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Old 04-12-2013, 06:31 AM
Elroch Elroch is offline
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Posts: 143
Default Re: Lecture 3 Q&A independence of parameter inputs

Originally Posted by Moobb View Post
Elroch, thank you so much for your help.
Thank you. But bear in mind there is much I do not yet know!
Regarding your point about non stationarity and the difficulty that introduces for financial forecasting, do you see that as necessarily invalidating any attempt towards machine learning forecasting in Finace?
No. The evidence seems pretty clear that it is not entirely hopeless, but a tough struggle.
Could it be that the time series itself is non stationary, but some specific patterns within it (which people try to capture with technical indicators for example) are stable? Those technical indicators would than be your features and maybe when we conditional on them your time series become more stationary? I think another main use in Finance is in terms of classification, which can then be used for portfolio allocation for example.
Many thanks again!!
I basically agree with that, except that I can't see a reason why any structure is likely to be entirely stable. You can always calculate a technical indicator (typically a real valued, non-linear transformation of past prices), but the idea that probabilistic statements based on one or many such indicators will remain permanently true seems implausible: at the very least the probabilities are going to change over time.

Regarding non-stationarity, there is an awkward conflict between the wish to have plenty of training data and the wish to have training data that is recent enough not to be misleading. One paper I read found an interesting way of dealing with this by weighting more recent data more strongly when training. [If this doesn't display in your browser, try saving it and loading in a PDF viewer] Unfortunately, I am not yet aware of how to do this without modifying or rewriting general purpose machine learning tools.

Fortunately discovering eternal truths about markets (or other time series) is not necessary, since there are two things you can do. One is to execute the training process at intervals, and a more radical solution is to replace your approach by a more sophisticated one if it stops being effective enough (for example, you might start with just moving averages as features, and it might work. If it stopped working you might add a Hurst exponent calculated at an appropriate scale, as a complex feature that might make machine learning more feasible, if you describe the problem in the right way. I don't know if this is true, but it may be. ). The possibilities are infinite, and I sometimes think that is more of a problem than a help!

And yes, classification is surely as useful as prediction of real valued quantities. I like to think of it in information terms. A binary classification is a prediction of 1 bit of information. There are a huge range of possible bits you might choose to model. A real-valued prediction can be approximated by a classification problem where you have a sequence of bins corresponding to intervals. I am not sure of the relative merits when there is a choice. One issue is how the information being modelled relates to the way it will be used later. Of course any output can be considered as an indicator itself, but then there is the question of how that output will be used in trading and how it will affect trading results. In principle, error measures should be tailored to suit the effect on results, but this may not be easy.
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