Re: st: More efficient processing of nested loops?

[Nick Cox <njcoxstata@gmail.com>]

Sorry, Robson, but I spent a while working on my previous post and
will add no more time to this thread.
Someone else may naturally be able to help.

On Wed, Aug 15, 2012 at 7:55 PM, Robson Glasscock <glasscockrc@vcu.edu> wrote:
> Nick,
> Thank you for your help with this. Your approach gives me the return
> for a single day. There is some variation, but that day is usually the
> return on the date the two datasets were merged. These observations
> have non-missing values for datadate_2 and date_t_4. I had a difficult
> time writing this up, and I apologize if my description of the merged
> datasets or the problem was not clear enough.
>
> Dataset A has around 12 years of daily returns for 3,500 firms.
> Dataset B has the earnings announcement date for quarter t
> (datadate_2) and quarter t-4 (date_t_4) in each row. I merged these
> two datasets (1:1 cusip date) into Dataset C and ran the two loops.
> There are typically around 250 days between datadate_2 and date_t_4
> that need to be added together.
>
> I can provide any additional information if needed. Here is the code I
> ran from your responses:
>
> bysort cum_id_cum (datadate_2) : gen d2_temp_2 = datadate_2[1]
> bysort cum_id_cum (date_t_4) : gen dt_temp_2 = date_t_4[1]
> egen cusip_mode = mode(cusip), by(cum_id_cum)
> local mark "date <= d2_temp_2 & date >= dt_temp_2 & cusip_mode==cusip"
> egen cum_ab_temp = total(ab_ret) if `mark', by(cum_id_cum)
> replace cum_ab = cum_ab_temp if `mark'
>
> I wrote the two inefficient loops based on what I would do by hand to
> sum up the abnormal returns. Go into the first cusip and identify the
> two boundary dates for the first earnings announcement. Go back
> through all the daily returns for that cusip and mark each day that is
> between these two dates. Sum up these individual returns into one
> number. Do this again for the first cusip but second earnings
> announcement, etc.
>
> Thanks again for the time you have already spent on this,
> Robson
>
>
> On Wed, Aug 15, 2012 at 1:48 PM, Nick Cox <njcoxstata@gmail.com> wrote:
>> More speed-ups:
>>
>> bysort cum_id_cum (datadate2) : gen d2_temp_2 = datadate_2[1]
>> bysort cum_id_cum (date_t_4) : gen dt_temp_2 = date_t_4[1]
>>
>> On Wed, Aug 15, 2012 at 6:07 PM, Nick Cox <njcoxstata@gmail.com> wrote:
>>> egen cum_ab_temp = total(ab_ret) if `mark', by(cum_id_cum)
>>> replace cum_ab = r(sum) if `mark'
>>>
>>> should be
>>>
>>> egen cum_ab_temp = total(ab_ret) if `mark', by(cum_id_cum)
>>> replace cum_ab = cum_ab_temp if `mark'
>>>
>>> On Wed, Aug 15, 2012 at 5:59 PM, Nick Cox <njcoxstata@gmail.com> wrote:
>>>> The good news is that I think you are right. This code appears to be
>>>> much more complicated than it needs to be.
>>>>
>>>> I can't follow your word description -- doesn't mean it's unclear,
>>>> just means that it is too much for me to absorb -- but from looking at
>>>> your code there are several major and minor inefficiencies.
>>>>
>>>> The main problems are
>>>>
>>>> 1. You have two outer loops tangled together, one Stata's and one
>>>> home-made, but neither appears necessary.
>>>>
>>>> 2. The inner loop is a loop over one case, and so not needed.
>>>>
>>>> 3. -egen- calls are very inefficient to calculate constants that
>>>> -summarize- can calculate, except that #1 implies to me that you can
>>>> do most of the work in a few -egen- calls.
>>>>
>>>> 4. Some copying of values from one place to another to no obvious purpose.
>>>>
>>>> With some guesswork, your code to me boils down to
>>>>
>>>> egen d2_temp_2 = min(datadate_2), by(cum_id_cum)
>>>> egen dt_temp_2 = min(date_t_4), by(cum_id_cum)
>>>> egen cusip_mode = mode(cusip), by(cum_id_cum)
>>>> local mark "date <= d2_temp_2 & date >= dt_temp_2 & cusip_mode==cusip"
>>>> egen cum_ab_temp = total(ab_ret) if `mark', by(cum_id_cum)
>>>> replace cum_ab = r(sum) if `mark'
>>>>
>>>> Note that your use of "temporary variables" is not the same as Stata's.
>>>>
>>>> Nick
>>>>
>>>> On Wed, Aug 15, 2012 at 4:26 PM, Robson Glasscock <glasscockrc@vcu.edu> wrote:
>>>>
