Fama 5 factors model is r−Rf=α+β1(Rm−Rf)+β2⋅SMB+β3⋅HML+ β4 ⋅ RMW + β5 ⋅ CMA + e
where Rf is risk free return, (Rm−Rf) is premium return and Rm is market return, SMB is the "Small Minus Big" market capitalization risk factor. HML is the "High Minus Low" value premium risk factor, RMW is the ''Robust minus Weak'' profitability risk factor. CMA is the ''Conservative minus Aggresively' investment risk factor.
Please let mt detail my question. I have over over 1000 stocks and their yearly return ,yearly size, yearly book-to-market ratio (call it b/m), yearly profitability value (profit) and yearly investment value .
its methodology and what I want to do is:
RMW = 1/2 (Small Robust + Big Robust) - 1/2 (Small Weak + Big Weak).
CMA (Conservative Minus Aggressive) is the average return on the two conservative investment portfolios minus the average return on the two aggressive investment portfolios,
CMA = 1/2 (Small Conservative + Big Conservative) - 1/2 (Small Aggressive + Big Aggressive)
So, basically those are want I want to do.
Now I compute those manually although I use excel, this is really a huge amount of workload, so tired to do this. I am still in step 1 at first stage. Is anyone interesting in telling me how to achieve this through STATA?
My data looks like the following:
crossposted: https://quant.stackexchange.com/ques...ios-with-stata
where Rf is risk free return, (Rm−Rf) is premium return and Rm is market return, SMB is the "Small Minus Big" market capitalization risk factor. HML is the "High Minus Low" value premium risk factor, RMW is the ''Robust minus Weak'' profitability risk factor. CMA is the ''Conservative minus Aggresively' investment risk factor.
Please let mt detail my question. I have over over 1000 stocks and their yearly return ,yearly size, yearly book-to-market ratio (call it b/m), yearly profitability value (profit) and yearly investment value .
its methodology and what I want to do is:
- Construct the 5 factors as 2x3 using 6 equally weighted portfolios formed on size and B/M, the 6 equally weighted portfolios formed on size and profits and the 6 equally weighted portfolios formed on size and investments
- To construct the SMB, HML, RMW, and CMA factors, I want to sort into two size and three respective B/M, profits, and investment groups each year. Big stocks are those in the top 90% yearly size, and small stocks are those in the bottom 10%. The B/M, profits, and investment breakpoints are the 30th and 70th percentiles of respective ratios for the big stocks
SMB = average return on the nine small stock portfolios minus the average return on the nine big stock portfolios
SMB(B/M) = 1/3 (Small value + Small Neutral + Small Growth)
SMB(profits) = 1/3(small robust + small neutral + small weak) - 1/3(big robust + big neutral + big weak)
SMB(investments) = 1/3(small conservative + small neutral + small aggressive) - 1/3(big conservative + big neutral + big aggressive)
SMB = 1/3(SMBBM + SMBprofits SMBinvestments)
HML is the average return on the two value portfolios minus the average return on the two growth portfolios
HML = 1/2 (Small Value + Big Value)
- 1/2 (Small Growth + Big Growth).
RMW = 1/2 (Small Robust + Big Robust) - 1/2 (Small Weak + Big Weak).
CMA (Conservative Minus Aggressive) is the average return on the two conservative investment portfolios minus the average return on the two aggressive investment portfolios,
CMA = 1/2 (Small Conservative + Big Conservative) - 1/2 (Small Aggressive + Big Aggressive)
So, basically those are want I want to do.
Now I compute those manually although I use excel, this is really a huge amount of workload, so tired to do this. I am still in step 1 at first stage. Is anyone interesting in telling me how to achieve this through STATA?
