Hi everyone,

I am not sure if this is a specific Stata question, if not I am sorry.

I am working on a dataset that has monthly data of stock returns from banks as dependend variable (variable = exreturn) and independend variables which are "mktminusrf" "smb_5" "hml" "rmw" "cma" "funding". When I do my regression without holding for time-fixed effects I am getting the right results

Code:
reg exreturn mktminusrf smb_5 hml rmw cma funding, vce(robust)
However, I try to do a time-fixed regression as this was done by some other researchers with similar data. My issue is that I do not understand how I should set up the panel data and hold for time-fixed effects.
Everytime when I read something here or watch a video I always see that the fixed effects are hold by an "ID" and the panel date is set up like this
Code:
xtset id year, yearly
and afterwards run by the code
Code:
xtreg y x1 x2, fe
.

Therefore my question might be more a theoretical question, I hope this is okay and somebody can help me out:
Is it possible to run a time-fixed regression with this dataset that you see below? And if yes, how should I set my panel data up.

My idea was to hold for fixed year or month but doing this poriveds me a result which I believe cannot be correct
Code:
reg exreturn mktminusrf smb_5 hml rmw cma funding  i.year, robust

Extra information: The variable exreturn (dependend variable) is a Portfolio of 40 stocks from the same industry

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input int year byte month double(funding smb_5 hml rmw cma rf exreturn)
2010  1 -.3933305349832981   .25   .61 -1.18   .41   0                  .
2010  2 -.4309383413670224  1.53  2.74  -.55  1.43   0  1.079242090318634
2010  3 -.0141456717752401  1.85  2.01   -.9  1.67 .01   7.86393930364869
2010  4 -.3451440767419464  5.03  3.12   .49  1.69 .01  7.512007394694056
2010  5 -.4262781423107988  -.08 -2.32  1.38  -.18 .01 -7.985979824222703
2010  6  -.371163521472528 -2.59 -4.27  -.34 -1.48 .01 -6.335626764257626
2010  7  -.024398109698932   .13   .04   .32  2.03 .01  2.881855746293185
2010  8 -.4337655287944647 -3.07 -1.51   .34 -2.13 .01  -9.47372995462771
2010  9 -.2048099491622005  3.71 -2.94  -.01   .39 .01  6.938139934459893
2010 10 -.0729573838647816   .72 -2.23  1.46  -.16 .01  -1.05813682753868
2010 11 -.3881111120403277  3.54  -.58   -.1  1.76 .01 -.2466604777739863
2010 12  .1058078519319547  1.03  3.47 -3.44  3.44 .01  14.13006373515285
2011  1 -.3926004371311564 -2.38   .68 -1.07    .8 .01 -.7969297791341908
2011  2 -.4062703543627456  1.76  1.73 -1.76   .72 .01  .5468279827189715
2011  3 -.3648256507560639  2.66 -1.16  1.21  -.03 .01  .9523648032545907
2011  4 -.4321655271184945  -.41 -2.15   .96 -1.28   0  .5892439640544306
2011  5 -.3267984264572796  -.69 -2.12  2.02 -1.46   0  -1.49001583687659
2011  6 -.2500915499918396   .09  -.26  2.16  -1.4   0 -2.242232233723338
2011  7 -.3658353605515791 -1.38 -1.18  2.41 -1.75   0 -3.803201280158997
2011  8 -.3068993764872049 -3.39 -1.58  2.79  -.23 .01 -10.01027367894514
2011  9   -.26837506428909  -3.9  -.98  1.71   .24   0 -9.691976652945483
2011 10 -.3872412082164993  3.72  -.96 -1.42  -.86   0  14.10180765195524
2011 11 -.4345422286371686  -.34  -.18  1.46  1.52   0 -.7010521563359489
2011 12 -.4031635549919299  -.36  1.57   .59  2.44   0   2.80698102953636
2012  1 -.2686391422356094  2.35 -2.14 -1.05 -1.41   0  4.812300227465119
2012  2 -.4327092170083873 -1.54   .01  -.17  -.03   0  4.929256079524911
2012  3 -.4036606428912604   -.3  -.06   .25   .77   0  6.134441796438536
