I am trying to look at stock tickers. For example "MSFT" is the stock ticker for Microsoft. Using the package quantmod, an R package, I can get the latest 365 days of stock prices.
Each quarter a company will release their financial statements. The balance sheet, income statement, and cash flow documents. Now using these documents I can calculate certain financial ratios, such as the P/E ratio (Price/Earnings ratio). I would like to see how these ratios correlate to stock price. I will have four different P/E ratio calculations, because the financial statements are released quarterly.
My problem is I have way more than four stock prices since these are released on a daily basis. If I only use the latest four quarters stock prices as shown below, I know I will run into an error of not having enough data points to make a significant statistical prediction:
Dependent Variable-Stock Price: $2.00, $3.00, $3.50, $3.25
Independent Variable 1-# Days: 90, 180, 270, 360
Independent Variable 2-PE Ratio: 36, 48, 41, 51
But, if I use a different stock price on a daily basis, I run into having to repeat four different PE ratios a great many times as shown below. I feel like this will spit out a better p-value, but doubt you should even do this. I've learned regression on my own, so I don't know if the below is valid and the consequences of doing this:
Dependent Variable-Stock Price: $2.00, $2.05,..., $3.00, $2.97,..., $2.50, $2.60,..., $3.25, $3.28
Indpendent Variable 1-# Days: 90, 91,..., 180, 181,..., 270, 271,..., 360, 361
Indpendent Variable 2-PE Ratio: 36, 36,..., 48, 48,..., 41, 41,..., 51, 51
Would there be any way to work around either problem of not having enough data to make a significant prediction or not having to repeat values in the independent variable PE ratio?