My data looks like the following:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long firmid double year float(size BM profits investments) 5047 1966 8.989434 .2758487 .3733373 8.487084 5597 1966 5.670904 .4619963 .4015927 5.138976 6081 1966 6.936917 1.0625646 .24965106 7.491935 8551 1966 4.0239954 .8555347 .18384364 4.5174313 10983 1966 6.872267 .4317856 .28234044 6.95292 1078 1967 6.442712 .310929 .29705524 5.66608 1209 1967 5.308663 .55467165 .3036453 5.754158 1397 1967 3.7776885 1.0129733 .25924754 4.4986978 1414 1967 6.807433 .7044064 .3161646 7.118988 2435 1967 5.256106 .4477937 .27640146 4.975636 2991 1967 8.531097 .7736171 .1569048 8.577299 3170 1967 6.483671 .4308351 .3398067 6.243779 4040 1967 6.4163 .29091993 .28505445 5.44975 4060 1967 7.87118 .3560086 .3340697 7.555172 4087 1967 8.898063 .29654956 .379469 8.029791 4199 1967 6.192754 .6145301 .29132837 6.272122 4839 1967 8.675532 .7835172 .09970196 8.983038 5047 1967 9.070559 .26935583 .3665313 8.584328 5071 1967 6.484484 .3231619 .3719541 6.224756 5125 1967 4.7162895 .3866479 .3758476 4.3670654 5568 1967 5.660178 .8484411 .240282 6.27702 5597 1967 5.692617 .4740874 .3313259 5.329094 6104 1967 7.197984 .7738066 .18868434 7.29295 6730 1967 7.456137 .16159034 .3755758 5.920237 7063 1967 5.15637 .26193228 .331632 4.778552 7739 1967 5.694016 .882827 .1861031 5.865901 8215 1967 6.747763 .5122795 .20208962 6.915426 9667 1967 5.605176 .7082593 .20257004 5.682899 10190 1967 4.293953 .3946872 .3932487 4.2696977 10519 1967 7.219262 .21573205 .4930443 6.506766 10857 1967 7.993703 .562075 .29287618 8.035376 10983 1967 6.884759 .47122255 .3176695 7.123189 1045 1968 6.56291 .53647876 .3242966 7.25734 1078 1968 6.83005 .23176427 .329141 5.844703 1209 1968 5.288413 .6295648 .3002278 5.743644 1397 1968 4.0297947 .8419294 .2451539 4.646312 1414 1968 6.914986 .6691056 .3079258 7.198258 1794 1968 6.853618 .3208851 .38759765 6.602352 2316 1968 6.87217 .6346655 .17327642 6.930593 2435 1968 5.258457 .4910951 .303345 5.046002 2991 1968 8.670271 .7223451 .1528875 8.660357 3170 1968 6.587067 .4135994 .3230288 6.275327 3835 1968 6.754664 .7671866 .16304463 7.227482 4040 1968 6.289933 .3539621 .27653322 5.531411 4060 1968 7.765576 .4281658 .3223285 7.745955 4087 1968 8.941022 .30126455 .4455932 8.09843 4199 1968 6.49559 .5343718 .3397161 6.434065 4503 1968 9.735607 .58286905 .28414568 9.728324 5047 1968 9.048331 .29319733 .3519227 8.655871 5071 1968 6.403985 .4654331 .3496339 6.433583 5125 1968 5.163025 .29296684 .41898045 4.514337 5568 1968 6.004989 .6652396 .22293574 6.395762 5597 1968 5.909685 .397268 .3373649 5.337442 6081 1968 6.925948 1.1405088 .1774337 7.550753 6104 1968 7.403625 .6520171 .21070223 7.446345 6502 1968 6.122864 .6175537 .3209695 6.379614 7063 1968 5.798007 .17223245 .3434258 5.161076 7435 1968 8.6443615 .151911 .4344559 7.058672 7739 1968 6.068147 .6553814 .1894745 5.974064 8214 1968 6.346857 .3908263 .26125008 5.859076 8215 1968 7.060985 .4767436 .2342857 7.057726 8543 1968 6.565828 .4099832 .3465899 6.66772 8551 1968 4.634445 .6372877 .2095334 4.910447 8762 1968 8.278703 .28181222 .3403535 7.385098 9667 1968 5.778897 .654312 .20084496 5.781052 10016 1968 5.217306 .6595056 .2044608 5.192401 10190 1968 4.5900917 .3210852 .3607563 4.368181 10519 1968 7.101638 .27352118 .5740599 6.793132 10581 1968 6.056965 .7176358 .25971824 5.873525 10795 1968 6.674854 .6955235 .29122746 7.493745 10857 1968 7.914267 .6217688 .29159674 8.07359 10983 1968 6.676416 .6343378 .33977845 7.213621 11228 1968 4.919917 .6921756 .26839763 5.114395 1045 1969 6.435575 .6466864 .3062931 7.307067 1078 1969 6.93961 .2261898 .32917935 5.959997 1209 1969 5.249522 .7300214 .29689828 5.889293 1414 1969 6.586228 .9639692 .26948836 7.223749 1794 1969 6.55702 .4998141 .3403229 6.741006 2086 1969 6.690217 .12834859 .1935287 5.320162 2403 1969 7.688845 .1527779 .4225672 6.407136 2435 1969 5.496543 .42672575 .3102272 5.148081 3170 1969 6.473836 .4796143 .2932108 6.3637 3851 1969 6.531176 .3300984 .55810875 6.452467 3946 1969 5.27388 .22431025 .3528176 4.302875 4040 1969 6.164437 .4266514 .2653891 5.604374 4060 1969 7.637028 .5209601 .3180288 7.870844 4199 1969 6.543495 .5860513 .3416991 6.600559 4390 1969 3.757255 .3254207 .389152 3.2712026 5125 1969 5.161422 .38208315 .3923619 4.7048163 5568 1969 6.130436 .7346607 .20958787 6.506594 5597 1969 5.682165 .4926932 .2299529 5.32742 6066 1969 10.632246 .1273093 .5481468 8.907877 6104 1969 7.405817 .6744854 .2280687 7.542972 6502 1969 5.925242 .8261876 .3139357 6.540047 7063 1969 5.966887 .19922693 .2939452 5.504482 7435 1969 8.723453 .1600976 .4204961 7.203113 7739 1969 5.85014 .8340432 .1781812 6.015355 8214 1969 6.457884 .3805804 .289143 6.022532 8215 1969 6.90955 .5869568 .2858093 7.130624 8479 1969 7.069248 .25319684 .3638099 6.317575 end
crossposted: https://quant.stackexchange.com/ques...ios-with-stata
