How to calculate equal and value weighted portfolio(1-10) monthly raw returns-also report Newey t-statistics

[Peter Eze]

Dear Stata Friends, I have 10 portfolios formed every month by sorting stocks based on MAX. After the portfolios are formed, I need to calculate an equal and value weighted average return for each of the10 portfolios and also report the Newey-West t-statistics for each of the equal and value weighted portfolio returns. I actually tried using these stata codes below, however, I doubt I used them correctly:

bys period: egen portfo = xtile(max), nq(10)
bys period: egen  w_ret = wtmean(ret), weight(mv) 
collapse (mean) ret w_ret, by(period portfo)
reshape wide ret w_ret,i(period) j(portfo)

If I'm not mistaken, I think use of the collapse command

collapse (mean) ret, by(portfo)

should compute the equal weighted average returns for each of the portfolio, but I am confused on how to generate its t-statistics and the value weighted returns for each of the portfolios as well. Please I need your help on how to get this correctly done, thanks.

* Example generated by -dataex-. To install: ssc install dataex
clear
input double permno float period double ret float(max mv portfo)
10001 493  -.012658228166401386  .03947368      24414  2
10001 494    .03846153989434242  .04934211      25090  4
10001 495   -.02500000037252903 .032894738   24462.75  2
10001 496    .09743587672710419  .04081629    26846.3  3
10001 497    .11495330929756165 .071428575    29653.4  6
10001 498   .025423744693398476  .13043478    30407.3  9
10001 499   .020661156624555588  .13732716   31035.55  9
10001 500  -.021862395107746124  .03250003    30090.1  3
10001 501   -.03347277268767357  .03076916    29082.9  2
10001 502  -.004329020623117685  .06023421      29256  4
10001 503   .006956504657864571   .0258467    29380.7  2
10001 504  -.013100403361022472 .022093235    28995.8  2
10001 505  -.053097378462553024  .02235129    27488.3  2
10001 506  -.015887869521975517  .04702079    26738.4  5
10001 507  -.043269213289022446 .034313764   25581.45  3
10001 508   .014824124984443188  .02739604   25960.67  2
10001 509  -.024015802890062332  .04205369   25019.28  4
10001 510   -.09979426115751267  .03294372    22522.5  3
10001 511    .01714281365275383  .04891207    22908.6  3
10001 512 -.0016853498527780175  .05555556      22540  5
10001 513   -.01942858099937439  .02857143   22187.88  3
10001 514   -.02389276586472988 .027348455 -21682.875  2
10001 515   -.10614927113056183 .030864196  19046.441  3
10001 516    .14814308285713196 .027227756   21868.04  3
10001 517   .035545047372579575  .08828733   22645.34  8
10001 518    -.1092676967382431 .035792366   19836.45  5
10001 519   -.32418301701545715  .05675676   13405.81  6
10001 520     .6324950456619263 .017218495   21893.36  1
10001 521    -.2879146337509155  .17886177   15595.95 10
10001 522    .01830277033150196  .05405407    15881.4  6
10001 523    .08660134673118591  .06354517   17256.75  7
10001 524    .03759398311376572  .03983364    17905.5  5
10001 525    -.1304347962141037 .066478044      15570  8
10001 526  -.005000034812837839  .04962405   15492.15  6
10001 527 -.0033500806894153357 .025000015    15446.2  3
10001 528   .010084104724228382  .03144753   15601.96  4
10001 529    .07986681163311005   .1008404   16848.04  9
10001 530    .11710327863693237 .030995116    18835.5  4
10001 531   -.01241381373256445  .06213064   18601.68  7
10001 532   -.07960889488458633  .15766422   17120.82 10
10001 533   .010622108355164528   .0431655   17309.34  6
10001 534   .051051076501607895  .06531883      18193  8
10001 535   .004285744391381741  .06015039   18270.97  8
10001 536   -.13229022920131683  .02478135    15853.9  3
10001 537   -.05573765188455582  .03007516   14970.24  4
