Finance Question

Economics

Question Description

I’m studying for my Finance class and don’t understand how to answer this. Can you help me study?


Follow the requirements to write 5pages report and an excel spreadsheet

The course is FINANCIAL METRICS FOR DECISION MAKING

The written report must be self-contained and formatted as a PDF file

And you will need to make a quantitative analysis of the given data on an excel

I have attached a sample which was done by another student please have a look make sure you are on the track

All the work must be original

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Finance Discipline Group UTS Business School FINANCIAL METRICS FOR DECISION MAKING – SUMMER 2020 ASSIGNMENT General Instructions and Information § § § § § § § § This assignment accounts for 40% of students’ final grade for 25624 Financial Metrics for Decision Making. The assignment is to be undertaken individually. The assignment is due on Friday the 5th of February 2021 (Week 11) by 5pm. The assignment must be submitted via UTSOnline. You’ll need to provide a written report and an Excel spreadsheet: • The written report must be self-contained and formatted as a PDF file. • Excel files will also be examined and will constitute 20% of the value of the assignment. The Excel file should include all calculations. The scope of this assignment is limited to [5] pages not including appendices and cover sheet. Use standard fonts (think Calibri, Times New Roman, Arial) and standard font sizes. There is no specific word count. You are encouraged to use figures and tables when reporting your results. The file names, for both the report and the Excel spreadsheet, will take the form: “Name – Student number”. For example, if your name is Jane Doe and your student number is 12345, then your file name will be “Jane Doe - 12345”. Please don’t write the words “name”, “student number” or anything else in the file name. All assignment-related questions should be posted to the Discussion Board on UTSOnline. Marking § § § This assessment will be graded on the quality of both, the written report and the quantitative analysis in Excel. Marks will be awarded 70% for content and analysis, and 30% for effectiveness of communication and presentation. Late submissions will be allocated a mark of zero with no exceptions unless via special consideration filing. Files In the Assignment folder on UTSOnline, you’ll find the following files: § Cover Sheet: is the cover sheet you’ll need to fill in, sign, and submit along with your written report. § Data: this Excel spreadsheet contains the following worksheets: • • Cover: you’ll need provide your student details here. Part 1 to Part 4: these worksheets contain the data (when applicable) for each part and should be used to perform all relevant data analysis required. Instructions Part 1 – Hypothesis Testing [10 marks] The national average annual salary for a campus manager is $89,000 a year. A state official took a sample of 25 campus managers in the state of New South Wales (NSW) to learn about salaries in the state and see if they differed from the national average. The data for this question is provided in the worksheet named ‘Part 1’. a. [5 marks] Formulate the null and alternative hypotheses that can be used to determine whether the annual salary mean of a campus manager in NSW differs from the national mean of $89,000. b. [5 marks] What is the p-value for your hypothesis test in part (a)? At a 5% significance level, can your null hypothesis be rejected? What is your conclusion? Part 2 – Modelling [40 marks] Background Information Your boss, a real estate business manager, has approached you for financial advice. She is interested in either purchasing or leasing a new car for her personal use. Aware of your financial expertise, she has asked you to develop a Spreadsheet Model that allows her to decide whether to buy or lease the vehicle. The retail price of the car she is interested in is $50,000. Buy Scenario In the Buy Scenario, your boss would like to purchase the car by making an initial down payment of $15,000 dollars and finance the difference with a conventional car loan to be repaid monthly for 3-years at a 5% interest rate. The following table summarises the relevant information for the Buy Scenario. Buy Scenario Car Price $ 50,000.00 Down Payment $ 15,000.00 Interest Rate 5% Term 3 years Lease Scenario In the Lease Scenario, there is no initial down payment. Instead, your boss would like to use a Finance Lease to rent the car for 3 years. At the end of this 3-year period, she plans to purchase the car from the lease financier (lessor) by paying a residual value of $25,000. In this scenario, to rent the car, your boss would have to pay a monthly rent of $850 for 3 years. The following table summarises the relevant information for the Lease Scenario. Lease Scenario Car Price $ 50,000.00 Residual Value $ 25,000.00 Monthly Rent $850 Term 3 years Note: A Finance Lease is a common way people can use a car without actually buying it. Under a Finance Lease, the car belongs to the financier (lessor) who rents it out to the borrower (lessee) in exchange for monthly instalments. At the end of the lease term, the lessee has the option to claim ownership of the car by paying a residual value. a. [5 marks] Lay out the decision-making problem, the alternatives, and the overall criteria you would use to evaluate the different alternatives. b. [5 marks] Carefully establish all the inputs and assumptions you would include in the Spreadsheet Model for each scenario. If you include inputs/variables other than the ones provided (e.g. interest rate on savings), justify your choices based on data from the Australian market. c. [10 marks] Based on your answers to a) and b), build a Spreadsheet Model which helps your boss decide whether to buy or lease the vehicle. Make your spreadsheet selfexplanatory. d. [5 marks] Perform What-If analysis for at least one of your inputs (e.g. down payment). That is, show