That would not invalidate an underpinning mathematical theory.
For example, one of the pillars of modern Physics is the covariance of theories under certain group actions. But what may be to particular to a given model, such as initial and boundary conditions, usually breaks the symmetry of the theory.
The symmetry group of the observed data is therefore usually MUCH SMALLER than the covariance group of the theory. And that is in Physics, let alone Phynance!!
Even if you were correct that the markets are manipulated in such a way as to screw the most number of participants, the "screw as many market participants as possible" [from now on known as SAMMPAP] may be a covariant theory to a well known mathematical theory of markets, for example the sited one above that markets are multifractals (which is very likely to be correct at least as an approximation or pertubation of the basic model - i.e., Log-Levy distributions and their relation to multi-fractals, etc.)
The problem is not the mathematics, it is that the evolution equations or even the theory governing the dynamics are mostly unknown in the financial markets/economics - all we can see is noisy data. If we understood
exactly how SAMMPAP tries to screw over most people and could write it down as some sort of statistical equation and it's governed dynamics, we would be able to tell many things with a great deal of statistical accurary (in the Log-Levy sense.)
BTW,
There is no general analytic solution for the form of p(x) for Levy Distributions. There are, however three special cases which can be analytically expressed as can be seen by inspection of it's characteristic function.
* For α = 2 the distribution reduces to a Gaussian distribution with variance σ2 = 2c2 and mean μ and the skewness parameter β has no effect.
* For α = 1 and β = 0 the distribution reduces to a Cauchy distribution with scale parameter c and shift parameter μ.
* For α = 1 / 2 and β = 1 the distribution reduces to a Lévy distribution with scale parameter c and shift parameter μ.
Other special cases are:
* In the limit as c approaches zero or as α approaches zero the distribution will approach a Dirac delta function δ(x − μ
.
http://en.wikipedia.org/wiki/Levy_skew_alpha-stable_distribution
I have been able with some success able to identify [some of the time] when the markets are in "α = 2" above and take advantage of it. I have been able to do this by noticing a "symmetry group of the observed data," but it is really hilarious, it even "knows" how to screw those that know they are getting screwed!!!!!! LMAO. The screwing is fractal too!
. I am working on the the other three cases...
nitro
Quote from hypostomus:
...manipulations for the purpose of convincing traders that the positions they hold are wrong. So there, I said it, I am paranoid.
