Where do you begin with this ? Do you postulate the hypothesis that chaos is an integral element in prediction ? And if so, what is the root core of this hypothesis, if so ?
To rearrange the order as I think it makes more sense.... this (chaos) isn't hypothesis, it's a theory (there's a significant difference between these terms) that is wildely applied in other systems to explain 'what happens'. The history of it I'm only on nodding terms with, but one part of it is this 'linear v non-linear' question. I'm more familiar with it in a physics setting, I know that there are equations that I can use to decide what a system will do for any given inputs - for example I can take a known spring and calculate how hard it will pull for any given extension... knowing the start conditions I can confidently and accurately predict the outcome, perhaps more importantly though if I'm slightly out on my input measuring I will find my result is only slightly different to what I predicted. The spring might pull a little harder, but not outrageously so.
If I make a double jointed pendulum arm (I saw this demonstrated recently) I have a non-linear system - releasing the arm from one point causes the arm to gyrate as it spins slowly to a halt... put the arm to what you believe to be the same point and a radically different set of gyrations results. A tiny change in input causes a radical change in output.
Non-linear systems exhibit this behaviour - significant changes in results despite the input conditions being virtually identical.
Anyone trying to predict the market via computation is going to run their choice of inputs into som sort of algorithm. The market is non-linear - it's not a simple graph, showing regular repetition, so I'd be pushed to accept it is linear. So assuming you got your algorithm/equation right for calculating the future price you still have a significant problem ahead of you... slight imperfection in the input data will cause the prediction to bounce all over the place. (This will look random, but it isn't - it's entirely predicted by your formula).
This deviation from what actually occurs will become worse at an accelerating rate as time passes - longer predictions are far less likely to be accurate than short term ones. They simply can't help but do so - all non-linear systems share this 'flaw'.
My input here on this subject is because I know that many people (including myself) try to use a PC to analyse markets using all sorts of ideas. While we are vaguely aware of limitations in our software and perhaps have an idea that it's better in some cases than others, we are often unaware there's actually information 'out there' in non-finance land that can inform and guide our program development and expectations. Some of our problems have been solved in other fields already.
If you accept Chaos has application to the markets (and it's applicable to just about everything else in creation, so it would be surprising to be able to say it isn't in this one special case), then it follows that prediction via an equation is going to be problematical. However, if you don't look too far ahead then the number of possible outcomes diminishes - you might end up with say 4 target prices for the Dow at 250 point intervals and 'commonsense' tells you which one to go with.... so the problems aren't necessarily insurmountable.
Where do you begin - the $64m question! Personally I think your best bet would be to arrange a distributed computing system of like minded individuals looking to share the outcome. (No point doing all this in public, solving the conundrum and failing to make a profit thereby <g>). I'd then look at deciding possible inputs, then simply (!) run as many combinations of them through a number crunch as possible, comparing the output prediction to reality.... ie hit and miss looking for the equation. There are probably better ways, and you might be able to reduce the number of tests by eliminating some obvious no-hopers. A bit like Omnitrader's approach, in a way, but you are looking for the parameters in your equation.
Dave