Dear Statalist Community,
I have a problem calculating the number trading days around event dates, more specific around the dates of quarterly earning announcements. I use the current Stata version, Stata 15.1.
In general I have a data set of panel data, which is xtset by xtset permno date. Permno and cik are both variables, that allow me to identify my firms, while date describes the specific calendar date, prmean the average stock price on the given date and earningsannouncement corresponds to a dummy variable that equals 1("Yes"), if the firm announced its quarterly earnings on the given date and 0("No") otherwise.
In the below example you can find a part of my dataset only focussing on one specific firm (cik = 350563).
For an event study I want to calculate the trading days around the event dates of the given earning announcements. In line with a detailed description on the homepage of the the library of the Princeton University (https://dss.princeton.edu/online_hel...ventstudy.html) I did this with the following code:
This gives me now, as I wanted, a variable diff, that calculates with negative values (-1 to -71) the trading days before the first earnings announcement and with positive values the trading days after the first earnings announcement, as you can see below.
However, this is actually the problem. The variable only calculates the trading days around the first earning announcement on April 15th in 1993. What I actually want is a variable that counts the trading days before and after each earning announcement, such that for the second earning announcement on July 6th 1993 the variable diff has a value of 0 again. I know that this is in such a sense not possible, as the 63rd trading day after the first earning announcement (diff = +63) is also the first day before the second announcement.
Therefore, I thought about a variable that counts just the last 10 trading days before an announcement and the following 20 trading days after an announcement, as this will be the maximum time horizon I need for my event study. However I didn't came up with a solution to that. In general I'm searching for an approach that allows me to generalize the approach of the Princeton University to panel data, where we have multiple event dates for every individual firm.
Any recommendations, approaches and solutions are highly appreciated.
Thank you very much in advance Statalist community.
Best regards
Philipp
I have a problem calculating the number trading days around event dates, more specific around the dates of quarterly earning announcements. I use the current Stata version, Stata 15.1.
In general I have a data set of panel data, which is xtset by xtset permno date. Permno and cik are both variables, that allow me to identify my firms, while date describes the specific calendar date, prmean the average stock price on the given date and earningsannouncement corresponds to a dummy variable that equals 1("Yes"), if the firm announced its quarterly earnings on the given date and 0("No") otherwise.
In the below example you can find a part of my dataset only focussing on one specific firm (cik = 350563).
