How to deal with infrequent data in a time series prediction model
I would be tempted to put the last quarter revenue in a separate table, with a date field representing when that quarter began (or ended, it doesn't really matter). Then you can write queries to work the way that most suits your application. You could certainly reconstitute the view you mention above using that table, as long as you can relate it to the main table.
You would just need to join the main table by company name, while selected the max() of the last quarter revenue table.
I am trying to create a basic model for stock price prediction and some of the features I want to include come from the companies quarterly earnings report (every 3 months)  so for example; if my data features are Date, OpenPrice, Close Price, Volume, LastQrtrRevenue how do I include LastQrtrRevenue if I only have a value for it every 3 months?  Do I leave the other days blank (or null) or should I just include a constant of the LastQrtrRevenue and just update it on the day the new figures are released?  Please if anyone has any feedback on dealing with data that is released infrequently but is important to include please share.... Thank you in advance.