[R] R Book Advice Needed

Wed Jun 13 19:14:47 CEST 2007

Pat:

I have done PCA to extract eigenvectors on return series for equities.

Rotation does help and does make factors more understandable,
have had success doing this.

You are right, when doing pure statistical factors, one tends to find first factor
which explains most of the variance is the Market Beta.

Our scree score showed 20 factors explains most of the variance in equity returns.

If you sort on the factor loadings, the other first few factors tend to things such as 
interest rates,Energy prices, currency exposure. After that it gets a little more
complicated what the factors are but they tend to be sector specific. 

That's the major complaint about pure statistical factor analysis...
Interpretation but can get reasonable idea by sorting factor cores.

As for missing values, a lot of work has been done there with sampling
such as EM and Maximum Likehood.

I will check out your R code. Hopefully it will get included
Eventually in the Portfolio package.

Being new to R, will need to figure out how to "source" the code to R!

Regards,
Neil

-----Original Message-----
From: Patrick Burns [mailto:patrick at burns-stat.com] 
Sent: Wednesday, June 13, 2007 12:56 PM
To: Gottlieb, Neil
Subject: Re: [R] R Book Advice Needed

Neil,

'factor.model.stat' is a part of POP, which is an R package (that runs under S-PLUS as well).

We've made 'factor.model.stat' public domain so you don't have to have POP in order to use it.  The version of 'factor.model.stat' in the Public Domain area is not in a package.
You can just 'source' the code.  I just checked and 'factor.model.stat' is not in the 'portfolio' package -- I'm not sure why they haven't included it.

The statistical factors are already orthogonal.  Rotation is only aimed at trying to make them more interpretable.  I'm not very optimistic about that, other than the first factor represents the market.  But if you do have success, I'd be interested in hearing of it.

A caveat to the paragraph above is that orthogonality assumes no missing values.  Having no missing values is not a very common occurrence though (at least for a lot of us).  Most of the code in 'factor.model.stat' is handling missing values.

I haven't had call for rotations, but I'd be extremely surprised if there weren't a bunch somewhere in R.  The 'RSiteSearch' function should be your friend for this.

Pat

ngottlieb at marinercapital.com wrote:

>Thank Patrick. Is factor.model.stat part of r packages?
>
>Also want to rotate the factors so they are orthogonal. 
>Do you have varimax or promax rotation functio?
>
>Neil
>
>-----Original Message-----
>From: Patrick Burns [mailto:patrick at burns-stat.com]
>Sent: Wednesday, June 13, 2007 11:28 AM
>To: Gottlieb, Neil
>Subject: Re: [R] R Book Advice Needed
>
>Most or all of the work for your factor model should be done in 'factor.model.stat' from the Public Domain page of the Burns Statistics website.  It is also in the 'portfolio' package, I believe.
>
>Patrick Burns
>patrick at burns-stat.com
>+44 (0)20 8525 0696
>http://www.burns-stat.com
>(home of S Poetry and "A Guide for the Unwilling S User")
>
>ngottlieb at marinercapital.com wrote:
>
>  
>
>>Cody:
>>
>>Think you might have asked the question for me Neil.
>>
>>I do time series analysis of return data in finance.
>>
>>I will be creating a factor model based on PCA Or Single Value 
>>Decomposition to get Eigenvectors Of the correlation matrix (tends to 
>>work better for finance data Than covariance).
>>
>>>From there will be doing style analysis, some optimization,
>>Regime switching, co-intregration testing and some Statistical Process 
>>Control charting such as CUSUM.
>>
>>Ultimately, what I learned over the years with statistics, 
>>visualization is critical for my end-users. The don't care what 
>>cluster method I use, be it Hierarchical or Rosseau' newer methods 
>>such as Fanny, which I find more robust.
>>
>>In end I need practical stuff: as a programmer on Data types, data 
>>structures and even how to format and read in Data.
>>
>>So that's basically stuff I will be doing.
>>
>>Neil
>>
>>-----Original Message-----
>>From: r-help-bounces at stat.math.ethz.ch 
>>[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of 
>>Cody_Hamilton at edwards.com
>>Sent: Tuesday, June 12, 2007 6:36 PM
>>To: r-help at stat.math.ethz.ch
>>Subject: Re: [R] R Book Advice Needed
>>
>>
>>
>>Alain,
>>
>>Can you tell us what you plan to use R for?
>>
>>Regards,
>>-Cody
>>
>>ngottlieb at marinercapital.com a écrit :
>> 
>>
>>    
>>
>>>I am new to using R and would appreciate some advice on which books 
>>>to start with to get up to speed on using R.
>>>
>>>My Background:
>>>1-C# programmer.
>>>2-Programmed directly using IMSL (Now Visual Numerics).
>>>3- Used in past SPSS and Statistica.
>>>
>>>I put together a list but would like to pick the "best of"
>>>and avoid redundancy.
>>>
>>>Any suggestions on these books would be helpful (i.e. too much 
>>>overlap, porly written etc?)
>>>
>>>Books:
>>>1-Analysis of Integrated and Co-integrated Time Series with R (Use R)
>>>- Bernhard Pfaff 2-An Introduction to R - W. N. Venables
>>>3-Statistics: An Introduction using R - Michael J. Crawley 4-R 
>>>Graphics (Computer Science and Data Analysis) - Paul Murrell 5-A 
>>>Handbook of Statistical Analyses Using R - Brian S. Everitt 
>>>6-Introductory Statistics with R - Peter Dalgaard 7-Using R for 
>>>Introductory Statistics - John Verzani 8-Data Analysis and Graphics 
>>>Using R - John Maindonald; 9-Linear Models with R (Texts in 
>>>Statistical Science) - Julian J.
>>>Faraway
>>>10-Analysis of Financial Time Series (Wiley Series in Probability and 
>>>Statistics)2nd edition - Ruey S. Tsay
>>>
>>>Thanks.
>>>
>>>Neil Gottlieb
>>>
>>>   
>>>
>>>      
>>>
>>Cody Hamilton, PhD
>>Edwards Lifesciences
>>	[[alternative HTML version deleted]]
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>>R-help at stat.math.ethz.ch mailing list
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>>PLEASE do read the posting guide
>>http://www.R-project.org/posting-guide.html
>>and provide commented, minimal, self-contained, reproducible code.
>>
>>
>> 
>>
>>    
>>
>
>
>  
>
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