Dear all,

I am currently working on a study on the effect of televised stock recommendations in the Netherlands. As part of my analysis, I need to create calendar time portfolios based on these recommendations in order to examine the possible long term excess returns caused by the recommendations. Here is where I hope anyone is able to help:

On an eventdate (i.e date of broadcast) a certain recommendation is given about stock X. The recommendation is represented by the variable ADVICE in my dataset (see the DataEx below) where an ADVICE of 1 represents a “strong buy” advice, 2 a “buy” advice, 3 a “sell” advice and 4 a “strong sell” advice. I wish to create separate equally weighted portfolios for each type of recommendation that looks as follows: on the date of the recommendation, companies with the same value for ADVICE will be put in the same portfolio. The stocks will be bought using the opening price (PO in my dataset) of the same date and will be held within the portfolio for several durations (60, 120, 180, 240 and 300 trading days) after which the stock is sold. Stocks with ADVICE values 3 and 4 (sell recommendations imply that short positions need to be taken, hence the portfolios of advices 3 and 4 only contain short positions.
I wish to create portfolio returns of the separate portfolios (this means for each type of recommendation, five portfolios which represent the different holding durations) which I can use in CAPM and four-factor regressions in order to calculate excess returns of the portfolios. My dataset looks as follows:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input byte ADVICE long companyid float(eventdate date) double(P PO) float(ret mrktret) double(MOM SMB HML rf) float Mkt_rf
1 3 16320 16320  36.6 35.93   .018753996    .005853557 -.43  -.16 -.13 .01  -.0041464427
1 3 16320 16321  36.7 36.65  .0027285146    .003200389 -.23   .07  .16 .01   -.006799611
1 3 16320 16322 35.86 36.48  -.023154287  -.0013875903 -.01   .14 -.11 .01    -.01138759
1 3 16320 16323  35.5 35.65  -.010089772    -.01034675  .33   .31  .23 .01    -.02034675
1 3 16320 16324  35.2 35.25  -.008486614    .005626621 -.22     0  .04 .01  -.0043733786
1 3 16320 16327 34.78  35.3  -.012003574    .013465884  -.4  -.42 -.07 .01    .003465884
1 3 16320 16328 35.22  34.4   .012571594  -.0031168545  .08   .37 -.21 .01   -.013116854
1 3 16320 16329 35.28 35.22   .001702128   -.003247049  .13    .2  .16 .01   -.013247048
1 3 16320 16330 35.25 35.02 -.0008507019  .00012044927  .04  -.05  .05 .01    -.00987955
1 3 16320 16331 34.95 35.08   -.00854706     .00836574  .08  -.25 -.14 .01    -.00163426
1 3 16320 16334  35.2 35.06   .007127614   -.008938003  .23   .28  .14 .01   -.018938003
1 3 16320 16335  35.3 35.52  .0028368814    .006456576   .3  -.42  .11 .01   -.003543424
1 3 16320 16336 18.78  18.9    -.6310905   -.013925955  .48   .57 -.06 .01   -.023925954
1 3 16320 16337 35.15 35.38     .6268321    -.00939289  .52   .38    0 .01    -.01939289
1 3 16320 16338 35.57 35.15   .011877968  -.0014104375  .12  -.12 -.01 .01   -.011410438
1 3 16320 16341 35.87  35.7   .008398705   -.007514896  .49   .26  .04 .01   -.017514896
1 3 16320 16342  35.5 35.61  -.010368595    .002439289  .37  -.28    0 .01   -.007560711
1 3 16320 16343  35.8 35.44   .008415197   .0032021704 -.07   -.3   .3 .01    -.00679783
1 3 16320 16344  36.1 35.82   .008344972    -.00418952  .29   .69  .21 .01    -.01418952
1 3 16320 16345 35.81 36.25  -.008065681    .021167235 -.38 -1.33  .08 .01    .011167236
1 3 16320 16348 35.59 35.53  -.006162485    .012971986 -.44  -.24 -.11 .01    .002971986
1 3 16320 16349 35.63 35.82  .0011232801   .0027111995  .13  -.09   .4 .01     -.0072888
1 3 16320 16350 35.91  35.8   .007827829    .002525817  .09   .25 -.11 .01   -.007474183
1 3 16320 16351 35.98 35.79  .0019474203   .0021641501  .06    .4  .33 .01    -.00783585