2012  4 -.4073266661488228  -.66   -.2   .96   .72   0 -.6589440873023984
2012  5  -.392320825187783   -.2   .08  1.98  2.37 .01 -5.232003502156303
2012  6 -.3499596157667108   .99   .54 -1.48   .37   0  3.161005632020767
2012  7 -.3997460756840325 -2.74   .01   .68   .12   0 -2.194379715301951
2012  8 -.2152798630418495   .61    .6  -.77  -.69 .01  3.209782285250071
2012  9 -.3207090996904808   .69  1.56 -1.14  1.57 .01  3.744035044489593
2012 10 -.3458741745940881   -.8  4.16 -1.35  2.28 .01 -.0112728353654932
2012 11 -.4301461075274643   .41 -1.12   .94   .93 .01 -1.707135468131021
2012 12  -.348421750078157  1.91  3.26 -1.75   .88 .01  3.042648155235065
2013  1 -.3217032754891418   .57  1.34 -1.88  1.47   0  4.353588346029981
2013  2 -.3033110232139128  -.35   .28  -.96   .49   0  1.795811335142117
2013  3 -.3892140258169673    .9  -.07   .13  1.21   0  4.731694854308732
2013  4  .0938156063606059 -2.32   .35   .04   .39   0 -1.228710327935455
2013  5 -.1475205687643588  2.27  1.33  -.71  -.83   0  4.940060638818191
2013  6 -.3079867562669904  1.33   -.4  -.47   .01   0  2.524169010641897
2013  7 -.2844372170362073  1.81   .71 -1.43   .53   0  7.810874448103843
2013  8 -.3654470106302271  -.03 -2.48   .85 -2.13   0 -3.497821482444668
2013  9 -.0458660933512685  2.72 -1.57   -.1 -1.32   0  1.662269053409684
2013 10  .1428253664352239 -1.57  1.36  2.83   .89   0  3.530331927754367
2013 11  -.311854721483656  1.47  -.38   .77   .12   0  5.958017535781948
2013 12 -.3969188882565902  -.44   -.2  -.57   .07   0  1.758179600545098
2014  1 -.4001033576116764   .56 -1.88  -4.5 -1.42   0 -3.509387301462624
2014  2 -.2543478651298571   .16  -.49  -.49   -.4   0  2.497577726394331
2014  3 -.0462855112663287 -1.23   4.6  1.76  1.91   0  4.418488481502361
2014  4  .3741421135893079 -4.21  1.62  2.85  1.09   0 -5.144931450627418
2014  5  -.214192483262064 -1.83  -.38   .45 -1.09   0 -.1524946883270472
2014  6  5.119203394626711  3.04   -.6  -1.9  -1.9   0  5.056250663074426
2014  7  .6428957931617213 -4.16   .04  1.48   .44   0 -3.606526297369947
2014  8 -.0663865031955063    .3  -.76  -.91  -.65   0  2.334665259623833
2014  9 -.2642274871290511  -3.8 -1.68  1.28  -.62   0 -2.045957417917509
2014 10 -.2376954205022849  3.79 -1.81  -.78  -.18   0  3.921821214508064
2014 11 -.2130740354885704 -2.27 -3.37  1.69   .15   0  .2556962153253985
2014 12    2.6553250856076  2.85  1.56 -1.52   .81   0  1.699910054766814
2015  1 -.2924061574223497  -.91 -3.06  1.09 -1.67   0 -9.063475876310159
2015  2 -.2044215992408486   .35 -2.16   .06 -1.62   0  7.821358748819074
2015  3 -.1474428987800883  3.07  -.73   .16  -.54   0  1.178161237891585
2015  4  1.668465799464843 -2.99  2.13   .41  -.49   0  1.169923120498791
2015  5  .1219010726727801   .85  -1.9 -1.54  -.68   0  2.282959490396854
2015  6  .1296991390935276  2.88 -1.04  1.03 -1.51   0  4.042932941219418
2015  7  .3822663939439909  -4.5 -4.49   .31  -2.6   0 -.4668957188768061
2015  8  .2912371723790906   .38  2.88   .75  1.14   0 -5.438906417704657
2015  9  .4157110891708222 -2.81   .73  1.66   -.5   0  -1.03380138198852
2015 10   5.63291267059109 -2.05  -.32  1.19   .45   0  4.103701862358857
2015 11  .1172253396197023  3.35 -1.23 -2.11    -1   0  4.884271693531828
2015 12  -.382441203188589    -3 -2.07   .45   .17 .01 -5.998214832502895
2016  1 -.1641108774045147 -3.56  3.13  2.27     3 .01 -9.570953046717952
2016  2 -.3199323998477768   .87  -.03  2.44  2.09 .02 -3.224477640041395
2016  3 -.0534622178129129  1.01   1.3   .58   .07 .02  7.358411454974729
end
Edit// I also believe the titel is a bit misleading I am also sorry for this

Thanks