Question

In: Statistics and Probability

The daily changes in the closing price of stock follow a random walk. That is, these...

The daily changes in the closing price of stock follow a random walk. That is, these daily events are independent of each other and move upward or downward in a random matter and can be approximated by a normal distribution. Let's test this theory. Use either a newspaper, or the Internet to select one company traded on the NYSE. Record the daily closing stock price of your company for the six past consecutive weeks (so that you have 30 values). Decide whether the your 2 data sets are normally distributed by creating a histogram or a boxplot. Please attach your histogram or boxplot as a Word document in your post. Please do NOT answer the discussion in your attachment; answer on the discussion board. Discuss your results. What can you say about the stock with respect to daily closing prices and daily changes in closing prices. Which, if any, of the data sets are approximately normally distributed? NYSE - GE Date Closing Stock Price 1/2/19 8.05 1/3/19 8.06 1/4/19 8.23 1/7/19 8.74 1/8/19 8.56 1/9/19 8.5 1/10/19 8.94 1/11/19 8.94 1/14/19 8.9 1/15/19 8.73 1/16/19 8.98 1/17/19 9.14 1/18/19 9.06 1/22/19 8.66 1/23/19 8.73 1/24/19 8.78 1/25/19 9.16 1/28/19 8.93 1/29/19 8.9 1/30/19 9.1 1/31/19 10.16 2/1/19 10.19 2/2/19 10.21 2/3/19 10.63 2/4/19 10.47 2/5/19 10.06 2/6/19 9.81 2/7/19 10.03 2/8/19 9.81 2/12/19 10.31 Sample Size 30 Mean Median Standard Deviation Minimum Maximum

Solutions

Expert Solution

We have to select the two data sets for the closing stock prices for the company traded on the NYSE. Then we have to create the histograms for these datasets to check whether the given two datasets are normally distributed or not. The histograms for the Closing stock prices and daily changes are given as below:

No any one of the histogram above shows that the data is distributed normally or approximately normally. Actually, the given sample size is less or not adequate to test the normality properly. For the large sample sizes, we get more reliable results for checking the normality of the data.


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