<Student’s name>
<Professor’s name>
The IBM historical data is below:
The HP data is below:
We are using Open Prices for our calculations (look at MS Excel file). The further calculations are processed in Excel file and commented in Word file.
Then, we calculate the historical monthly returns of these stocks using the adjusted closing prices from yahoo using monthly return =(Pricemonth2 – Pricemonth1) / Pricemonth1. After which, find the average monthly return.
After this we are able to calculate the mean monthly return. Just finding the average value for both data sets.
The same we do for standard deviations.
The mean values shows us the risks of return for both stocks. For IBM this value was positive, so, the investment in this stock is quite good, as we can have a positive return. The annual return of investment is expected at the level of 13.76% For HP the value was negative, hence, the investment is risky. The annual loss of investment is expected at the level of -15.55%. The same say standard deviations, IBM has lower std.dev., than HP, so the variability of data of IBM is lower.
The correlation coefficient between the prices appeared to be approximately -0.70
This is an evidence of quite good negative linear association between data sets. According to this we can say, that the positive change of IBM price involves the negative change in HP price, and vise versa. This might be just because both companies are working on the same market, and the global factors, which affect the whole market also affects the price changes of all market participants.
The correlation coefficient between the returns appeared to be approximately -0.03
This is an evidence of that there is no linear association between data sets. There might be another association, but not linear.
According to our analysis we can say, that IBM stocks are more preferred to invest, than HP stocks.
