Control variables for intraday data.

[Ozer Erdem]

Hi everyone,
I would like to add control variables (BookToMarket, Size...) to my data to regress Abnormal return on text sentiment. For each firm and specific date, I have several thousands of row of data.
Each row is a unique sentence and variables associated with it.
I have included a sample of my data.
If I add the size(log of market cap), BooktoMarket ...., then I would be adding the same values for thousands of rows which raises some question marks on my mind. If my data was based on days rather than intraday data, I would have included the values for those variables.
I can also tag each of those values according to quintiles (1...5) and then use it in my regression as categorical variable.
In the sample data, I categorized variables (ln_mktcap bm roa) according to quintiles.

Should I just use the raw data rather than quintiles in my regression? What would be the best way to add those variables to my regression?

reghdfe AbnRet i.Sentiment i.ln_mktcap i.bm i.roa, absorb(year SIC) cluster(month_year)

Thank you.

* Example generated by -dataex-. For more info, type help dataex
clear
input byte Sentiment double AbnRet float(year month_year) byte(SIC ln_mktcap bm roa)
0  .000042760626015603265 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0  -.00008552490912983046 2017 685 38 5 3 5
0    .0000427551413056948 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0   -.0010592237732415244 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1  -.00004275514130580582 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1  -.00004275331338177146 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1 -.000042751485614056506 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0   .00004274965800266095 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
2                       0 2017 685 38 5 3 5
1  .000042751485614056506 2017 685 38 5 3 5
1  .000042753313381882485 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
2                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
2                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
2                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1    .0000427551413056948 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0  -.00012828187804658775 2017 685 38 5 3 5
0    .0001282654239170844 2017 685 38 5 3 5
0    .0021446675765255385 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0  .000042760626015603265 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1   -.0001282873636947457 2017 685 38 5 3 5
0    .0000855139387719861 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
2                       0 2017 685 38 5 3 5
2                       0 2017 685 38 5 3 5
1  -.00008552125203098448 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
1    .0000855139387719861 2017 685 38 5 3 5
1  1.1102230246251565e-16 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0   .00008552125203120653 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
0                       0 2017 685 38 5 3 5
2                       0 2017 685 38 5 3 5
1   -.0000427642832705466 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
1                       0 2017 685 38 5 3 5
0   .00004276245456480421 2017 685 38 5 3 5
end
format %tm month_year
label values Sentiment sentimentlabel
label def sentimentlabel 0 "Neu", modify
label def sentimentlabel 1 "Pos", modify
label def sentimentlabel 2 "Neg", modify