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long(cik permno) float date double prmean float earningsannouncement 350563 37161 12057 41.546542553191486 0 350563 37161 12058 41.458333333333336 0 350563 37161 12059 41.44202898550725 0 350563 37161 12060 41.18541666666667 0 350563 37161 12061 40.783203125 0 350563 37161 12064 40.72222222222222 0 350563 37161 12065 40.94763513513514 0 350563 37161 12066 41.213235294117645 0 350563 37161 12067 41.26865671641791 0 350563 37161 12068 41.38257575757576 0 350563 37161 12071 41.685267857142854 0 350563 37161 12072 41.934859154929576 0 350563 37161 12073 42.15573770491803 0 350563 37161 12074 41.96082089552239 0 350563 37161 12075 42.23134328358209 0 350563 37161 12078 41.823113207547166 0 350563 37161 12079 41.933035714285715 0 350563 37161 12080 41.8235294117647 0 350563 37161 12081 41.8445945945946 0 350563 37161 12082 41.816287878787875 0 350563 37161 12085 42.30357142857143 0 350563 37161 12086 42.57258064516129 0 350563 37161 12087 42.99013157894737 0 350563 37161 12088 43.13771186440678 0 350563 37161 12089 42.93614130434783 0 350563 37161 12092 43.1875 0 350563 37161 12093 43.39473684210526 0 350563 37161 12094 43.438380281690144 0 350563 37161 12095 43.343283582089555 0 350563 37161 12096 43.364754098360656 0 350563 37161 12100 43.31505102040816 0 350563 37161 12101 43.1764705882353 0 350563 37161 12102 43.31923076923077 0 350563 37161 12103 43.39715189873418 0 350563 37161 12106 43.85227272727273 0 350563 37161 12107 44.51151315789474 0 350563 37161 12108 44.96683673469388 0 350563 37161 12109 44.88901869158879 0 350563 37161 12110 44.878125 0 350563 37161 12113 45.06849315068493 0 350563 37161 12114 45.51013513513514 0 350563 37161 12115 45.6468023255814 0 350563 37161 12116 45.536144578313255 0 350563 37161 12117 45.364197530864196 0 350563 37161 12120 45.4609375 0 350563 37161 12121 45.625 0 350563 37161 12122 45.4078125 0 350563 37161 12123 45.422348484848484 0 350563 37161 12124 45.30283505154639 0 350563 37161 12127 44.90396341463415 0 350563 37161 12128 44.861301369863014 0 350563 37161 12129 44.9375 0 350563 37161 12130 45.036764705882355 0 350563 37161 12131 44.85339506172839 0 350563 37161 12134 44.57303370786517 0 350563 37161 12135 44.726027397260275 0 350563 37161 12136 44.81707317073171 0 350563 37161 12137 44.76411290322581 0 350563 37161 12138 44.929245283018865 0 350563 37161 12141 45.04094827586207 0 350563 37161 12142 45.27254098360656 0 350563 37161 12143 45.609848484848484 0 350563 37161 12144 45.669444444444444 0 350563 37161 12145 45.29891304347826 0 350563 37161 12148 45.467948717948715 0 350563 37161 12149 45.54967948717949 0 350563 37161 12150 45.71913580246913 0 350563 37161 12151 45.916666666666664 0 350563 37161 12155 46.467171717171716 0 350563 37161 12156 46.66029411764706 0 350563 37161 12157 46.5953125 0 350563 37161 12158 46.702651515151516 1 350563 37161 12159 47.35394736842105 0 350563 37161 12162 47.16470588235294 0 350563 37161 12163 46.985632183908045 0 350563 37161 12164 46.7373595505618 0 350563 37161 12165 46.53382352941176 0 350563 37161 12166 46.34487951807229 0 350563 37161 12169 45.72205882352941 0 350563 37161 12170 45.57471264367816 0 350563 37161 12171 45.49050632911393 0 350563 37161 12172 45.45192307692308 0 350563 37161 12173 45.48007246376812 0 350563 37161 12176 45.669642857142854 0 350563 37161 12177 45.71818181818182 0 350563 37161 12178 46.47844827586207 0 350563 37161 12179 46.684859154929576 0 350563 37161 12180 46.70703125 0 350563 37161 12183 46.682870370370374 0 350563 37161 12184 46.64558823529412 0 350563 37161 12185 46.54049295774648 0 350563 37161 12186 46.251543209876544 0 350563 37161 12187 