10001 538   .031249968335032463  .05132743   15438.06  7
10001 539    .16161616146564484  .04347826    17933.1  6
10001 540   -.04057973995804787  .09830507    17178.9  9
10001 541   -.04516613110899925  .05384614   16428.28  8
10001 542    .12482202798128128  .06976749   18663.75  9
10001 543   -.07468356192111969   .1572701  17269.875 10
10001 544    .21903032064437866  .06495473    21052.5  8
10001 545     .1284288913011551   .1785041   26362.65 10
10001 546   .009944767691195011  .18452384   26624.82 10
10001 547    .03938727080821991   .0883978    27673.5  9
10001 548    .21157896518707275  .19047624   33528.63 10
10001 549   -.11903561651706696  .18937197   29421.01 10
10001 550    -.0593966506421566   .0809312      27683  9
10001 551   .021052611991763115  .05022826      28421  7
10001 552   -.01649484969675541  .03578949    27805.7  6
10001 553  -.010537347756326199  .05081084   27522.09  7
10001 554    .17039397358894348  .02423609   32222.68  4
10001 555   -.09463147819042206  .04666664    29173.4  7
10001 556  -.010452263057231903   .1187308   28633.91 10
10001 557   -.07638739794492722 .034410834   26464.68  5
10001 558    .15741683542728424  .04000001   30630.67  5
10001 559    .12357395142316818 .064779095   34122.42  8
10001 560   -.05417025834321976        .05      32417  7
10001 561  .0072727203369140625  .07596154   32652.76  9
10001 562   .051444027572870255  .02775286   33978.91  4
10001 563   -.03729395940899849  .04473686  32844.902  7
10001 564   .023279275745153427 .023239003  33609.508  4
10001 565    .26162126660346985  .03153148   42413.91  5
10001 566    .01973225548863411  .07964598   43438.94  9
10001 567   .002764337230473757  .03431026   43559.02  6
10001 568    .04341829940676689  .04166666   45014.97  7
10001 569  .0006671266746707261 .033731587      42885  6
10001 570   -.05666669085621834  .02096014   40454.85  3
10001 571   .027561862021684647  .01282914  -41569.86  1
10001 572   -.03232463076710701  .04844541   39726.96  4
10001 573   -.04025876894593239  .05958088   38114.25  8
10001 574    .07940071821212769  .05689603    40726.5  7
10001 575  -.003929800353944302  .04194754    40652.5  4
10001 576  -.006082060746848583   .0449123      40250  5
10001 577   .021703556180000305  .05442179   41277.07  5
10001 578   -.05251631140708923  .04437228  38970.855  5
10001 579  -.036698244512081146  .05131413  37384.203  5
10001 580      .271627813577652  .13907287    47382.3 10
10001 581  -.010091708973050117  .18171445   46751.75 10
10001 582   -.05302322283387184 .032045256   44098.86  4
10001 583  -.019723936915397644  .08915143    43055.1  7
10001 584   -.13737370073795319  .06337263    36966.5  7
10001 585   -.01882350631058216  .13333334    36096.7  8
10001 586   -.13012051582336426  .14958455   31225.82  5
10001 587     .1559889167547226  .10929854  35493.223  5
10001 588   .034140389412641525   .0869565  36533.094  4
10001 589    .05622203275561333   .0745756   38415.18  5
10001 590   -.08053683489561081  .09693252      35174  7
10001 591    .04462098702788353  .07122507      36550  3
10001 592   .002941122744232416  .05072465  36921.918  3
end
format %tm period

[Rich Goldstein]

much of what you write is very unclear to me; however, I think you need the -newey- command for at least part of your analysis; see the help file

[Peter Eze]

Thanks for your response Rich. I have knowledge of the Newey command, however, my major problem is how to compute the equal and value weighted monthly returns for the 10 portfolios I have constructed.

[Kate Lussy]

Hi Peter,
I was wondering if you were able to figure out how to computed the equal-and value weighted monthly returns. I am struggling with the same problem now, so it would be a great help to see how you eventually did it. Thanks!