From theory to practice - page 1025
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when increasing the sample is random, 50/50
nothing is confused in my mind. It is possible to make money in a situation, but as the horizon increases, profits will fall proportionally and tend to zero
proven by Nobel.
1. you cheated - it's not proven by Nobels;
2. well, if you use random prediction models, with increasing sampling the probability of winning does approach 50% - if you open lots with a coin, the short term profit will tend to zero.
And if you don't use random ones, you don't.
Open the returns of the Soros investment fund for -teen years - what's not a horizon for you?
P.S. R^2 is the coefficient of determination.The proportionof variance of the dependent variable explained by the dependencymodel in question. Yoklmn....
1. you're cheating - no Nobels proved that;
2. well, if you use random prediction models, then as the sample increases the probability of winning does approach 50% - if you open lots with a coin, the short term profit will tend towards zero.
And if you don't use random ones, you don't.
Open the returns of the Soros investment fund for -teen years - what's not a horizon?
don't count insiders and crooks.
And do not take into account those who can play on temporary inefficiencies and stop in time.
I know what r^2 is, it's a bit of a stretch.
the efficient market theory has not been disproved by anyone
insiders and fraudsters are not taken into account
Nor do we take into account those who can play on temporary inefficiencies and stop in time
I know what r^2 is, dragged by the ears hereHere we go.
The coefficient of determination compares the "predictive" qualities of two or more models for the same process (well, given an equal number of variables).
It shows how much a given set of variables and model explain the behaviour of the dependent variable (process).
P.S. Everyone... and I'm Dartagnan...
There is no theory of ER.
there is an ER hypothesis that has not been proven by anyone.
Here we go.
The coefficient of determination compares the "predictive" qualities of two or more models for the same process (well, for an equal number of variables).
It shows how much a given set of variables and model explain the behaviour of the dependent variable (process).
P.S. Everyone... and I'm Dartagnan...
you're as dumb as a log... it's a statistical variable that tells you nothing about the future
you're as dumb as a log... it's a statistic that tells you nothing about the future
Well, forgive us all, but only God knows about the FUTURE!
And ALL statistics and TV and ALL science is based on statistical values AND NO OTHER HUMANITY HAS SEEN ANYTHING else.
Well, forgive us all, but only God knows about the FUTURE!
And all statistics and TV and all science is based on statistical values and mankind has not yet invented anything else.
So what next? Why did you have to cite this as an example. History can be fitted with any degree of accuracy
and then what? Why was that brought up as an example? History can be fitted with any degree of accuracy
The measure of randomness of a process is a function of the particular predictive model.
One process can be predicted by a model with R^2=0.82 and another with R^2=0.42.
So how random is the process?
Of course, no statistical model will give a 100% guarantee that the parameters will hold in the future, but they are applicable for a rough estimate
The measure of process randomness is a function of the particular predictive model.
One process can be predicted by a model with R^2=0.82 and another with R^2=0.42.
So how random is the process?
Certainly, no statistical model will give a 100% guarantee of preservation of parameters in the future, but for an approximate estimate they are applicable
Tell me more about the applicability of OLS as a measure of error in estimating future events
The man has wasted several years on nets and forests, and the end gives birth to the idea that statistical estimates mean nothing....
And the dumb one of the two of us is me!