Attached below is part of my dataset:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double event_dt float(returns_10mins returns_20mins returns_30mins returns_40mins pos_cnfp pos_cci pos_ijc pos_ism pos_rs neg_cnfp neg_cci neg_ijc neg_ism neg_rs) 1.3886568e+12 .291382 .3361348 .17941247 .22421534 0 0 0 2.6655996 0 0 0 0 0 0 1.3891698e+12 .022160664 .11075424 .15502162 .13289039 0 0 0 0 0 0 0 -.4327536 0 0 1.3892562e+12 -.4881305 -.465891 -.4214268 -.4214268 0 0 0 0 0 -2.0179906 0 0 0 0 1.3897746e+12 .13274339 .22114117 .17695205 .22114117 0 0 .4387522 0 0 0 0 0 0 -.59006274 1.3903794e+12 .06546645 .08727908 .13089007 .10908695 0 0 .26445103 0 0 0 0 0 0 0 1.3908168e+12 .02168492 .04336514 .02168492 -.06508298 0 0 0 0 0 0 -.3839862 0 0 0 1.3909842e+12 0 -.15474746 -.0883978 -.022092124 0 0 0 0 0 0 0 -.0841513 0 0 1.3913352e+12 -.4200293 -.4421851 -.2871978 -.3978785 0 0 0 0 0 0 0 0 -.3214038 0 1.391589e+12 -.11095086 0 -.08875084 -.066555746 0 0 0 0 0 0 0 -.8975567 0 0 1.3916754e+12 .04425758 0 -.06642312 -.08857396 0 0 0 0 0 -.7778 0 0 0 0 1.3921938e+12 -.12978587 -.1730853 -.2380696 -.2380696 0 0 0 0 0 0 0 -1.0137575 0 -.59006274 1.3927986e+12 0 .1081198 .0865052 .1081198 0 0 .6711537 0 0 0 0 0 0 0 1.393236e+12 -.26396847 -.02197078 .10978155 .4384049 0 0 0 0 0 0 -1.089188 0 0 0 1.3934034e+12 -.04373497 -.19695815 -.21886636 -.19695815 0 0 0 0 0 0 0 -.25845245 0 0 1.3937544e+12 .06515366 .0217226 -.10868385 -.08693763 0 0 0 0 0 0 0 0 -.4280825 0 1.3940082e+12 -.06513951 -.021708455 -.04342163 -.021708455 0 0 .032049473 0 0 0 0 0 0 0 1.3940946e+12 -.4994039 -.368884 -.3906255 -.56472784 0 0 0 0 0 -1.4411577 0 0 0 0 1.394613e+12 -.04495392 -.08992806 .08984727 .29170895 0 0 .14825024 0 .04330736 0 0 0 0 0 1.3952178e+12 .0893256 0 -.0670466 -.04469274 0 0 .5549529 0 0 0 0 0 0 0 1.3958226e+12 0 -.022802416 0 .04558924 0 0 .09014986 0 0 0 0 0 0 0 1.39626e+12 -.04460303 -.06691201 .02229406 -.04460303 0 .4219586 0 0 0 0 0 0 0 0 1.3964274e+12 .04443457 .022219753 .19980027 .11104943 0 0 0 0 0 0 0 -.0841513 0 0 1.3964328e+12 .3099405 .3541394 .4645512 .486619 0 0 0 1.4921342 0 0 0 0 0 0 1.3965138e+12 .614711 .483305 .6365953 .7022192 2.841826 0 0 0 0 0 0 0 0 0 1.3970322e+12 .10882578 .0870701 .04354452 .0870701 0 0 .7292541 0 0 0 0 0 0 0 1.3974642e+12 .13060516 .10884947 .08708905 .0653239 0 0 0 0 2.3656642 0 0 0 0 0 1.397637e+12 .15488443 .2211901 .2432822 .15488443 0 0 0 0 0 0 0 -1.42046 0 0 1.3982418e+12 .02229406 .02229406 0 -.02229903 0 0 0 0 0 0 0 -.7232555 0 0 1.3986792e+12 .4163477 .5037794 .4382128 .4600731 0 .8450798 0 0 0 0 0 0 0 0 1.3988466e+12 -.022368863 -.0671216 .22341385 .22341385 0 0 .3225514 0 0 0 0 0 0 0 1.3991976e+12 -.02249972 .0899483 .24716337 .3144657 0 0 0 0 0 0 0 0 -.26806444 0 1.3994514e+12 .067226894 .022413986 .04482295 -.04484305 0 0 1.1940572 0 0 0 0 0 0 0 1.3995378e+12 -.3830127 -.4055887 -.6316285 -.473347 1.8323687 0 0 0 0 0 0 0 0 0 1.4000562e+12 -.13714288 -.06854793 -.09140769 -.13714288 0 0 0 0 0 0 0 -.31655285 0 -.59006274 1.400661e+12 .11492933 .1838236 .1838236 .1608641 0 0 0 0 0 0 0 -.9556571 0 0 1.4010984e+12 -.1145082 .04576659 .3655476 .50228417 0 0 0 0 0 0 -.20264864 0 0 0 