some other news coverage mixed in. But it may have been based on anticipated future price too early. So October, which is 6 months from April, may be the time required for "digestion" of the mass media coverage leading to a stable rise at that point.

It would be great if someone could chart media coverage based on audience size over time.
Any further thoughts on the largest audiences in some of the recent price rises?  I would be interested in the beginning of the rise in late 2010.
(So I found that a Slashdot post in July 2010 seems to reach the first major audience and price was flat for 3 months then took off.)

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June 17, 2013, 09:06:21 AM
 #2

not linked ...
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June 17, 2013, 10:04:10 AM
 #3

It is linked, partially.

[media exposure] * [potential audience size] * [conversion rate]

Will give you new users as a function of media exposure.

However, new users are only one factor in price rises, (albeit an important one). You also need to model the network effects of current users and their behaviour.

It would be interesting to try to build a model of the Bitcoin market.... In modelling the market, however, I don't think it is right to treat each user equally.

I'd come up with a number of possible personas (e.g. geek evangelist; family investor; risk-taking big investor; etc), estimate their overall contribution to the market, and model their behaviour and network effects appropriately.

It's quite a big task and would take a lot of refinement (and a big computer to run the model on until you could get it to match historical data), but could potentially yield some very interesting analysis.


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