plz finish the last four questions (10-13)
write the report for those 4 questions. Estimated length: 1.5 pages (excluding tables/figures).Make sure to explain intermediate steps of each calculation in your report and clearly indicate what parts refer to each of the individual questions of the case study.
1.Aftaer you merged both datasets as described in the Instructions: Compute the market capitalization as [abs(PRC) * SHROUT] for each stock in each month. Why are some prices negative? (You might need to lookup the definition of PRC in WRDS, for example with an internet search, to answer this question.)
Why can closing prices at time “t” be negative? [select every answer that you think is correct]:
1. For each stock, compute the average excess return and the standard deviation over the period from January 2000 to December 2010 (to calculate such summary statistics the PivotTable is very useful). Which stock has the highest average excess return? (Reminder: excess return is the difference between the return and the risk-free rate)
1. Now compute the Sharpe ratio for each stock over this period. Which stock looks most attractive to you in terms of the tradeoff between return and risk over this period?
1. Do you observe any pattern regarding the Sharpe ratio of a stock and its average market cap (computed per PERMNO over the whole sample period)? Run a simple cross-sectional regression to check.
1. Now run a formal CAPM (“market model”) time-series regression for each of the 100 firms (you can use the “LINEST” function in EXCEL to do so). Estimate the market model over the entire sample period. Which stock has the largest beta estimate? Insert the PERMNO of the stock with the largest Beta coefficient below.
1. Make a scatter plot with on the vertical axis the historical average excess return of all stocks and on the horizontal axis the stock’s beta estimates over the full sample period. Also add a linear trend line, which is the security market line (SML). Does this plot support the CAPM predictions?
Indicate every correct statement from the list below.
1. Please indicate the Coefficient of determination (R2) from the regression in Question 6 (i.e. the estimated SML) that is closest to what you find:
1. Now run a cross-sectional regression. Explain the wholesample average excess returns of each stock by the wholesample average MARKETCAP of each stock and the estimated beta (from Question 5). What do you find? Assume statistical significance is indicated by a t-statistic below -2 or above 2. Select every answer that corresponds with your findings.
1. Now run a so called Fama-MacBeth regression (2nd step), that is: each month regress excess returns on MarketCap only, then compute the average coefficient on Market Cap as well as the Fama-MacBeth t-statistic, which is avg(X)/[stddev(X)/sqrt(T)], where: X is the monthly estimated slope coefficient when explaining Returns by MarketCap and T is the number of observations (the number of months in the sample).
1. As before, run a so called Fama-MacBeth regression and compute the Fama-MacBeth t-statistic. But this time explain Returns by MarketCap as of January for each year, i.e. in February to December you use the market cap estimated in January of each year for each stock.
1. Of course, smaller stocks are also associated with higher risk. Hence, redo the Fama-MacBeth regressions, use the MarketCap as of January for each year, and the CAPM-beta (estimated over the whole sample) as a control variable, to explain monthly returns (as before).
1. Are your results so far consistent with the data you received from Ken French’s website (using data from Jan-2000 to Dec-2010)?
Report the Fama-MacBeth test statistic, i.e. sqrt(N)*avg(X)/stddev(X), where N is the number of observations (the number of months), and X is the monthly estimated slope coefficient on MarketCap when explaining Returns by MarketCap and CAPM-Beta (i.e. the slope coefficients from the previous regression).
Round the value to two decimal digits, and use the dot to separate decimal from non-decimal digits, i.e. enter like:
Use all slope coefficients from 2005 (i.e. N=12).