# Tag Info

## Hot answers tagged statistical-finance

4

Keep in mind that Benford's law is not a universal or natural law. A violation of Benford's law is neither a necessary nor a sufficient condition to prove a flaw or a quality issue in the data. At the best, it can give you a hint, but it should not be trusted blindly. Moreover, note that for some types of data the law will not work at all, such as e.g Likert ...

2

You should de-trend to whatever frequency scale you are testing. I.e. 1 min means de-trend 1 min data. Merely by moving to higher frequency data, you are eliminating much of the systematic bias present at higher scales -- as 1) you have many more samples to compare (minimizing standard error) 2) At smaller intervals, the drift component also shrinks ...

2

I have not used random forests myself but I know of a guy who applied this classification technique to machine learning algorithms applied to pattern recognition. Thus I think its advantages over classic regression approaches can be applied to discern patterns in financial data, though I get the impression that it vastly overfits the data and thus you end ...

1

I agree with @MattWolf The graph you show is confusing and evil, it makes me feel dumb every time I look at it. So I inverted the axis. Now we see the familiar shape of an utility curve, discussed in your previous question. It is upward sloping at a declining rate. In this case $u$ takes the place of $R_p$ and the general form of mean variance utility is ...

1

In answer to your question 2, you should detrend over the entire range of the back test period. The purpose of the detrending is to satisfy/create the null hypothesis for the boot strap test (it's not strictly necessary for the permutation test). This hypothesis is that the return from your strategy is zero. To create this zero null hypthesis you have to 1) ...

1

I think the simplest way to achieve what you're looking for is through regression coefficient hypothesis testing. Perform linear regression on returns (y-axis) vs. dates (x-axis) over the desired time frames (do it once for 5 months, once for dataset w/15 months worth of data, and once for 60 months worth of data). As a result of regression, you will get ...

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