An important model that finance professionals use to understand asset prices is called the Capital Asset Pricing Model (CAPM). You will learn more about the CAPM in a future finance course. But the basic assumption of the model is that the rate of return on an individual stock is linearly related to the rate of return on the overall stock market. That is, each stocks rate of return is assumed to follow a linear regression model: Y (k) t = (k) 0 + (k) 1 Xt + e (k) t , where Y (k) t is the rate of return of an individual stock (k) in some given time period t; Xt is the rate of return of the entire stock market in that same time period; and e (k) t is the residual for stock k in that time period. The superscript (k)s here are simply denoting the different stocks (Apple, Target, etc), while the subscript ts are denoting the different time periods. Note that the market rate of return (Xt) is a predictor common to all stocks. (The rate of return can be interpreted similarly to an interest rate. For example, if a stock was worth $100 yesterday and $102 today, then it gained 2%, for an implied daily rate of return of 0.02.) The 1 (slope) term in this regression model is super important to finance professionals; they just call it beta, and they refer to the 0 (intercept) term as alpha. Once youve watched the video, please turn to the data in marketmodel.csv, which contains information on the daily returns for the S&P 500 stock index, denoted SPY, along with the returns for 6 individual stocks: Apple (AAPL), Google (GOOG), Merck (MRK), Johnson and Johnson (JNJ), Wal-Mart (WMT), and Target (TGT). (We can think of the return of the S&P 500 as a proxy for the whole market.) The data start from the beginning of 2019. The entries are interpretable as percentage returns, expressed on a 0-to-1 decimal scalefor example, if the S&P 500 gained 1.5% in value on a given day, the corresponding entry in the data frame would be 0.015. Regress the returns for each of the 6 stocks individually on the return of S&P 500 (which is like Xt, the market return, in the equation above). Make a clean, professional looking table (e.g. in Excel) that shows the ticker symbol, intercept, slope, and R2 for each of the 6 regressions. In your write-up, you should include: a two-to-three paragraph introduction, in your own words, on what the beta of a stock is measuring and how it is calculated. (Watch the video and summarize it in your own words, making sure to connect it to the regression model weve written down abovethis is a bridge you will have to make yourself, using what you know about regression models.) A reasonable aim for your summary is about 250 words here, but this is approximate; nobody on our end is breaking out the word counter. the table itself, along with an informative caption below the table, no more than 2-3 sentences in length, to give readers the information necessary to interpret the table. a conclusion that answers two questions: in light of your analysis, which of these six stocks has the lowest systematic risk? And which has the highest systematic risk? (Again, watch the video to understand how this is measured using the regression model.)