45.86693548387097 0 350563 37161 12190 45.45684523809524 0 350563 37161 12191 44.793432203389834 0 350563 37161 12192 44.08522727272727 0 350563 37161 12193 44.8125 0 350563 37161 12194 44.693359375 0 350563 37161 12197 44.582770270270274 0 350563 37161 12198 44.926339285714285 0 350563 37161 12199 45.58132530120482 0 350563 37161 12200 45.92460317460318 0 350563 37161 12201 45.68333333333333 0 350563 37161 12205 46.354166666666664 0 350563 37161 12206 46.791666666666664 0 350563 37161 12207 46.62755102040816 0 350563 37161 12208 46.55846774193548 0 350563 37161 12211 46.525862068965516 0 350563 37161 12212 45.89673913043478 0 350563 37161 12213 46.110169491525426 0 350563 37161 12214 46.22767857142857 0 350563 37161 12215 46.099489795918366 0 350563 37161 12218 45.77291666666667 0 350563 37161 12219 45.45696721311475 0 350563 37161 12220 45.51973684210526 0 350563 37161 12221 45.933098591549296 0 350563 37161 12222 46.06578947368421 0 350563 37161 12225 46.19444444444444 0 350563 37161 12226 46.150862068965516 0 350563 37161 12227 46.28365384615385 0 350563 37161 12228 46.424479166666664 0 350563 37161 12229 46.3578431372549 0 350563 37161 12232 46.80172413793103 0 350563 37161 12233 47.28846153846154 0 350563 37161 12234 47.28448275862069 0 350563 37161 12235 47.301923076923075 0 350563 37161 12236 47.432065217391305 0 350563 37161 12240 47.61830357142857 0 350563 37161 12241 47.690140845070424 0 350563 37161 12242 47.55227272727273 0 350563 37161 12243 47.666666666666664 0 350563 37161 12246 47.639150943396224 0 350563 37161 12247 47.66041666666667 0 350563 37161 12248 47.69444444444444 0 350563 37161 12249 47.8125 0 350563 37161 12250 47.97149122807018 1 350563 37161 12253 47.99583333333333 0 350563 37161 12254 48.107894736842105 0 350563 37161 12255 48.544117647058826 0 350563 37161 12256 48.73550724637681 0 350563 37161 12257 48.57258064516129 0 350563 37161 12260 48.06578947368421 0 350563 37161 12261 48.0546875 0 350563 37161 12262 48.06702898550725 0 350563 37161 12263 48.313291139240505 0 350563 37161 12264 48.30288461538461 0 350563 37161 12267 48.395270270270274 0 350563 37161 12268 48.51315789473684 0 350563 37161 12269 48.91235632183908 0 350563 37161 12270 49.61918604651163 0 350563 37161 12271 49.56349206349206 0 350563 37161 12274 49.62797619047619 0 350563 37161 12275 49.625 0 350563 37161 12276 49.445402298850574 0 350563 37161 12277 49.365 0 350563 37161 12278 49.441326530612244 0 350563 37161 12281 49.1765625 0 350563 37161 12282 48.72560975609756 0 350563 37161 12283 48.869791666666664 0 350563 37161 12284 49.05769230769231 0 350563 37161 12285 49.485416666666666 0 350563 37161 12288 49.45454545454545 0 350563 37161 12289 49.4365671641791 0 350563 37161 12290 49.52256944444444 0 350563 37161 12291 49.464622641509436 0 350563 37161 12292 49.43103448275862 0 350563 37161 12295 49.44556451612903 0 350563 37161 12296 25.1875 0 350563 37161 12297 25.1453488372093 0 350563 37161 12298 25.420516304347824 0 350563 37161 12299 25.63855421686747 0 350563 37161 12303 25.66023489932886 0 350563 37161 12304 25.480132450331126 0 350563 37161 12305 25.389084507042252 0 350563 37161 12306 25.591216216216218 0 350563 37161 12309 25.780133928571427 0 350563 37161 12310 25.610491071428573 0 350563 37161 12311 25.355978260869566 0 350563 37161 12312 25.498062015503876 0 350563 37161 12313 25.31030701754386 0 350563 37161 12316 25.259146341463413 0 350563 