Reference no: EM132777916
Business/Finance Data Analysis Assignment
This assignment asks you to perform some simple descriptive statistical analysis in Excel using the data files provided in the assignment folder. The techniques required will be drawn from Modules 1 & 2 and the associated collaborate sessions. You will be graded on (i) your ability to correctly perform the statistical analyses, and (ii) the quality of your expositions/interpretations. Since the former is fairly straightforward a greater emphasis will be placed upon the latter.
Q1 - Share Prices for Automotive Companies
The file share_prices.xls contains weekly share prices for two car companies - TSLA (Tesla) and TM (Toyota) from 2020. The weekly changes in these series are also provided and denoted with the prefix "C". Your task is to analyse these data from the perspective of a potential investor who is interested in both maximising returns but also minimising risk.
• Produce some line charts showing the performance of these two firms over 2020. Which company had the better year?
• Produce some histograms showing the distribution of the weekly changes in price. Interpret the results. If a client wishes to invest and is extremely concerned with minimising potential losses, which company would you recommend? Conversely, if a client is mostly interested in growth potential, which investment would you recommend?
• Calculate the mean, standard deviation and skewness of the two weekly change variables. Discuss these variables in the context of financial risk and return.
• Summarize the risk/return tradeoffsfor the weekly change series using the Coefficient of Variation. Interpret these statistics. Which investment is preferred?
• Write a couple of sentences contrasting a hypothetical positive (right) skew investment from a hypothetical negative (left) skew investment (again using weekly changes). Which investment is more likely to report large losses? Which is more likely to post frequent small gains?
• Consider the two theories below about the relationship between TSLA and TM prices. Theory 1 says that both are automotive companies and are mostly affected by common factors (e.g. demand for cars), which suggests the prices should be positively associated. Theory 2 claims that TSLA and TM are competitors, and therefore one firm's loss is the other's gain. This suggests the variables should be negatively related. Using a scatterplot for the weekly change variables, summarize the statistical evidence on this issue. Which theory do the data support? Briefly discuss.
Q2 - Accidents and Full/New Moons
It is sometimes claimed that full or new moons have strange effects upon human psychology, resulting in increased risk-taking and other forms of unusual behaviour. If this is the case, we may expect to see the effects show up empirically in data sets related to human activity. The file moon.xls has data on daily hospital admissions for accidents, stratified by whether or not there was a lunar/astronomical event taking place on the day of admission. The idea here is that if full/new moons (and other related spooky phenomena) cause people to act erratically, then there are likely to be more accidents, and therefore more hospitalisations, on those days. The first column gives admission counts for weekdays where there was no lunar/astronomicalevent taking place, and the middle column shows totals on weekdays where there was such an event. The column on the right gives admission counts over weekends.
Your first task is to determine whether or not there are any meaningful differences between the data observed on astronomically important days and non-astronomically important days. Secondly you are to see if there are differences between accident rates on weekdays and weekends.
• Compare the first two variables (weekday admissions and full/new moon admissions) using histograms, means, standard deviations and coefficients of skewness. Report the results, and discuss any differences/similarities that you observe. Are there more hospital admissions on astronomically important days?
• Do we expect to see large differences in the distributions of these variables? Why or why not?
• Do you think that sampling variation could explain any small differences that you observe? Write a short paragraph explaining why or why not.
• Perform the same analysis (histograms, means, standard deviations, skewness) using data comparing admissions on weekends and weekdays.
• Do the results line up with your expectations? Are the distributional differences between weekdays and weekends likely to be statistically meaningful?
Attachment:- Finance Data Analysis Assignment.rar