[R-SIG-Finance] making sense of 100's of funds

[paul sorenson]

Brian,

A few weeks ago this would have been "all greeks to me".  I am still a 
long way off to contemplate making a career change from engineering to 
econometrics.

You also answered my next question, where is "the R function" to 
subsample to monthly data.  Weekly is pretty trivial (holidays aside) 
but monthly requires a few tricks and I didn't want to reinvent the wheel.

cheers

Brian G. Peterson wrote:
> BBands wrote:
>> On 8/19/07, Patrick Burns <patrick at burns-stat.com> wrote:
>>> That paper talks about the effects of asynchrony and proposes a
>>> method of backing out data without asynchrony.  Our investigation
>>> suggested that using weekly data is adequate for avoiding asynchrony
>>> effects.
>> We have done a lot of work on this problem and agree with the choice
>> of weekly data.
>>
>> Paul,
>>
>> The use of benchmarks may not be the optimal path in this application,
>> relatively simple ranking might be more viable. As a compromise, you
>> might try looking at ranked Sharpe ratios...
> 
> A stack ranking of risk/reward ratios is a good idea.  I would recommend 
> using either a Cornish Fisher modified Sharpe ratio (to take possible 
> non-normality of distributions into account) or Sortino's Upside 
> Potential Ratio.  Even Sharpe himself recommends the use of Information 
> Ratio preferentially to the original Sharpe ratio, but old habits die 
> hard...
> 
> To answer an earlier question on sub-sampling:  Yes, from daily *price* 
> data you can construct a weekly or monthly series by simply taking the 
> price at the end of the week or month, and constructing your returns 
> series from the end of period closing price.  The zoo library also has a 
> good implementation of the aggregate() function for timeseries data to 
> help you automate the sampling while maintaining your original daily data.
> 
> Regards,
> 
>     - Brian
> 
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