37161 12317 25.177455357142858 0 350563 37161 12318 25.11697247706422 0 350563 37161 12319 25.272959183673468 0 350563 37161 12320 25.235759493670887 0 350563 37161 12323 25.623809523809523 0 350563 37161 12324 25.825 0 350563 37161 12325 25.741935483870968 0 350563 37161 12326 25.558673469387756 0 350563 37161 12327 25.533088235294116 0 350563 37161 12330 25.571691176470587 0 350563 37161 12331 25.508223684210527 0 350563 37161 12332 25.362068965517242 0 350563 37161 12333 25.00978260869565 0 350563 37161 12334 24.968373493975903 0 350563 37161 12337 25.099609375 0 350563 37161 12338 25.180059523809526 0 350563 37161 12339 25.098333333333333 0 350563 37161 12340 25.32528409090909 0 350563 37161 12341 25.413194444444443 1 350563 37161 12344 25.193055555555556 0 350563 37161 12345 25.190384615384616 0 350563 37161 12346 25.214285714285715 0 350563 37161 12347 24.99418604651163 0 350563 37161 12348 24.78188775510204 0 350563 37161 12351 24.05827067669173 0 350563 37161 12352 23.95420792079208 0 350563 37161 12353 24.37087912087912 0 350563 37161 12354 24.544303797468356 0 350563 37161 12355 24.545833333333334 0 350563 37161 12358 24.63653846153846 0 350563 37161 12359 24.348863636363635 0 350563 37161 12360 22.91764018691589 0 350563 37161 12361 22.7734375 0 350563 37161 12362 22.53611111111111 0 350563 37161 12365 22.761607142857144 0 350563 37161 12366 22.8640873015873 0 350563 37161 12367 23.049778761061948 0 350563 37161 12368 23.011494252873565 0 350563 37161 12369 22.851136363636364 0 350563 37161 12372 22.593023255813954 0 350563 37161 12373 22.569166666666668 0 350563 37161 12374 23.225235849056602 0 350563 37161 12375 23.166666666666668 0 350563 37161 12376 22.96276595744681 0 350563 37161 12379 22.875 0 350563 37161 12380 22.96638655462185 0 350563 37161 12381 23.32894736842105 0 350563 37161 12383 23.404761904761905 0 350563 37161 12386 23.287760416666668 0 350563 37161 12387 23.327272727272728 0 350563 37161 12388 22.951923076923077 0 350563 37161 12389 22.845 0 350563 37161 12390 22.81140350877193 0 350563 37161 12393 22.904605263157894 0 350563 37161 12394 22.77195945945946 0 350563 37161 12395 22.76909722222222 0 350563 37161 12396 22.734154929577464 0 350563 37161 12397 22.567028985507246 0 350563 37161 12400 22.55034722222222 0 350563 37161 12401 22.291208791208792 0 350563 37161 12402 22.361280487804876 0 350563 37161 12403 22.573943661971832 0 350563 37161 12404 22.741438356164384 0 350563 37161 12407 22.83776595744681 0 350563 37161 12408 22.681318681318682 0 350563 37161 12409 22.776785714285715 0 350563 37161 12410 22.963541666666668 0 350563 37161 12414 22.91796875 0 350563 37161 12415 22.865384615384617 0 350563 37161 12416 22.91231343283582 0 350563 37161 12417 22.819444444444443 0 350563 37161 12418 22.78409090909091 0 350563 37161 12421 22.404605263157894 0 350563 37161 12422 22.28030303030303 0 350563 37161 12423 22.107905982905983 0 350563 37161 12424 22.03429203539823 0 350563 37161 12425 21.727272727272727 0 350563 37161 12428 21.625954198473284 0 350563 37161 12429 21.439732142857142 0 350563 37161 12430 21.308189655172413 0 350563 37161 12431 21.194318181818183 0 350563 37161 12432 20.95612582781457 0 350563 37161 12435 20.734110169491526 1 350563 37161 12436 20.787852112676056 0 350563 37161 12437 20.712786259541986 0 350563 37161 12438 20.68846153846154 0 350563 37161 12439 20.524590163934427 0 350563 37161 12442 