1 3 16320 16352 35.79 35.75  -.005294704   -.008236863  .21   .16  .03 .01   -.018236862
1 3 16320 16355 35.41 35.71   -.01067426   -.001434249   .2    .2  .22 .01    -.01143425
1 3 16320 16356 35.35 35.53 -.0016958738   -.013425848   .2   .28 -.14 .01    -.02342585
1 3 16320 16357 35.23 35.25    -.0034004    .004716562 -.27  -.15 -.03 .01   -.005283438
1 3 16320 16358  35.2  35.4 -.0008519097   -.008481558  .16   .27  .03 .01   -.018481558
1 3 16320 16359  35.1 35.12  -.002844952    -.00240608  .06   .05  .01 .01    -.01240608
1 3 16320 16362 35.35  35.2   .007097262  -.0039414037  .38   .06  .07 .01   -.013941403
1 3 16320 16363 35.68  35.6   .009291915    .010657836 -.46  -.39  -.3 .01   .0006578357
1 3 16320 16364 35.91 35.66     .0064255   -.014153914  .25   .47 -.22 .01   -.024153914
1 3 16320 16365 35.73 35.75  -.005025136    .003496078 -.19   .05 -.01 .01   -.006503922
1 3 16320 16366 35.65 35.65  -.002241525   .0011626486  .25    .2  .17 .01   -.008837352
1 3 16320 16369 35.37 35.67  -.007885144     -.0154072  .36   .79  .11 .01     -.0254072
1 3 16320 16370 35.15 35.23  -.006239385    .003379062  -.3  -.12  .04 .01   -.006620937
1 3 16320 16371    35 35.24  -.004276557    .014135814  -.2   -.8  -.2 .01   .0041358145
1 3 16320 16372 34.45  34.7  -.015839064    .008810095 -.44  -.48  .06 .01  -.0011899055
1 3 16320 16373 18.93 18.78     -.598761 -.00042353655 -.14    .4  .23 .01   -.010423536
1 3 16320 16376 34.41 33.86     .5975993   .0020554992  .02  -.41 -.15 .01   -.007944501
1 3 16320 16377 34.05 34.43  -.010517187    .009377323 -.19  -.32   .1 .01   -.000622677
1 3 16320 16378  34.2  34.2  .0043956116    .003851613 -.15   .03 -.25 .01   -.006148387
1 3 16320 16379 34.05 34.16 -.0043956116    -.00600782  .12   .12  .45 .01    -.01600782
1 3 16320 16380 34.04 33.94 -.0002937289    .008359256 -.47   .06 -.25 .01  -.0016407438
1 3 16320 16383 34.05 34.19  .0002937289   .0019009154    0    .3  .04 .01   -.008099085
1 3 16320 16384 34.65 34.05   .017467692   -.001247068 -.07   .13  .19 .01   -.011247068
1 3 16320 16385 34.55 34.75 -.0028901754    .005481414 -.33   .02 -.02 .01  -.0045185857
1 3 16320 16386 34.73  34.5   .005196317    .011574747 -.38   -.4  .24 .01    .001574747
1 3 16320 16387 34.65 34.85 -.0023061412     .00361527 -.06   .03 -.12 .01    -.00638473
1 3 16320 16390 34.46 34.61  -.005498494  -.0032940314  .07   .24  .05 .01    -.01329403
1 3 16320 16391 34.61  34.3  .0043434263   -.008621502  .23   .41 -.09 .01   -.018621502
1 3 16320 16392 35.33 34.62   .020589804   .0078620445 -.24  -.42  .07 .01  -.0021379555
1 3 16320 16393 35.85 35.31   .014611105  -.0015791786 -.02   .12  .25 .01   -.011579178
1 3 16320 16394 37.15    37   .035620205   -.006577426  .56   .73  .33 .01   -.016577426
1 3 16320 16397    37  37.2 -.0040458585    -.00587985  .52   .08  .33 .01   -.015879849
1 3 16320 16398 36.89 36.83  -.002977401   -.000889416  .36   .29  .12 .01   -.010889416
1 3 16320 16399 37.03 37.06   .003787883   .0003558613  .15   .16  .11 .01    -.00964414
1 3 16320 16400 36.63 36.98  -.010860818    .008679277 -.11  -.35 -.01 .01  -.0013207227
1 3 16320 16401  36.9 36.95   .007343974 -.00002939404  .11   .31  .02 .01   -.010029394
1 3 16320 16404 36.78 36.97  -.003257332  -.0017652255  .07    .2 -.03 .01   -.011765226
1 3 16320 16405 36.27 36.72  -.013963266    -.00724052  .45   .58  .25 .01   -.017240519
1 3 16320 16406 35.26 36.34  -.028241776    .016473383 -.11  -.58 -.05 .01    .006473383
1 3 16320 16407  34.9  35.2  -.010262348    .004947624 -.36  -.22  .05 .01   -.005052376
1 3 16320 16408    35 35.01   .002861232   -.002820056  .02   .49    0 .01   -.012820056
1 3 16320 16411 35.39 34.79   .011081233    -.00364586  .16   .25   .2 .01    -.01364586
1 3 16320 16412 19.05  18.9    -.6193622    .003325557 -.04  -.05 -.03 .01   -.006674442
