[R-SIG-Finance] Static Portfolio Optimization

[Adrian Trapletti]

I agree with Patrick. I wrote tseries about 10(?) years ago and for 
sure, it may be possible to improve it. Concerning the second point, it 
is in fact highly non-trivial if not impossible to construct portfolios 
that are "better" out-of-sample than naive portfolios. Raman Uppal from 
the London Business School and other authors wrote several nice papers 
about the subject. Googling I found the following one: 
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=911512

Best regards
Adrian

> Message: 4
> Date: Fri, 02 Oct 2009 19:57:58 +0100
> From: Patrick Burns <patrick at burns-stat.com>
> Subject: Re: [R-SIG-Finance] Static Portfolio Optimization
> To: Jorge Nieves <jorge.nieves at moorecap.com>
> Cc: r-sig-finance at stat.math.ethz.ch, jessevel at andrew.cmu.edu
> Message-ID: <4AC64D36.5010003 at burns-stat.com>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>
> Jorge Nieves wrote:
>   
>> > Thanks for your responses. I found the function "portfolio.optim" and
>> > the tseries series package [...] However, I still do not see how to
>> > pass into the function the vector of expected values and the covariance
>> > matrix. 
>>     
> [...]
>
> This bothers me on two counts:
>
> 1) computational
> 2) subject matter
>
> That Jorge is not finding the functionality
> that he wants means that modular programming
> is not being used.  The modular approach
> would have a function that does the optimization
> with the expected returns and variance as
> arguments.  That function would then be
> used by a function that does the larger task.
>
> Okay, an advantage of R is that it is generally
> easy to modify functions for your own purposes.
> But it is better to organize ocmputations so
> that people don't feel compelled to do that.
> Let's abandon the SAS monolith culture.
>
> I haven't investigated the functions that are
> under discussion, so perhaps I am misunderstanding
> what they are doing.  But if they are using the
> history of returns to predict future returns, that
> is almost always going to be pretty much complete
> nonsense -- with or without the Efficient Market
> Hypothesis.
>
> I would hope the R community embrace quality in
> subject matter decisions as well as computational
> quality.
>
>
> Patrick Burns
> patrick at burns-stat.com
> +44 (0)20 8525 0696
> http://www.burns-stat.com
> (home of "The R Inferno" and "A Guide for the Unwilling S User")
>
>
>   

-- 
Adrian Trapletti
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Switzerland

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Email : a.trapletti at swissonline.ch