Client | Financial Product | Date | Return | Return +1 |
Customer A | Product A | Jan2016 | 2% | $1.02 |
Product A | Feb2016 | 3% | $1.03 | |
Product A | Mar2016 | 1% | $1.01 | |
Product A | Apr2016 | 7% | $1.07 | |
Product A | May2016 | -5% | $0.95 | |
Product A | Jun2016 | 3% | $1.03 | |
Product A | Jul2016 | -0.50% | $1.00 | |
Product A | Aug2016 | 3% | $1.03 | |
Product A | Sep2016 | 2% | $1.02 | |
Product A | Oct2016 | 5% | $1.05 | |
Product A | Nov2016 | 1% | $1.01 | |
Product A | Dec2016 | -1% | $0.99 | |
Customer A | Product B | |||
.... | .... | |||
..... | ..... | |||
....... | ....... | |||
......... | ......... | |||
......... | ......... |
Financial Product | Date_From | Date_To | GM_Formulae | Cumulative Return |
Product A | Jan2016 | Feb2016 | (1+r2)-1 | 2.00% |
Product A | Jan2016 | Mar2016 | (1+r2)*(1+r3)-1 | 5.06% |
Product A | Jan2016 | Apr2016 | .... | .... |
Product A | Jan2016 | May2016 | ..... | ..... |
Product A | Jan2016 | Jun2016 | ....... | ....... |
Product A | Jan2016 | Jul2016 | ......... | ......... |
Product A | Jan2016 | Aug2016 | ......... | ......... |
Product A | Jan2016 | Sep2016 | .... | .... |
Product A | Jan2016 | Oct2016 | ..... | ..... |
Product A | Jan2016 | Nov2016 | ....... | ....... |
Product A | Jan2016 | Dec2016 | (1+r1)*(1+r2)....*(1+r11)-1 | 23.16% |
Product A | Feb2016 | Mar2016 | ......... | ......... |
Product A | Feb2016 | Apr2016 | .... | .... |
Product A | Feb2016 | May2016 | ..... | ..... |
Product A | Feb2016 | Jun2016 | ....... | ....... |
Product A | Feb2016 | Jul2016 | ......... | ......... |
Product A | Feb2016 | Aug2016 | ......... | ......... |
Product A | Feb2016 | Sep2016 | ||
Product A | Feb2016 | Oct2016 | ||
Product A | Feb2016 | Nov2016 | ||
Product A | Feb2016 | Dec2016 | ||
For 12 months there will be 66 combinations of months |
data customer_dup(keep=cum_sum date_from date_to return prev_return) ;
retain cum_sum prev_return date_from start_pos position;
set customer_dup;
by customer product year;
if (first.customer or first.product or first.year) then
do;
put obs=;
prev_return=return_cal;cum_sum=return_cal;date_from=month;date_to=month;start_pos=obs;
end;
else
do;
date_to=month;
cum_sum=prev_return*return_cal;
prev_return=cum_sum;
end;final_cum=cum_sum-1;output;
if (last.customer or last.product or last.year) then
do;
do num=tot_group-1 to 1 by -1; position=start_pos+1;
do i= 1 to num by 1;
set customer_dup point=position;
if (i eq 1) then
do;date_from=month;date_to=month; prev_return=return_cal;cum_sum=return_cal;end;
else
do;
date_to=month;cum_sum=prev_return*return_cal;prev_return=cum_sum;
end;final_cum=cum_sum-1; output;
position=position+1;
end; start_pos=start_pos+1;
end;
end;
informat date_from date_to MONYY7.;
format date_from date_to MONYY7.;
run;
Of course you can do this in SAS 🙂
I'm assuming your question is how do you do this in SAS?
When googling I suggest limiting search to lexjansen.com or include SAS NOTES.
1. Why are you missing months in your data?
2. How do you account for missing months?
3. How do calculate the rate at the yearly boundaries, does it reset at some point?
4. How big is your dataset - 5 million is different than 200 million.
There's a geomean function but that would mean transposing your dataset. The best methods are to look at PROC EXPAND, usually under time series in EG or convert to logs and then use proc means and revert back to regular scale. The second option is detailed here: http://support.sas.com/kb/25/366.html
If I was right, that is not geometric mean.
