Case I.

GE Stock Closing Price The Excel dataset (GE.xlsx) given for this assignment is structured in the following format. The first column lists the months of observations, in total there are 72 months' recorded. The second column contains the monthly closing prices for the publicly traded stock of GE company, which is what we are interested in analyzing and predicting for its values. The third column contains monthly closing values of the S&P 500 Index (here we use this index to approximate the market conditions, although not appropriate in real modeling), which is commonly used in finance areas to represent the macroeconomic conditions of the market. With the data given,

(a). Obtain a scatter plot of GE and S&P500, and discuss what kind of relationship you can identify from the scatter plot?

(b). In Excel, run a simple linear regression model of GE on S&P 500, provide regression outputs, based on what, write down (type) clearly your estimate of the regression model (note: it's the model estimate not the regression equation estimate).

(c). Based on regression outputs, test the significance of the model. Also according to the commonly accepted argument in the market, that when the market is going well, so does the business of GE. Please use a formal hypothesis test to check whether this argument is valid statistically. Note: You are required to conduct two hypothesis tests here.

(d). Interpret the regression coefficient estimate you obtained in (b).

(e). What is the R2 of your regression model, interpret its meaning.

(f). Suppose the S&P 500 is predicted to be equal to 1900 at the end of next month, what will be your point estimate of the monthly closing price of GE stock?

(g). Based on your regression model, obtain an overlay plot of observed and predicted GE closing prices.