I am currently working on a sentiment study, which means I want to investigate the influence of sentiment on stock prices and trading volume. Regarding stock prices is relatively simple, I calculate the return, expressed as a percentage. Since it is an intraday data set I calculate this return for a period over 5 or 10 minutes for example.
Since the return is expressed as a percentage, I can compare very well across different companies.
It gets more complicated with the trading volume, as this is expressed in an absolute number (for example 14238 stocks per minute). Since companies issue a different number of shares it is not possible to compare them. Even if you look at just one company, the number of shares traded per minute varies greatly. Therefore, I would like to ask how to transform the trading volume so that I can 1.) detect abnormal intraday movements well and 2.) also compare well between several companies.
My data set looks like this:
TIMESTAMP HIGH LOW OPEN CLOSE COUNT VOLUME
<dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021-11-08 07:51:00 1970 1942 1942 1952. 9 14238
2 2021-11-08 07:52:00 1970 1946. 1970 1946. 11 33902
3 2021-11-08 07:53:00 1946. 1946. 1946. 1946. 7 25574
4 2021-11-08 07:54:00 1945 1935. 1943 1935. 4 15546
5 2021-11-08 07:55:00 1933. 1920 1933. 1920 4 18044
6 2021-11-08 07:56:00 1916 1916 1916 1916 6 26496
7 2021-11-08 07:57:00 1916 1907 1907 1916 12 69053
8 2021-11-08 07:58:00 1916 1903. 1916 1903. 16 111587
9 2021-11-08 07:59:00 1911. 1903. 1903. 1911. 23 189042
10 2021-11-08 08:00:00 1910 1903. 1910 1909. 98 1228126
Many thanks in advance!
(And can someone tell me what the variable COUNT
means?)
BTW
? @noob2 $\endgroup$