Well, the costs add up. You have to process that data as well afterwards. And all those resources are being diverted away from collecting other data and making longer term predictions. Multiply that by the millions who could be trading but don't (a few hundred dollars a month is a barrier to a lot of people, that might have been the profit of a small strategy that worked).
First of all, you haven't yet told me why high frequency trading is driving up data costs.
Secondly, if $100/month is stopping your business from being profitable, that's a fault with your business model, nothing more. It would cost more to get a medium size Windows instance on Amacon EC2 for the month.
>Multiply that by the millions who could be trading but don't (a few hundred dollars a month is a barrier to a lot of people, that might have been the profit of a small strategy that worked).
I don't think you can claim that millions of people are being locked out of the market because of data costs.
If there's more data, then it's more costly to deal with it - simple as that.
What if there's a little appliance that you plug in which makes predictive models (you can think of it as installing software on your laptop). What if millions of people want this appliance but it costs a $100 a month but could have made about $100 dollars a month? They will choose not to invest.
This might seem contrived, but it's also the scenario behind the web (lot less blogs when it cost $100 per month).
The net effect is that less predictive models of the economy are created. This is bad because that's how capitalism allocates resources.
The larger framework is that competition increases quality. Any barrier to business decreases competition.
>If there's more data, then it's more costly to deal with it - simple as that.
You can still trade on daily bars. Anyone can. Whether you sum up a days worth of data into a daily bar, or you have an auction once a day you're still going to have the same sized data set.
>The net effect is that less predictive models of the economy are created.
No it's not. $100/month is a reasonable cost. I don't know how to make this clearer to you. Data costs are among the CHEAPEST part of the equation when you're building a financial model. Quantitative analysts are paid six figures. Skilled programmers are paid on the order of six figures. Getting data costs down to $100 is not going to make someone go "Oh you know what? I'm ready to put in 100 hour weeks developing financial models because I can now afford a bus pass".
The examples you're giving sound ridiculous because they are ridiculous, and so is the premise you're basing them upon. It's like me telling you that I want to become a programmer but a $100 laptop cuts into my expenses too much.
>The larger framework is that competition increases quality. Any barrier to business decreases competition.
$100 will not increase competition. I can't put it any more plainly than that.
Well, if you want to brush everything off like that (including how you make predictive model from daily data when there's so much volatility when you actually want buy/sell) then you're clearly trying to win some type of "argument". Have at it boss.
>Well, if you want to brush everything off like that (including how you make predictive model from daily data when there's so much volatility when you actually want buy/sell) then you're clearly trying to win some type of "argument". Have at it boss.
Trade on opens. Occasionally there are gaps, but they are traditionally due to big news, almost always related to fundamentals. If you're modelling the stock market as a random walk, then you're just as likely to have volatility go for you as against you under normal market conditions.