1 3 16320 16413  34.6 34.69     .5967866  -.0001747539 -.29   .01  .12 .01   -.010174754
1 3 16320 16414 35.24 34.81    .01832812   -.009305932  .34   .25 -.03 .01    -.01930593
1 3 16320 16415 35.05 35.16  -.005406187    .004897873  .14  -.06 -.05 .01   -.005102126
1 3 16320 16418 35.29 34.99   .006824024    .010216794 -.21  -.57  .12 .01    .000216794
1 3 16320 16419 35.69 35.63     .0112709   .0024874196  .27   .03 -.18 .01    -.00751258
1 3 16320 16420    36 35.68   .008648401   -.004806866  .17   .38  .17 .01   -.014806867
1 3 16320 16421 35.07 35.86  -.026172874     .00555766   .3  -.06  .14 .01  -.0044423393
1 3 16320 16422 33.85 34.28  -.035407066   -.008522779  .47   .74  .27 .01   -.018522779
1 3 16320 16425 34.48 34.38   .018440446 -.00020381127  .27  -.29 -.11 .01    -.01020381
1 3 16320 16426 34.42 34.63  -.001741655   .0016874695  .06  -.11   .2 .01   -.008312531
1 3 16320 16427 33.87 33.95  -.016108124    .008135422 -.23  -.42  .04 .01   -.001864578
1 3 16320 16428 31.85  33.1   -.06149229    .004373227 -.07   .03  .04 .01   -.005626773
1 3 16320 16429 33.57  31.9    .05259543  -.0007466974 -.16   .04  .07 .01   -.010746697
1 3 16320 16432 34.05 33.86   .014197222  -.0007472553  .15   .17 -.12 .01   -.010747255
1 3 16320 16433 34.02    34 -.0008814457   .0017235936 -.03  -.05 -.09 .01   -.008276407
1 3 16320 16434 34.69 34.26   .019502874   -.003133402  .13     0  .01 .01   -.013133402
1 3 16320 16435  34.6 34.97  -.002597779 -.00014396982  .06   .25 -.09 .01    -.01014397
1 3 16320 16436 34.23 34.44   -.01075123    .002329763 -.04   .19 -.07 .01   -.007670237
1 3 16320 16439 34.38  33.9  .0043725474    .010943123 -.24  -.15  .18 .01   .0009431231
1 3 16320 16440 33.64 34.18  -.021759165    .002185669 -.31   .01  .13 .01   -.007814331
1 3 16320 16441 33.91 33.15   .007994121   -.007170937   .1   .35  .11 .01   -.017170938
1 3 16320 16442  34.3 34.25   .011435398    .009776138 -.28  -.16   .2 .01 -.00022386223
1 3 16320 16443 34.13  34.5  -.004968591    .002429174 -.17   .26  .16 .01   -.007570826
1 3 16320 16446 34.63 34.13   .014543596    .001888415 -.05   .23 -.01 .01   -.008111585
1 3 16320 16447 34.84  34.5   .006045794   -.007773781  .43   .63    0 .01   -.017773781
1 3 16320 16448  34.1  34.7   -.02146877   -.006491311  .12   .71  .13 .01    -.01649131
1 3 16320 16449 18.94 19.12    -.5880213    .002823773  .14     0 -.05 .01   -.007176227
1 3 16320 16450 34.46 34.33     .5985231     .00466029  .22  -.14  .22 .01   -.005339709
1 3 16320 16453 33.83 34.36   -.01845125    .004271758  .09   .04  .05 .01   -.005728242
1 3 16320 16454 33.09 33.61   -.02211686 -.00005646049  .35   .33  .12 .01    -.01005646
1 3 16320 16455 34.08 33.42    .02947958  -.0007907818   .2   .46 -.04 .01   -.010790782
1 3 16320 16456 34.25 34.12   .004975863  -.0033678166  .18   .32  .15 .01   -.013367817
1 3 16320 16457 35.19 34.25    .02707539   .0022653274  .25    .2  .06 .01   -.007734673
end
format %td eventdate
format %td date
label values companyid companyid
label def companyid 3 "ABN AMRO HOLDING", modify
In my dataset, I have 949 recommendations (an advice for a specific company on a specific eventdate) meaning I have have 949 panels: for each recommendation I have a year of pricing data of the respective stock following the recommendation of said stock.

Variable "ADVICE" represents the type of recommendation, "companyid" the recommended stock, "eventdate" is the date of the recommendation, "date" is the date for which the pricing and factor data is shown, "ret" is the daily log return of the stock, "marketret" is the daily log market return, rf is the riskfree rate and MOM, SMB, HML and Mkt_rf the factor data which will be used in the regressions.

Any help or ideas on how to form the portfolios and to compute its returns would be much appreciated, I thank you all in advance!

Kind regards,

Robert