Two Variable Hypothesis Testing

Beta is the slope of the characteristic line. As shown in appendix1, we use the monthly historical data from April 2003 to April 2008 to graph SCL. We use S&P 500 as the market because it contains the stocks of 500 Large-Cap corporations and can reflect the market change broadly. As shown in exhibit 1, we can get the SCL: Ri-Rf = - 0.001 + 0.1649(Rm - Rf). So the beta is 0.1649 and the alpha (intercept) is -0.001. From the regression line, we find the R square is only 0.103. It reflects the degree that X variable explains Y variable is 10.3%. That is, Ri-Rf has no close relationship with Rm-Rf. In addition, due to the fact that beta is only 0.1649, it also provides the same result which Ri-Rf and Rm-Rf have no strong positive relationship. It is a good issue to consider why Wal-Mart stock return does not relate with S&P 500 return very well. From exhibit 2, we find the Wal-Mart price did not change very much while S&P 500 price went up. The major reasons may be that S&P 500 includes many high-tech stocks and those stocks performed very well during the period; therefore the volatility is greater than Wal-Mart. Moreover, maybe Wal-Mart has lower weight in S&P 500 so its trend of price change is different from S&P 500. From exhibit2, we also find Wal-Mart had good relationship with S&P 500 before 2004. To examine the situation, we use the data from 1998 to 2003 and find the SCL

2. The Impression in data: Larger the cell less is the error, smaller the cell more is the error.

Linear regression is an approach for modeling the relationship between a scalar dependent variable Y and explanatory variables (or independent variables) denoted X. Function $f(X,W)=Y$ (shown below) can be learned to predict future values.

The key coefficient in these figures is the coefficient on goodwill. The p-values for the goodwill coefficients in both periods are sufficiently low (.0029 and 0.0008, respectively) to indicate that there is a significant correlation between market capitalization and goodwill. This coefficient goes down from 0.418 in prior-2001 periods to 0.171 in post-2001 periods, indicating that investors have a significant negative reaction to the goodwill impairment that arises from FAS 142, and therefore our second hypothesis is

* Stock Beta: Exhibit 5 shows a detailed measurement of the company’s stock returns in relation to the rest of the market through 5-year historical price and index data. The analysis includes monthly returns of both the NYSE and the S&P 500 index in order to capture a comprehensive view of the market return. In each comparison, the monthly returns of the Target stock and market are plotted on Y-axis and X-axis respectively to get the regression line’s slope or beta. The analysis arrives at an average beta of 0.988 which indicates a similar movement of Target stock’s returns in comparison to the whole market over time.

(a) Estimate the expected return and standard deviation for a portfolio that allocates 50% to stock #1

Complete problem 18 in Chapter 3 (shown below) and submit to the instructor. Show your work to find the annualized return for each of the listed share prices. Write a 100 word analysis of the process to calculate these annualized returns.

For the month of December, I was given an assignment consisting of $100,000 and four stocks to invest in. My four stocks were The Ralph Lauren Corp., Visa Inc., Master card Inc. and The Chevron Corp. As stated I was given a month to record my data and I ended up with a total capital gain of $5,518.36 for the one month period for my investments. I have to thank you Mr. Acker, this project was not difficult, but it did confuse me. Receiving this assignment scared me in a way, because I didn’t know what I was getting into. The finance world is scary and tricky, one minute the market is doing good and other days it would be low. While calculating my capital gains or losses I thought I would lose a larger

We added the market risk premium of 6% to the 4.60% because 6% is the rate that investors want above the risk free rate due to the risk of the investment. This equals 10.6% which is then multiplied by the beta of the company of 1.1. Beta is a measure of the stock’s volatility in relation to the market. A beta of 1.1 means that Worldwide Paper Company has slightly higher volatility than the market does. The total cost of equity then calculates to equal 11.2%. This tells us that given the risk taken in investing in the company, a shareholder should expect an 11.2% return.

For estimation of betas, the above equation was run for the period from Jan, 2003 to Dec, 2006. Based on the estimated betas we have divided the sample of 63 stocks into 10 portfolios each comprising of 6 stocks except portfolio no.1, 5 and 10 having seven stocks each. The first portfolio 1 has the 7 lowest beta stocks and the last portfolio 10 has the 7 highest beta stocks. The rationale for forming portfolios is to reduce measurement error in the betas.

Using the same market risk premium and risk free rate (5.5% & 4.62% respectively) given in the case, the averaged beta of 1.40, the pretax cost of debt of 7.65%, and the weighted average of debt & equity, the products & systems

5. From the data of question 3, the variance of revenue is favorable because Apple increased price of flash memory by $2. However the company didn’t gain profits from the revenue due to the increased price of flash memory provided by Samsung. The variance of flash memory is unfavorable because of two reasons: first reason is the price of each unit

In March 1995, Fred Aldrich, a summer trainee with the First Investments, Inc., was called into the office of the head of investment analysis section of the trust department. The following conversation took place: Fred, here are the 1994, 1993, and 1985 Basic Industries Company’s financials (Exhibit 1) and a 10-year summary (Exhibit 2 ). Our trust department has owned this stock since the early 1980s. As you know, our portfolio people place a lot of emphasis on the quality of a company’s earnings and the return on owners’ equity in making stock selections. Well, they are worried. The 1994 Basic Industries annual report

The trendline, known as the line of best fit or the least squares regression line, shows the linear equation which best explains the sums up the data’s trend. The formula on the right is the formula of the line of best fit.

The author has experience in dealing with financial data during his day to day job. Therefore he is comfortable with extracting relevant figures and come to a conclusion on his