20.209033613445378 0 350563 37161 12443 20.112723214285715 0 350563 37161 12444 20.1706008583691 0 350563 37161 12445 20.9779792746114 0 350563 37161 12446 20.921875 0 350563 37161 12449 20.755172413793105 0 350563 37161 12450 20.549212598425196 0 350563 37161 12451 20.198426573426573 0 350563 37161 12452 20.45 0 350563 37161 12453 20.887218045112782 0 350563 37161 12456 20.65 0 350563 37161 12457 20.61413043478261 0 350563 37161 12458 20.716535433070867 0 350563 37161 12459 20.721217105263158 0 350563 37161 12460 20.698529411764707 0 350563 37161 12463 20.645833333333332 0 350563 37161 12464 20.796677215189874 0 350563 37161 12465 20.652397260273972 0 350563 37161 12466 20.377777777777776 0 350563 37161 12467 20.159375 0 350563 37161 12471 20.113065326633166 0 350563 37161 12472 20.013392857142858 0 350563 37161 12473 19.84452736318408 0 350563 37161 12474 20.104545454545455 0 350563 37161 12477 20.220833333333335 0 350563 37161 12478 20.214139344262296 0 350563 37161 12479 19.833896396396398 0 350563 37161 12480 19.85763888888889 0 350563 37161 12481 20.074380165289256 0 350563 37161 12484 20.403508771929825 0 350563 37161 12485 20.285194174757283 0 350563 37161 12486 20.30909090909091 0 end format %td date label values earningsannouncement earningsannouncementlabel label def earningsannouncementlabel 0 "NO", modify label def earningsannouncementlabel 1 "YES", modify
For an event study I want to calculate the trading days around the event dates of the given earning announcements. In line with a detailed description on the homepage of the the library of the Princeton University (https://dss.princeton.edu/online_hel...ventstudy.html) I did this with the following code:
Code:
// Define a variable for the date of the earnings announcements generate earnings_date = date if earningsannouncement == 1 format earnings_date %td // Calculate trading days around the date of the earnings announcements sort cik date by cik: gen datenum = _n by cik: gen target = datenum if date == earnings_date egen td = min(target), by(cik) drop target gen diff = datenum-td
However, this is actually the problem. The variable only calculates the trading days around the first earning announcement on April 15th in 1993. What I actually want is a variable that counts the trading days before and after each earning announcement, such that for the second earning announcement on July 6th 1993 the variable diff has a value of 0 again. I know that this is in such a sense not possible, as the 63rd trading day after the first earning announcement (diff = +63) is also the first day before the second announcement.
Therefore, I thought about a variable that counts just the last 10 trading days before an announcement and the following 20 trading days after an announcement, as this will be the maximum time horizon I need for my event study. However I didn't came up with a solution to that. In general I'm searching for an approach that allows me to generalize the approach of the Princeton University to panel data, where we have multiple event dates for every individual firm.
