Dear,
I am constructing a 2 month momentum strategy, which means buy the top 10% stocks at t0 with the highest average return at t-1 and t-2 and sell the bottom 10% stocks with the lowest average return at t-1 and t-2. I want to form portfolios consisting of the top 10% stocks with the highest 2 months prior return, hold them for 2 months and then sell them and again pick at t3 the stocks with the highest 2 months prios returns and so on. Being able to test if this strategy is lucrative I need for every portfolio the monthly average weighted returns.
So I need at the and someting like this:
Monthly average weighted returns
portfolio:1 2 3
top 10% middle 80% bottom 10%
10-6-2012: 4% 3% 1%
11-6-2012: 4.5% 4% 2%
......
I have managed to calculate the 2 months lagged returns for every stock at every date for the sample period 2012-2017. Here is a dataex:
Here stock stands for the stock id, MV=market value, P=price, retL2return_pct= 2mont lagged return. The total data consits of arround 300 stocks for the period 10-6-2012 - 10-6-2017
Maybe anyone could assist me with the furhter commands to construct the 10% top portfolios and 10% bottom portfolios construing each month
.
Kind regards
I am constructing a 2 month momentum strategy, which means buy the top 10% stocks at t0 with the highest average return at t-1 and t-2 and sell the bottom 10% stocks with the lowest average return at t-1 and t-2. I want to form portfolios consisting of the top 10% stocks with the highest 2 months prior return, hold them for 2 months and then sell them and again pick at t3 the stocks with the highest 2 months prios returns and so on. Being able to test if this strategy is lucrative I need for every portfolio the monthly average weighted returns.
So I need at the and someting like this:
Monthly average weighted returns
portfolio:1 2 3
top 10% middle 80% bottom 10%
10-6-2012: 4% 3% 1%
11-6-2012: 4.5% 4% 2%
......
I have managed to calculate the 2 months lagged returns for every stock at every date for the sample period 2012-2017. Here is a dataex:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int(date stock) double(MV P) float(month return_pct L2return_pct retL2return_pct bucket) 19333 1 96780.31 25.89 635 -4.461182 . . . 19364 1 99930.44 26.490000000000002 636 2.2650056 .57311887 2.9520695 . 19395 1 95818.56 25.400000000000002 637 -4.2913384 -4.461182 -.03807145 . 19423 1 96233.5 25.51 638 .43120345 2.2650056 -.8096237 . 19454 1 93259.13 24.555 639 -3.889228 -4.2913384 -.09370273 . 19484 1 99734.69 26.26 640 6.492764 .43120345 14.05731 . 19515 1 94227.63 24.810000000000002 641 -5.844418 -3.889228 .50271904 . 19545 1 93495.56 24.465 642 -1.410178 6.492764 -1.217192 . 19576 1 92597.5 24.23 643 -.9698721 -5.844418 -.8340515 . 19607 1 94317.19 24.68 644 1.8233387 -1.410178 -2.292985 . 19637 1 92887.81 24.060000000000002 645 -2.576891 -.9698721 1.6569393 . 19668 1 95841.19 24.825000000000003 646 3.081571 1.8233387 .6900706 . 19698 1 95281.38 24.68 647 -.58752024 -2.576891 -.7720042 . 19729 1 100607.6 25.810000000000002 648 4.378148 3.081571 .4207519 . 19760 1 98463.63 25.26 649 -2.1773555 -.58752024 2.7060094 . 19788 1 102264.2 26.235000000000003 650 3.7164094 4.378148 -.1511458 . 19819 1 105660.8 26.845000000000002 651 2.272304 -2.1773555 -2.0436072 . 19849 1 112489.7 28.580000000000002 652 6.070679 3.7164094 .6334795 . 19880 1 114153.5 29.045 653 1.600964 2.272304 -.2954446 . 19910 1 120510.2 30.485000000000003 654 4.7236347 6.070679 -.22189347 . 19941 1 120755.90000000001 30.585 655 .3269577 1.600964 -.7957745 . 19972 1 121534.90000000001 30.825000000000003 656 .7785888 4.7236347 -.8351716 . 20002 1 114538.5 29.16 657 -5.709877 .3269577 -18.463657 . 20033 1 108842.8 27.830000000000002 658 -4.779016 .7785888 -7.138048 . 20063 1 106488.90000000001 27.235000000000003 659 -2.1846888 -5.709877 -.6173843 . 20094 1 103551.6 26.515 660 -2.715444 -4.779016 -.4317984 . 20125 1 113759.5 29.205000000000002 661 9.210752 -2.1846888 -5.216047 . 20153 1 110060.90000000001 28.26 662 -3.343949 -2.715444 .2314557 . 20184 1 107763.1 27.67 663 -2.1322732 9.210752 -1.2314982 . 20214 1 109574.1 28.135 664 1.6527457 -3.343949 -1.4942497 . 20245 1 101960.1 26.18 665 -7.467533 -2.1322732 2.502146 . 20275 1 98251 25.335 666 -3.335307 1.6527457 -3.01804 . 20306 1 103641.5 26.725 667 5.201123 -7.467533 -1.696498 . 20337 1 86940.69 22.19 668 -20.437134 -3.335307 5.127512 . 