Normalisation for regression First off, I know little about statistics, so some of this question may seem naive.
I'm trying to perform linear regression to model the relationship between x and y where:
-x is a company's daily stock volume on a date
-y is variable that is taken from the same date, however is something unrelated to stock volume.  It is the volume of activity on that wikipedia for that company.
I assume that the variables need normalising.  Specifically, x needs to be normalised as overall index volume fluctuates.  My first thoughts were to divide the daily volume by the total index volume.  I'll do the same with the y variable.  
I just wondered if this seems sensible?  Thanks
EDIT:
I've just noticed a typo in the question, the Y variable description has changed.
 A: It's fairly common practice to log-transform financial data.  You could also look through the rest of the family of Box-Cox transformations, and stop when you judge the distribution to be normal. You're idea to divide daily volume by total index volume is a good one, but that distribution STILL may not be normal.
Furthermore, Volume (and possibly your Y variable) are likely to-be auto-correlated, so you need to deal with this as well.  Commonly, you can deal with auto-correlation by differencing: rather than using Today's Volume as Y, use (Today's Volume-Yesterday's Volume)/(Today's Volume) as Y.  However, if you are unlucky, the differenced series will also be auto-correlarted, and then you disappear down the rabbit hole of time-series analysis...
On the other hand, if you are lucky,  differencing and log-transformations will be sufficient for your data, and you can use linear regression to build your models.  One extra check you should do during your model building is to check for auto-correlation in your errors.  If you can't get rid of the error auto-correlation, try a method of regression that is robust to these sorts of errors.
/Edit: as @whuber noted, differencing is only needed if your residuals are auto-correlated.  If your regression model removes the apparent auto-correlation in the y variable, you are all set.
A: Honestly this is more of a finance question than a stats one. (I'm new to CV but have been finding a huge number of questions on here about forecasting financial time series!)
Anyway, it always helps to normalize your variables as much as possible. A guiding principle might be that we should always be able to meaningfully compare two individual datapoints. So if you're looking at volume of a single stock over time, you probably want to adjust by total market volume; if you're looking at multiple different stocks, you might want to adjust by shares outstanding so as to not bias the numbers for large vs small cap issuers.
