[R-SIG-Finance] making sense of 100's of funds
sf at metrak.com
Mon Aug 20 01:58:33 CEST 2007
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
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.
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
>> We have done a lot of work on this problem and agree with the choice
>> of weekly data.
>> 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
> 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.
> - Brian
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