>>>>> I am running Stata 12. I have written code that creates a variable
>>>>> that is the sum of abnormal returns for each firm. The abnormal
>>>>> returns are accumulated from three days after the firm makes its
>>>>> earnings announcement in quarter t-4 to three days after the firm
>>>>> makes its earnings announcement in quarter t. My problem is that it
>>>>> takes around 2 minutes for my code to execute for each firm/quarter
>>>>> announcement, and there are around 150,000 earnings announcements in
>>>>> the dataset.
>>>>>
>>>>> I modified a panel dataset with the earnings announcement dates for
>>>>> each firm so that each observation contains both the quarter t
>>>>> earnings announcement date and the quarter t-4 earnings announcement
>>>>> date (with the three days added to each per above). I then merged this
>>>>> dataset with a second panel dataset that contains the daily returns
>>>>> for each firm. The merged dataset has around 10.2 million
>>>>> observations.
>>>>>
>>>>> Next, I created a count variable, cum_id_cum, which is a running total
>>>>> of each earnings announcement. This variable is truly cumulative and
>>>>> does not reset back to 1 when the next firm releases its first
>>>>> earnings announcement. The loop contains a variable, runn, that starts
>>>>> with a value equal to "1" and increases by 1 each time the loop is
>>>>> processed. I'm using that to help identify the particular dates of the
>>>>> quarter t and quarter t-4 earnings announcement so that the abnormal
>>>>> returns are accumulated over the correct period. Datadate_2 is the
>>>>> quarter t earnings announcement and  date_t_4 is the quarter t-4
>>>>> earnings announcement. Cusip is the identifier for each firm. Date is
>>>>> the date of the firm's return in the stock market for each trading
>>>>> day.
>>>>>
>>>>> The big picture of my approach was to create temporary variables that
>>>>> will equal the cusip, datadate_2, and date_t_4 when runn equals
>>>>> cum_id_cum. These first-step temporary variables (d2_temp, dt_temp,
>>>>> and cusip_temp) have missing values except in the observation where
>>>>> runn equals cum_id_cum so I created second-step temporary variables
>>>>> (cusip_temp_2, d2_temp_2, and dt_temp_2) which place the cusip,
>>>>> datadate_2, and date_t_4 values for each particular run through the
>>>>> entire dataset. This allows me to mark the days for each firm that are
>>>>> between the dates of the earnings announcements and then sum up the
>>>>> abnormal returns in a temporary variable called cum_ab_temp. The final
>>>>> variable with the sum of the abnormal returns for each firm is cum_ab
>>>>> and is retained in the observation where _merge==3 (from the merge
>>>>> mentioned in the second paragraph of this post).
>>>>>
>>>>> My code is below. Note that I constrain it to the first 25,000
>>>>> cum_id_cum values due to macro size constraints:
>>>>>
>>>>> gen runn= 1
>>>>> levelsof cum_id_cum if cum_id_cum !=. & cum_id_cum <25000, local(cum_id_cum)
>>>>> foreach 1 of local cum_id_cum{
>>>>> gen d2_temp= datadate_2 if runn== cum_id_cum
>>>>> gen dt_temp= date_t_4 if runn== cum_id_cum
>>>>> gen cusip_temp= cusip if runn== cum_id_cum
>>>>> egen cusip_temp_2= mode(cusip_temp)
>>>>> egen d2_temp_2= min(d2_temp)
>>>>> egen dt_temp_2= min(dt_temp)
>>>>> foreach x of varlist date{
>>>>> replace mark= 1 if `x' <= d2_temp_2 & `x' >= dt_temp_2 & cusip_temp_2==cusip
>>>>> egen cum_ab_temp= total(ab_ret) if mark==1
>>>>> replace cum_ab= cum_ab_temp if datadate_2== d2_temp & dt_temp==
>>>>> date_t_4 & cusip_temp==cusip & d2_temp !=. & dt_temp !=.
>>>>> replace runn= runn+1
>>>>> drop d2_temp
>>>>> drop dt_temp
>>>>> drop d2_temp_2
>>>>> drop dt_temp_2
>>>>> drop cusip_temp
>>>>> drop cusip_temp_2
>>>>> drop cum_ab_temp
>>>>> replace mark=0
>>>>>
>>>>> }
>>>>> }
>>>>>
>>>>> I'm wondering if there is a more efficient way to do the above which
>>>>> will result in a significantly faster processing time. My fear is that
>>>>> the above ignores the functionality of Stata and instead uses
>>>>> inefficient brute force.
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