GM should like : ((1+r1)*(1+r2)) **1/2
data have;
infile cards expandtabs truncover;
input Client & $20. FinancialProduct & $20. Date : monyy5. Return : percent8.;
format date monyy. return percentn8.2;
cards;
Customer A Product A Jan2016 2% $1.02
Customer A Product A Feb2016 3% $1.03
Customer A Product A Mar2016 1% $1.01
Customer A Product A Apr2016 7% $1.07
Customer A Product A May2016 -5% $0.95
Customer A Product A Jun2016 3% $1.03
Customer A Product A Jul2016 -0.50% $1.00
Customer A Product A Aug2016 3% $1.03
Customer A Product A Sep2016 2% $1.02
Customer A Product A Oct2016 5% $1.05
Customer A Product A Nov2016 1% $1.01
Customer A Product A Dec2016 -1%
;
run;
proc sql ;
create table temp as
select a.client,a.financialproduct,a.date,b.date as _date,b.return
from have as a,have as b
where a.client=b.client and a.financialproduct=b.financialproduct
and a.date lt b.date
order by a.client,a.financialproduct,a.date,b.date ;
quit;
data want;
set temp;
by client financialproduct date;
retain cum 1;
if first.date then cum=1;
cum=cum*(1+return);
mean=cum-1;
run;
It looks like a compound return rate?
Thanks a lot @Ksharp. You are correct. It is compounded return. If the time period exceeds 12 months , then I have to calculate the annualized return too.
My real problem is far more complicated than what I presented in "I HAVE". I tried to adjust your code but I am missing something.
Can you help me modify your code for the data bellow (I removed the last variable since you didn't used in your code)
Customer A ClassA Product A Jan2016 2% $1.02
Customer A ClassA Product A Feb2016 3% $1.03
Customer A ClassA Product A Mar2016 1% $1.01
Customer A ClassA Product A Apr2016 7% $1.07
Customer A ClassA Product A May2016 -5%
Customer A ClassA Product A Jun2016 3%
Customer A ClassA Product A Jul2016 -0.50%
Customer A ClassA Product A Aug2016 3%
Customer A ClassA Product A Sep2016 2%
Customer A ClassA Product A Oct2016 5%
Customer A ClassA Product A Nov2016 1%
Customer A ClassA Product A Dec2016 -1%
Customer A ClassA Product A Jan2015 -2%
Customer A ClassA Product A Feb2015 0.5%
Customer A ClassA Product A Mar2015 3%
Customer A ClassA Product A Apr2015 4%
Customer A ClassA Product A May2015 6%
Customer A ClassA Product A Jun2015 -1%
Customer A ClassA Product A Jul2015 5%
Customer A ClassA Product A Aug2015 8%
Customer A ClassA Product A Sep2015 -6%
Customer A ClassA Product A Oct2015 4%
Customer A ClassA Product A Nov2015 1.3%
Customer A ClassA Product A Dec2015 .8%
Customer A ClassA Product B Jan2016 3%
Customer A ClassA Product B Feb2016 7%
Customer A ClassA Product B Mar2016 9%
Customer A ClassA Product B Apr2016 -2%
Customer A ClassA Product B May2016 -1%
Customer A ClassA Product B Jun2016 -4%
Customer A ClassA Product B Jul2016 -4%
Customer A ClassA Product B Aug2016 1%
Customer A ClassA Product B Sep2016 -2%
Customer A ClassA Product B Oct2016 3%
Customer A ClassA Product B Nov2016 -5%
Customer A ClassA Product B Dec2016 6%
Customer A ClassA Product B Jan2015 -1%
Customer A ClassA Product B Feb2015 3%
Customer A ClassA Product B Mar2015 -1%
Customer A ClassA Product B Apr2015 2%
Customer A ClassA Product B May2015 -3%
Customer A ClassA Product B Jun2015 3%
Customer A ClassA Product B Jul2015 -9%
Customer A ClassA Product B Aug2015 2%
Customer A ClassA Product B Sep2015 1%
Customer A ClassA Product B Oct2015 -2%
Customer A ClassA Product B Nov2015 3%
Customer A ClassA Product B Dec2015 -2%
Thanks once again.