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long(cik permno) float date double prmean float(earningsannouncement earnings_date datenum td diff) 350563 37161 12057 41.546542553191486 0 . 1 72 -71 350563 37161 12058 41.458333333333336 0 . 2 72 -70 350563 37161 12059 41.44202898550725 0 . 3 72 -69 350563 37161 12060 41.18541666666667 0 . 4 72 -68 350563 37161 12061 40.783203125 0 . 5 72 -67 350563 37161 12064 40.72222222222222 0 . 6 72 -66 350563 37161 12065 40.94763513513514 0 . 7 72 -65 350563 37161 12066 41.213235294117645 0 . 8 72 -64 350563 37161 12067 41.26865671641791 0 . 9 72 -63 350563 37161 12068 41.38257575757576 0 . 10 72 -62 350563 37161 12071 41.685267857142854 0 . 11 72 -61 350563 37161 12072 41.934859154929576 0 . 12 72 -60 350563 37161 12073 42.15573770491803 0 . 13 72 -59 350563 37161 12074 41.96082089552239 0 . 14 72 -58 350563 37161 12075 42.23134328358209 0 . 15 72 -57 350563 37161 12078 41.823113207547166 0 . 16 72 -56 350563 37161 12079 41.933035714285715 0 . 17 72 -55 350563 37161 12080 41.8235294117647 0 . 18 72 -54 350563 37161 12081 41.8445945945946 0 . 19 72 -53 350563 37161 12082 41.816287878787875 0 . 20 72 -52 350563 37161 12085 42.30357142857143 0 . 21 72 -51 350563 37161 12086 42.57258064516129 0 . 22 72 -50 350563 37161 12087 42.99013157894737 0 . 23 72 -49 350563 37161 12088 43.13771186440678 0 . 24 72 -48 350563 37161 12089 42.93614130434783 0 . 25 72 -47 350563 37161 12092 43.1875 0 . 26 72 -46 350563 37161 12093 43.39473684210526 0 . 27 72 -45 350563 37161 12094 43.438380281690144 0 . 28 72 -44 350563 37161 12095 43.343283582089555 0 . 29 72 -43 350563 37161 12096 43.364754098360656 0 . 30 72 -42 350563 37161 12100 43.31505102040816 0 . 31 72 -41 350563 37161 12101 43.1764705882353 0 . 32 72 -40 350563 37161 12102 43.31923076923077 0 . 33 72 -39 350563 37161 12103 43.39715189873418 0 . 34 72 -38 350563 37161 12106 43.85227272727273 0 . 35 72 -37 350563 37161 12107 44.51151315789474 0 . 36 72 -36 350563 37161 12108 44.96683673469388 0 . 37 72 -35 350563 37161 12109 44.88901869158879 0 . 38 72 -34 350563 37161 12110 44.878125 0 . 39 72 -33 350563 37161 12113 45.06849315068493 0 . 40 72 -32 350563 37161 12114 45.51013513513514 0 . 41 72 -31 350563 37161 12115 45.6468023255814 0 . 42 72 -30 350563 37161 12116 45.536144578313255 0 . 43 72 -29 350563 37161 12117 45.364197530864196 0 . 44 72 -28 350563 37161 12120 45.4609375 0 . 45 72 -27 350563 37161 12121 45.625 0 . 46 72 -26 350563 37161 12122 45.4078125 0 . 47 72 -25 350563 37161 12123 45.422348484848484 0 . 48 72 -24 350563 37161 12124 45.30283505154639 0 . 49 72 -23 350563 37161 12127 44.90396341463415 0 . 50 72 -22 350563 37161 12128 44.861301369863014 0 . 51 72 -21 350563 37161 12129 44.9375 0 . 52 72 -20 350563 37161 12130 45.036764705882355 0 . 53 72 -19 350563 37161 12131 44.85339506172839 0 . 54 72 -18 350563 37161 12134 44.57303370786517 0 . 55 72 -17 350563 37161 12135 44.726027397260275 0 . 56 72 -16 350563 37161 12136 44.81707317073171 0 . 57 72 -15 350563 37161 12137 44.76411290322581 0 . 58 72 -14 350563 37161 12138 44.929245283018865 0 . 59 72 -13 350563 37161 12141 45.04094827586207 0 . 60 72 -12 350563 37161 12142 45.27254098360656 0 . 61 72 -11 350563 37161 12143 45.609848484848484 0 . 62 72 -10 350563 37161 12144 45.669444444444444 0 . 63 72 -9 350563 37161 12145 45.29891304347826 0 . 64 72 -8 350563 37161 12148 45.467948717948715 0 . 65 72 -7 350563 37161 12149 45.54967948717949 0 . 66 72 -6 350563 37161 12150 45.71913580246913 0 . 67 72 -5 350563 37161 12151 45.916666666666664 0 . 68 72 -4 350563 37161 12155 46.467171717171716 0 . 69 72 -3 350563 37161 12156 46.66029411764706 0 . 70 72 -2 350563 37161 12157 46.5953125 0 . 71 72 -1 350563 37161 12158 46.702651515151516 1 12158 72 72 0 350563 37161 12159 47.35394736842105 0 . 