1.4012658e+12 -.02235386 -.02235386 .06703162 .06703162 0 0 0 0 0 0 0 -.490854 0 0 1.4017032e+12 .11152003 -.08930565 -.20105 -.13398841 0 0 0 .5853651 0 0 0 0 0 0 1.4018706e+12 .06694187 .06694187 .08924588 .1115449 0 0 0 0 0 0 0 -.20035207 0 0 1.401957e+12 .1783724 .3564272 .3564272 .3786618 .4623907 0 0 0 0 0 0 0 0 0 1.4024754e+12 -.022043426 0 -.022043426 0 0 0 0 0 0 0 0 -.9556571 0 0 1.402821e+12 .04420866 -.022111664 .022106776 .022106776 0 0 0 0 .04330736 0 0 0 0 0 1.4030802e+12 -.1103631 -.04413063 -.08828074 -.1103631 0 0 .26445103 0 0 0 0 0 0 0 1.403685e+12 .06560963 .06560963 -.04376368 -.04376368 0 0 0 0 0 0 0 -.490854 0 0 1.4041224e+12 .13230433 .22041005 .1983909 .15433803 0 1.3487953 0 0 0 0 0 0 0 0 1.4042898e+12 .04385003 .04385003 .06576784 .04385003 0 0 0 0 0 0 0 -.490854 0 0 1.4042952e+12 .021975607 -.10995053 -.17597893 -.2200221 0 0 0 0 0 0 0 0 -.05470702 0 1.4043762e+12 -.19984463 -.1554002 -.06657051 -.06657051 0 0 0 0 0 -1.8593615 0 0 0 0 1.4048946e+12 .11229648 .06739302 .08984727 .17961387 0 0 1.8331615 0 0 0 0 0 0 0 1.405413e+12 -.11280317 -.09023236 -.11280317 -.067666635 0 0 0 0 0 0 0 0 0 -.59006274 1.4054994e+12 .26972368 .2472745 .1798966 .22482024 0 0 0 0 0 0 0 -.20035207 0 0 1.4061042e+12 .068642035 -.13742559 -.27504027 -.389864 0 0 .3806518 0 0 0 0 0 0 0 1.4065416e+12 .18386583 .1609011 .11495575 .023001725 0 .7846338 0 0 0 0 0 0 0 0 1.406709e+12 .04548556 -.02275054 0 -.02275054 0 0 0 0 0 0 0 -.25845245 0 0 1.40706e+12 -.09097112 -.20480153 0 0 0 0 0 0 0 0 0 0 -.10804638 0 1.4073138e+12 .068298236 .068298236 .04553734 .09105396 0 0 .26445103 0 0 0 0 0 0 0 1.4074002e+12 -1.0454271 -1.2324305 -1.2558304 -1.209036 0 0 0 0 0 -2.868819 0 0 0 0 1.4079186e+12 .1166453 .13995804 .13995804 .13995804 0 0 .4387522 0 0 0 0 0 0 -1.0123093 1.4085234e+12 .022896394 0 .022896394 .06867346 0 0 .26445103 0 0 0 0 0 0 0 1.4091282e+12 -.09057972 -.02263724 -.09057972 -.09057972 0 0 0 0 0 0 0 -.4327536 0 0 1.4095656e+12 -.24980143 -.36355415 -.29528698 -.2270664 0 0 0 0 0 0 -1.1496339 0 0 0 1.409652e+12 .13586959 .3844854 .5649094 .3393285 0 0 0 0 0 0 0 0 -.6414399 0 1.409733e+12 -.067834936 -.04521818 -.022606533 0 0 0 0 0 0 0 0 -1.246159 0 0 1.4098194e+12 .06719678 .13434844 .20145503 .17909116 .04418687 0 0 0 0 0 0 0 0 0 1.4103378e+12 .13407823 .11174434 .13407823 .13407823 0 0 1.5426595 0 0 0 0 0 0 0 1.4109426e+12 .04458315 .02229406 .04458315 .08914643 0 0 .4387522 0 0 0 0 0 0 0 1.4115474e+12 0 0 .022474436 .022474436 0 0 0 0 0 0 0 -.8394563 0 0 1.4119848e+12 -.06785028 -.022611646 -.11310939 -.29435095 0 0 0 0 0 0 -.58547246 0 0 0 1.4121522e+12 0 -.04486317 -.13464993 -.11219568 0 0 0 0 0 0 0 -1.4785606 0 0 1.412244e+12 .46713465 .6445183 .5558658 .5558658 0 0 0 0 0 0 0 0 -.05470702 0 1.412757e+12 .06573902 .06573902 .02191781 0 0 0 1.1940572 0 0 0 0 0 0 0 1.4128434e+12 -.3532792 -.4639354 -.530388 -.530388 0 0 0 0 0 -.6191709 0 0 0 0 1.4133618e+12 -.1123722 -.1123722 -.06740816 -.04493373 0 0 0 0 0 0 0 -.6651552 0 0 1.4134482e+12 .24900974 .20378135 .20378135 .2942177 0 0 0 0 1.7322943 0 0 0 0 0 1.4139666e+12 .18140595 .22670606 .2493484 .27198565 0 0 .9616556 0 0 0 0 0 0 0 1.414404e+12 .25076953 .4327532 .5009118 .4554779 0 0 0 0 0 0 -.28324312 0 0 0 1.4145714e+12 .0891067 .02228412 .0891067 .11137099 0 0 0 0 0 0 0 -.8394563 0 0 1.4149224e+12 -.08853476 -.17714797 -.13283154 -.08853476 0 0 0 0 0 0 0 0 -1.0148153 0 1.4151762e+12 .06556661 .06556661 .02186031 0 0 0 .49685255 0 0 0 0 0 0 0 1.4152626e+12 .5797118 .5797118 .5154651 .5368852 2.466885 0 0 0 0 0 0 0 0 0 1.4156946e+12 -.021410983 -.06424671 -.14997324 -.14997324 0 0 .26445103 0 0 0 0 0 0 0 1.4158674e+12 .06386376 .08514262 .06386376 .06386376 0 0 0 0 .2544307 0 0 0 0 0 1.4163858e+12 0 -.04223865 0 -.021117095 0 0 0 0 0 0 0 -.20035207 0 0 1.4169042e+12 -.06347191 -.08463818 -.08463818 -.14816386 0 0 .7292541 0 0 0 0 0 0 0 1.417428e+12 -.021256244 .06374164 -.021256244 -.04251701 0 0 0 0 0 0 -1.1496339 0 0 0 1.4175144e+12 -.06344507 -.04229224 .021139415 .23228814 0 0 0 .3186684 0 0 0 0 0 0 1.4175954e+12 0 -.16806726 -.21012826 -.18909556 0 0 0 0 0 0 0 -1.0718578 0 0 1.4176818e+12 -.37712175 -.33514905 -.4401139 -.4611201 0 0 0 0 0 -1.1383204 0 0 0 0 1.4182002e+12 -.02116626 -.08469194 -.12706482 -.06351223 0 0 0 0 0 0 0 -1.246159 0 0 1.4185458e+12 .10467917 .12560186 .20924887 .2301497 0 0 0 0 .4655541 0 0 0 0 0 1.418805e+12 0 -.020718947 -.020718947 -.04144219 0 0 1.484559 0 0 0 0 0 0 0 1.4198472e+12 .1856245 .1856245 .2268276 .370905 0 1.630876 0 0 0 0 0 0 0 0 1.4203656e+12 -.24665993 -.3908264 -.432055 -.55584294 0 0 0 0 0 0 0 0 -.05470702 0 1.4206194e+12 -.021068156 0 0 0 0 0 0 0 0 0 0 -1.9433638 0 0 1.4207058e+12 .315027 .3988669 .3569558 .3988669 0 0 0 0 0 -.12886299 0 0 0 0 1.4217426e+12 .16732904 .2091176 .1882255 .12552303 0 0 1.5426595 0 0 0 0 0 0 0 1.4222664e+12 -.06390457 -.04259851 .04258037 .04258037 0 .4421073 0 0 0 0 0 0 0 0 1.4224338e+12 -.02131969 0 -.063972704 -.02131969 0 0 .4387522 0 0 0 0 0 0 0 1.4228712e+12 .08460237 .06345849 .12687674 .2324845 0 0 0 0 0 0 0 0 -.4280825 0 1.4230386e+12 -.06289968 -.12583894 -.16782047 -.18881784 0 0 .8454549 0 0 0 0 0 0 0 end format %-tc event_dt
- returns columns represent equity returns at 10 minutes interval, starting from 10 to 40 minutes
- columns labelled pos refer to positive observations
- columns labelled neg refer to negative observations
Code:
eststo: quietly regress returns_10mins pos_cnfp - neg_rs, noconstant vce(robust) eststo: quietly regress returns_20mins pos_cnfp - neg_rs, noconstant vce(robust) eststo: quietly regress returns_30mins pos_cnfp - neg_rs, noconstant vce(robust) eststo: quietly regress returns_40mins pos_cnfp - neg_rs, noconstant vce(robust) estout using 1.Results_MNA_Surprise.xls, cells(b(star fmt(2)) t(par ([ ]))) starlevels(* 0.10 ** 0.05 *** 0.010) stats(r2 N cnfp cci ijc ism rs) replace eststo clear
- Add a column to my regression output table showing positive_betas - negative_betas for each of cnfp, cci, ijc, ism and rs, and for each return interval
- Perform a Wald Test that positive_betas - negative_betas !=0, saving the results in the table along with starlevels for each of cnfp, cci, ijc, ism and rs, and for each return interval
- And finally is there a more efficient way to run all the above regressions?
Regards
Parvesh
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