Add one more code.
CODE NOT TESTED.
data want;
set temp;
by client financialproduct date;
retain cum 1;
if first.date or month(_date)=1 then cum=1;
cum=cum*(1+return);
mean=cum-1;
run;
Let's say you have data set have, sorted by customer/product/date, where date is a true sas date variable. You also have the additional vars RET and RETPLUS1, and there are no gaps in the time series.
You want rolling cumulative returns for 2-months, 3-months, .... 12 months:
Erroneous program removed.
This program takes advantage of temporary arrays, which have their values automatically retained. We also use the fact that
3-month cumret = (prior 2-month cumret + 1) * (1 + ret) - 1
and generally
J-month cumret = (prior {J-1}-month cumret + 1) * (1+ret) - 1
Editted. See the comments below. So the program first updates 2-month-cumret, then 3-month-cumret, ... 12-month-cumret
Thanks to @ksachar's response, I realize that I wan't paying attention to my own advice. First the program should loop from 12 to 2, not 2 to 12. I.e. update 12 month cumret first, then 11 month, etc. And I should update R{1} AFTER the loop, not before. The corrected program follows:
data want (drop=N nmonths);
set have (rename=(date=todate));
by prod;
format fromdate yymmddn8.;
array r {12} _temporary_;
if first.prod then do;
N=0;
call missing(of r{*});
end;
N+1;
do nmonths=12 to 2 by -1;
if N<nmonths then continue;
r{nmonths}=(1+ret)*(1+r{nmonths-1})-1;
cumret=r{nmonths};
fromdate=intnx('month',todate,1-nmonths,'end');
output;
end;
r{1}=ret;
run;
@mkeintz Thank you so very much for responding to my post. I tried the code but it is not giving me correct cumret. Will you be able to provide me the modified code on this sample data (I have added another variable). Also, Can you share the output so I can compare? want to make sure I am not making any mistake
If I need to extend the code to 24 months, will it work if I change :
array r {12} _temporary_;
to
array r {24} _temporary_;
AND
do nmonths=2 to 12;
to
do nmonths=2 to 24;
data customer_dup(keep=cum_sum date_from date_to return prev_return) ;
retain cum_sum prev_return date_from start_pos position;
set customer_dup;
by customer product year;
if (first.customer or first.product or first.year) then
do;
put obs=;
prev_return=return_cal;cum_sum=return_cal;date_from=month;date_to=month;start_pos=obs;
end;
else
do;
date_to=month;
cum_sum=prev_return*return_cal;
prev_return=cum_sum;
end;final_cum=cum_sum-1;output;
if (last.customer or last.product or last.year) then
do;
do num=tot_group-1 to 1 by -1; position=start_pos+1;
do i= 1 to num by 1;
set customer_dup point=position;
if (i eq 1) then
do;date_from=month;date_to=month; prev_return=return_cal;cum_sum=return_cal;end;
else
do;
date_to=month;cum_sum=prev_return*return_cal;prev_return=cum_sum;
end;final_cum=cum_sum-1; output;
position=position+1;
end; start_pos=start_pos+1;
end;
end;
informat date_from date_to MONYY7.;
format date_from date_to MONYY7.;
run;
This works perfectly. My initial requirement was for a data for 6 years (i.e. 72 months and different combinations for 72 months-- which is nCr =(72C2) = 2556 ). After discussing with @lakshmi_74lakshmi i made minor changes to the code; removed YEAR in BY statement, anywere in first.year, last.year etc. and the retun was calculated for each Product group (date combinations).
@lakshmi_74 @Ksharp @mkeintz @Reeza Thank you for finding time and helping me resolve this.
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