[R-SIG-Finance] LSPM - Unexpected Results

[Noah Silverman]

Josh,

1)
R version: 2.11.0
LSPM Version:  (Don't know where to check)

2)
I'm fine with it taking many hours - that's to be expected.  There are
48 observations for each of the 13 assets.

3)
The last output line from the optimizer is:
0 0.5908 0.545 0.0954 0.41 0 0.0969 0.0078 0.6066 0.4896 0.0113 0.7568 1
-0.5036 -1 best value:  -1

The last two values are negative, which I am now assuming are the "z"
values.  But do not know what "z" values are - don't see any mention of
them in the Handbook of Portfolio Mathematics.

4)
Is there documentation about how to specify the margin and other
constraints?  The help files in R left me a bit lost.

I'm trying this while looking at a portfolio that is a "fund of funds". 
So, they want to invest 100% of their fund and can't short anything. 
Furthermore, they can only re-balance every 90 days, which only adds
further constraint. 


I'm using the jpt example function from your blog with n=4 and the
following call in R:
DEctrl <- list(NP=100, itermax=1000, trace=1 )
res <- maxProbProfit(jpt, 1e-6, 6, probDrawdown, 0.1, DD=0.2,
calc.max=4, snow=cl, control=DEctrl)


Thanks!!!!

--
Noah
 



On 12/27/10 10:23 AM, Joshua Ulrich wrote:
> Noah,
>
> On Mon, Dec 27, 2010 at 12:02 PM, Noah Silverman
> <noah at smartmediacorp.com> wrote:
>> Hi,
>>
>> I've been playing with the LSPM library from optimizing a portfolio of
>> 13 assets (Using the interesting Leverage Space model developed by Ralph
>> Vince.)
>>
> Which version (and revision) of LSPM are you using?  Which version of R?
>
>> Using the function:  maxProbProfit
>>
>> res <- maxProbProfit(jpt, 1e-6, 6, probDrawdown, 0.1, DD=0.2,
>> calc.max=4, snow=cl, control=DEctrl)
>>
>> It takes a LONG time for the optmizer to find a solution.
>>
> It's always going to take a long time, especially with 13 assets and
> 100 optimizer iterations (the default).  How many observations are in
> your lsp object?  A reproducible example, or just some sample data,
> would really help here.
>
>> It has found a solution that has a 100% probability of profit given the
>> constraints.
>>
>> The odd thing is that several of the values (optimal F) are negative.
>> My understanding is that indicates shorting the asset.  This assets in
>> this portfolio can't be shorted.
>>
> I doubt any f values are negative because they're bound between [0,1]
> during the optimization.  My guess is that you're referring to the two
> "z" values, which will always be negative.
>
> Here's a quick example from ?maxProbProfit:
>
> require(LSPM)
> data(port)
> ipop <- cbind(runif(50,0,0.01),runif(50,0,0.01),runif(50,0,0.01),
>   runif(50,-1,-0.8),runif(50,-1,-0.8))
> DEctrl <- list(itermax=11, NP=50, initial=ipop)
> res <- maxProbProfit(port, 1e-6, 4, probDrawdown, 0.1,
>   DD=0.2, calc.max=4, control=DEctrl)
>
>> res
> $f
> [1] 0.002308994 0.001884296 0.116972712
>
> $z
>     zminus      zplus
> -0.9256852 -0.9976883
>
> $profitProb
> [1] 0.9963587
>
>
>> I've never read about shorting with the LSPM model, is this common?
>> Is there a way to indicate, as a constraint, not to short?
>> I have a portfolio of X dollars to allocate, so the percentages must sum
>> to one, how do I do this once I get the F values?
>>
> You need to do this during the optimization via the "margin" and
> "equity" arguments.
>
> HTH,
> --
> Joshua Ulrich  |  FOSS Trading: www.fosstrading.com
>
>
>
>> Thanks in advance for any and all suggestions!
>>
>> --
>> Noah
>>
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