The sensitivity of a security towards a factor can be estimated from past price movements. We will estimate the beta from the one-factor index model. First, we show the process of collecting and synchronizing data from different sources and then present the simple beta estimation method and, at last, a linear regression model is built.
We download the time series of the price of a given stock, for example Google, and the time series of the price of the market index, the S&P 500, from June 1st 2009 to June 1st 2013 from Quandl, as discussed in the second chapter:
> library(Quandl) > Quandl.auth("yourauthenticationtoken") > G <- Quandl('GOOG/NASDAQ_GOOG', + start_date = '2009-06-01', end_date = '2013-06-01')
The resulting G is a variable containing 6 variables, from which we only need the Close values:
> str(G) 'data.frame': 1018 obs. of 6 variables: $ Date : Date, format: "2009-06-01" "2009-06-02" ... $ Open : num 419 426 426 435...