[R-SIG-Finance] Strategies based on Neural Networks (or SVMs) - any experience with R ?

[Patrick Burns]

As I learned last week at useR,
logistic regression might not be
the statistician's favorite for
much longer: beta regression does
the same thing, but better.  It can
get the heteroscedasticity more
accurately.

On 23/08/2011 04:12, Stephen Choularton wrote:
> I think that Gentil should be aware of the /No Free Lunch Theorem/ (Duda
> et al., 2001, Wolpert and Macready, 1997). There are no
> context-independent or usage-independent reasons to favor one machine
> learning algorithm over another. If one performs better than another, it
> is owing to its better fit to the particular problem, not its general
> superiority. If you wish to use these techniques try lots of them:
> certainly neural networks and support vector machines, but also try some
> of the ensemble techniques such as bagging, boosting and random forest.
> You can even try the statisticians favorite, logistic regression. They
> are all available in R.
>
> Stephen Choularton Ph.D., FIoD
>
>
> On 23/08/2011 12:14 AM, Brian G. Peterson wrote:
>> On Mon, 2011-08-22 at 10:11 +0200, Gentil Homme wrote:
>>> I just send out this post in order to share within r-sig-finance any
>>> possible experience, advice, ... about NNs or SVMs with R.
>> It seems that you're asking us to share with you, and not sharing much
>> yourself in return.
>>
>> Perhaps you could answer your own questions in this thread with the
>> things you are trying?
>>
>> SVM's have been discussed on this list many times, please search the
>> list archives.
>>
>> This blog has covered this topic:
>> http://www.aphysicistinwallstreet.com/
>>
>> Also, there are a few books on machine learning that use R.
>>
>>> Several good records have been published in the litterature using these
>>> techniques for financial trading strategies.
>> Which ones? References?
>>
>>> There are also commercial packages (expensive !) which seem to have
>>> achieved
>>> good results.
>> Which packages? References again?
>>
>> Note that neural network strategies are very likely to create look-ahead
>> bias as you develop and test them. You try something, fail, and try
>> again on the same data. Unless you are very careful to reserve a 'pure'
>> set of instruments and dates that you won't *ever* touch until you think
>> you have a 'good' machine learning system, you're at pretty serious risk
>> of introducing your look-ahead knowledge into the system. While this is
>> true to one degree or another in any quantitative strategy development,
>> I think it is a particular risk in self-adaptive machine learning
>> methodologies.
>>
>>
>>> So I feel it could be nice to share within this group about the
>>> following
>>> subjects :
>>>
>>> - experience using the R packages
>>> - data pre-processing before feeding the NNs (technical indicators,
>>> wavelets, EMDs, ....)
>>> - which type of NNs are suitable
>>> - how to build and train them
>>> - etc ...
>>>
>>> Thanks to all for sharing within the R community
>> Now, your turn. Bring the community up to date with your research so
>> far.
>>
>> Regards,
>>
>> - Brian
>>
>
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-- 
Patrick Burns
patrick at burns-stat.com
http://www.burns-stat.com
http://www.portfolioprobe.com/blog
twitter: @portfolioprobe