20367 1 93304.25 23.67 669 6.25264 5.201123 .20217125 . 20398 1 95373.75 24.195 670 2.16987 -20.437134 -1.1061729 . 20428 1 87549.13 22.21 671 -8.937415 6.25264 -2.4293826 . 20459 1 82013.38 20.55 672 -8.077859 2.16987 -4.7227387 . 20490 1 80297.19 20.12 673 -2.137177 -8.937415 -.760873 . 20519 1 91496.69 21.735 674 7.430412 -8.077859 -1.919849 . 20550 1 88606.63 20.725 675 -4.873342 -2.137177 1.2802705 . 20580 1 95489.94 22.335 676 7.208417 7.430412 -.02987646 . 20611 1 93843.88 21.95 677 -1.7539864 -4.873342 -.6400855 . 20641 1 105508.6 24.39 678 10.0041 7.208417 .3878358 . 20672 1 97505.69 22.540000000000003 679 -8.207631 -1.7539864 3.679416 . 20703 1 96575.63 22.325000000000003 680 -.9630459 10.0041 -1.0962651 . 20733 1 100268.6 22.945 681 2.702114 -8.207631 -1.3292197 . 20764 1 99285.31 22.720000000000002 682 -.9903169 -.9630459 .02831748 . 20794 1 106299.1 24.325000000000003 683 6.59815 2.702114 1.4418476 . 20825 1 117033.7 26.425 684 7.94702 -.9903169 -9.024724 . 20856 1 111918.3 25.27 685 -4.570637 6.59815 -1.692715 . 20884 1 109881 24.810000000000002 686 -1.854091 7.94702 -1.2333064 . 20915 1 111469.6 24.900000000000002 687 .3614458 -4.570637 -1.07908 . 20945 1 109589.40000000001 24.48 688 -1.7156863 -1.854091 -.0746483 . 20976 1 107194.40000000001 23.945 689 -2.234287 .3614458 -7.181528 . 21006 1 105480.6 23.385 690 -2.3946974 -1.7156863 .3957665 . 21037 1 109472.6 24.27 691 3.646477 -2.234287 -2.632054 . 21068 1 105819 23.46 692 -3.452685 -2.3946974 .4418044 . 21098 1 116700.5 25.68 693 8.64486 3.646477 1.370743 . 19272 2 43519.51 28.09 633 . . . . 19303 2 44526.55 28.740000000000002 634 2.2616563 . . . 19333 2 45308.97 29.245 635 1.726791 . . . 19364 2 45749.66 29.395000000000003 636 .51029086 2.2616563 -.7743729 . 19395 2 45290.54 29.1 637 -1.0137457 1.726791 -1.587069 . 19423 2 47656.22 30.62 638 4.964076 .51029086 8.727934 . 19454 2 48667.85 31.270000000000003 639 2.0786695 -1.0137457 -3.050484 . 19484 2 50792.32 32.635000000000005 640 4.1826262 4.964076 -.1574209 . 19515 2 47508.36 30.525000000000002 641 -6.912367 2.0786695 -4.3253803 . 19545 2 47850.78 30.745 642 .7155635 4.1826262 -.8289201 . 19576 2 47181.54 30.315 643 -1.4184397 -6.912367 -.7947968 . 19607 2 44644.64 28.685000000000002 644 -5.682413 .7155635 -8.941172 . 19637 2 43282.8 27.810000000000002 645 -3.14635 -1.4184397 1.218177 . 19668 2 44979.28 28.900000000000002 646 3.771626 -5.682413 -1.6637368 . 19698 2 44963.71 28.89 647 -.034614053 -3.14635 -.9889987 . 19729 2 45078.450000000004 28.865000000000002 648 -.08661008 3.771626 -1.0229636 . 19760 2 42946.75 27.5 649 -4.963636 -.034614053 142.39946 . 19788 2 44469.37 28.475 650 3.424056 -.08661008 -40.53416 . 19819 2 46538.63 29.8 651 4.4463086 -4.963636 -1.8957765 . 19849 2 47327.270000000004 30.305 652 1.6663917 3.424056 -.5133282 . 19880 2 49326.24 31.585 653 4.0525565 4.4463086 -.08855708 . 19910 2 50388.21 32.265 654 2.1075468 1.6663917 .2647367 . 19941 2 47374.13 30.335 655 -6.362288 4.0525565 -2.5699444 . 19972 2 50747.43 32.495000000000005 656 6.647176 2.1075468 2.1539874 . 20002 2 48092.53 30.795 657 -5.520377 -6.362288 -.13232839 . 20033 2 48514.21 31.065 658 .8691453 6.647176 -.8692459 . 20063 2 52301.33 33.49 659 7.240967 -5.520377 -2.3116798 . 20094 2 49263.82 31.545 660 -6.165795 .8691453 -8.0940895 . 20125 2 57892.21 37.07 661 14.904235 7.240967 1.058321 . 20153 2 61866.72 39.615 662 6.424334 -6.165795 -2.0419312 . 20184 2 61491.93 39.375 663 -.6095238 14.904235 -1.040896 . 20214 2 59110.32 37.85 664 -4.0290623 6.424334 -1.6271564 . 20245 2 58251.39 37.300000000000004 665 -1.4745308 -.6095238 1.419152 . 20275 2 57782.880000000005 37 666 -.8108108 -4.0290623 -.7987594 . 20306 2 66083.25 42.315000000000005 667 12.560557 -1.4745308 -9.518342 . 20337 2 54597.020000000004 34.96 668 -21.03833 -.8108108 24.947273 . 20367 2 58446.6 37.425000000000004 669 6.586506 12.560557 -.4756199 . 20398 2 64427.91 41.255 670 9.283723 -21.03833 -1.4412766 . 20428 2 61937.01 39.660000000000004 671 -4.021684 6.586506 -1.6105944 . end format %tdnn/dd/CCYY date format %tm month
Here stock stands for the stock id, MV=market value, P=price, retL2return_pct= 2mont lagged return. The total data consits of arround 300 stocks for the period 10-6-2012 - 10-6-2017
Maybe anyone could assist me with the furhter commands to construct the 10% top portfolios and 10% bottom portfolios construing each month
.
Kind regards
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