Tag Info

Hot answers tagged

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 ...


3

2) Alternative to Fama-MacBeth is Fama-French approach. Explanation of difference see, for example, here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1271935 Fama-French approach was used by Carhart (introduced momentum), Pastor-Stambaugh (introduced liquidity), Fama-French themselves (used it to build 5-factor model), and many other (elsevier or ...


3

As with many machine learning technologies, you can run a separate training and testing phase before deploying it live for prediction. All it does is build a collection of decision trees based on the parameters you give it - if the output field is a factor, you get classification (a finite enumerated set of values); if it's numeric, you get prediction. One ...


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 ...


2

PX_BID and PX_ASK are the static equivalents of BID and ASK, the latter two of which populate in "real time" (i.e. as they are dynamically updated). So the PX_BID and PX_ASK values are dependent upon when you pulled the data. Bloomberg's source depends on the asset in question and the exchange on which they are listed, but the data does come from the ...


2

The key assumption is that there is no time-series correlation between the error terms. Fama-MacBeth can deal with cross-sectional correlations. See Samuel Thompson's "Simple formulas for standard errors that cluster by both firm and time" in the Journal of Financial Economics (2011) for a treatment of different regression methods for testing equity ...


1

They represent the current BID and ASK at the time you query them. If you look up those fields in the terminal FLDS<GO> you will see they are marked as reference data, that means they are not continually updated. They are refreshed each time you query them. They come from the NBBO quote at the time you query them.


1

Recently I found a book on earnings trading but did not have time to read thoroughly. Trading on Corporate Earnings News - John Shon I also had spent some time to see earnings surprise effects and it is a quite interesting but not easy to use topic. There is certainly a jump if the estimates and announced earnings have a large mismatch but the magnitude ...


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

It could help with things like fraud detection, analysis of bankruptcy probability, default risk, unsupervised learning for qualitative/descriptive purposes, or for a purely backwards looking supervised analysis on returns again for descriptive/understanding purposes (variable important, etc, perhaps impulse response analysis). It may also be good at ...


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 ...



Only top voted, non community-wiki answers of a minimum length are eligible