73 72 1 350563 37161 12162 47.16470588235294 0 . 74 72 2 350563 37161 12163 46.985632183908045 0 . 75 72 3 350563 37161 12164 46.7373595505618 0 . 76 72 4 350563 37161 12165 46.53382352941176 0 . 77 72 5 350563 37161 12166 46.34487951807229 0 . 78 72 6 350563 37161 12169 45.72205882352941 0 . 79 72 7 350563 37161 12170 45.57471264367816 0 . 80 72 8 350563 37161 12171 45.49050632911393 0 . 81 72 9 350563 37161 12172 45.45192307692308 0 . 82 72 10 350563 37161 12173 45.48007246376812 0 . 83 72 11 350563 37161 12176 45.669642857142854 0 . 84 72 12 350563 37161 12177 45.71818181818182 0 . 85 72 13 350563 37161 12178 46.47844827586207 0 . 86 72 14 350563 37161 12179 46.684859154929576 0 . 87 72 15 350563 37161 12180 46.70703125 0 . 88 72 16 350563 37161 12183 46.682870370370374 0 . 89 72 17 350563 37161 12184 46.64558823529412 0 . 90 72 18 350563 37161 12185 46.54049295774648 0 . 91 72 19 350563 37161 12186 46.251543209876544 0 . 92 72 20 350563 37161 12187 45.86693548387097 0 . 93 72 21 350563 37161 12190 45.45684523809524 0 . 94 72 22 350563 37161 12191 44.793432203389834 0 . 95 72 23 350563 37161 12192 44.08522727272727 0 . 96 72 24 350563 37161 12193 44.8125 0 . 97 72 25 350563 37161 12194 44.693359375 0 . 98 72 26 350563 37161 12197 44.582770270270274 0 . 99 72 27 350563 37161 12198 44.926339285714285 0 . 100 72 28 350563 37161 12199 45.58132530120482 0 . 101 72 29 350563 37161 12200 45.92460317460318 0 . 102 72 30 350563 37161 12201 45.68333333333333 0 . 103 72 31 350563 37161 12205 46.354166666666664 0 . 104 72 32 350563 37161 12206 46.791666666666664 0 . 105 72 33 350563 37161 12207 46.62755102040816 0 . 106 72 34 350563 37161 12208 46.55846774193548 0 . 107 72 35 350563 37161 12211 46.525862068965516 0 . 108 72 36 350563 37161 12212 45.89673913043478 0 . 109 72 37 350563 37161 12213 46.110169491525426 0 . 110 72 38 350563 37161 12214 46.22767857142857 0 . 111 72 39 350563 37161 12215 46.099489795918366 0 . 112 72 40 350563 37161 12218 45.77291666666667 0 . 113 72 41 350563 37161 12219 45.45696721311475 0 . 114 72 42 350563 37161 12220 45.51973684210526 0 . 115 72 43 350563 37161 12221 45.933098591549296 0 . 116 72 44 350563 37161 12222 46.06578947368421 0 . 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49.1765625 0 . 157 72 85 350563 37161 12282 48.72560975609756 0 . 158 72 86 350563 37161 12283 48.869791666666664 0 . 159 72 87 350563 37161 12284 49.05769230769231 0 . 160 72 88 350563 37161 12285 49.485416666666666 0 . 161 72 89 350563 37161 12288 49.45454545454545 0 . 162 72 90 350563 37161 12289 49.4365671641791 0 . 163 72 91 350563 37161 12290 49.52256944444444 0 . 164 72 92 350563 37161 12291 49.464622641509436 0 . 165 72 93 350563 37161 12292 49.43103448275862 0 . 166 72 94 350563 37161 12295 49.44556451612903 0 . 167 72 95 350563 37161 12296 25.1875 0 . 168 72 96 350563 37161 12297 25.1453488372093 0 . 169 72 97 350563 37161 12298 25.420516304347824 0 . 170 72 98 350563 37161 12299 25.63855421686747 0 . 171 72 99 350563 37161 12303 25.66023489932886 0 . 172 72 100 end format %td date format %td earnings_date label values earningsannouncement earningsannouncementlabel label def earningsannouncementlabel 0 "NO", modify label def earningsannouncementlabel 1 "YES", modify
Thank you very much in advance Statalist community.
Best regards
Philipp
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