what would happen to your model’s output at, at least, three different values of the chosen input. In your spreadsheet, highlight the section you would present to your boss to help her with her decision-making problem. e. [5 marks] Of all the inputs included in your model, which one do you think is the most important in determining whether buying or leasing is the best option for your boss? Provide an explanation. f. [5 marks] Describe the model’s limitations and/or aspects that could be improved. What other factors haven’t been considered? g. [5 marks] Are there any cognitive biases you would suggest your boss to be aware of when finally making her decision? Part 3 – Simple Linear Regression [20 marks] The Toyota Hilux is the top selling car in Australia. The price of a previously owned Hilux depends on many factors, including the number kilometres (kms) travelled. To investigate the relationship between a car’s kms and its sales price, data was collected on a sample of 20 used Hilux in Sydney. The data for this question is provided in the worksheet named ‘Part 3’. a. [2 marks] Create a scatter plot for this data with kms as the independent variable. What does the scatter plot indicate about the relationship between price and kms? b. [5 marks] Estimate a simple linear regression model with price as the dependent variable and kms as the independent variable. What is the estimated regression model (equation)? c. [5 marks] Test whether each of the regression parameters (intercept and coefficient) is equal to zero at a 5% significance level. Interpret the coefficients of the estimated regression parameters and discuss whether these interpretations are reasonable. d. [4 marks] Using the model estimated in part (b), calculate the predicted price for each of the cars in the sample. Based on the difference between the true and predicted prices, identify the two cars that were the biggest bargains. e. [4 marks] Suppose that you are considering purchasing a previously owned Hilux that has been driven 100,000 kms. Use the model estimated in part (b) to predict the price for this car. Is this the price you would offer the seller? Part 4 – Multiple Linear Regression [30 marks] A financial institution has a large dataset of information provided by its customers when they apply for a credit card. This customer information includes the following variables: • Annual household income (in thousands of dollars) • Household size (number of people) • Number of years of post-high school education • Number of hours per week watching television • Age • Gender In addition, the financial institution has records of the credit card charges accrued by each customer over the past year. The data for this question is provided in the worksheet named ‘Part 4’. a. [5 marks] Plot histograms to contrast the distribution of annual credit card charges for 1) People with zero years of post-high school education vs. People with at least 1 year of post-high school education, and 2) Female vs. Male. Describe the overall shape of each histogram and comment on any observable differences. b. [10 marks] Estimate a multiple linear regression model in which the dependent variable is the credit card charges accrued by each customer in the data over the past year, and the independent variables are all the variables the financial institution collected when the customer first applied for a credit card (e.g. annual household income). What is the estimated regression model (equation)? a. Hint: For Gender, create a dummy variable that takes 1 if the customer is female and 0 if male. c. [15 marks] Interpret each of the regression coefficients and comment on both their economic and statistical significance. For each significant regressor (at a 5% significance level) provide a potential explanation for its statistical relationship with the dependent variable. Finance Discipline Group UTS Business School FINANCIAL METRICS FOR DECISION MAKING – SPRING 2020 ASSIGNMENT COVER SHEET 1. Name: ________Wei Xiang_______________________ 2. Student No.: ________13338900__________________ Declaration I have carefully read, understood, and have taken into account all the requirements and guidelines for this assignment. I affirm that this assignment is my own work; that it has not been previously submitted for assessment; that all material which is quoted is accurately indicated as such; and that I have acknowledged all sources used fully and accurately according to requirements. I am fully aware that failure to comply with these requirements is a form of cheating and could result in disciplinary action in accordance with UTS Student Rules Section 16 – Student misconduct and appeals. Signature(s): Wei Xiang Date: 16/10/20 Table of Contents Part 1-Optimisation……………………………………………………………………………...3 Part 2 – Descriptive Analysis………………………………………………………………..3-4 Part 3 – Hypothesis Testing…………………………………………………………………4-5 Part 4 – Simple Linear Regression…………………………………………………………5-6 Part 5 – Multivariate Linear Regression……………………………………………………6-7 References……………………………………………………………………………………….8 Appendix……………………………………………………………………………………..9-14 Part 1 – Optimisation Following are the 10 stocks that are randomly selected from the S&P500 list, using the ‘stock selector’ cells in the spreadsheet. Table 1. Selected Stocks Stock 1 CE Stock 2 NOW Stock 3 CTAS Stock 4 SJM Stock 5 SNA Stock 6 NDAQ Stock 7 LLY Stock 8 HLT Stock 9 CNC Stock 10 AVGO Based on the sample selected, the study forms an equally weighted portfolio of the daily returns (Appendix). Using Excel Solver, the study formulates portfolio B, maximising Sharpe Ratio for the given period. Following the portfolio weights and the Sharpe ratio. Table 2. Portfolio weights Stock selector 93 413 107 265 418 331 290 231 94 78 Stock Symbols CE NOW CTAS SJM SNA NDAQ LLY HLT CNC AVGO Portfolio A weights 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% Portfolio B weights 7% 123% 124% 85% -115% -15% -45% -48% -30% 14% Table 3. Sharpe Ratio Mean 0.17% 0.62% Portfolio A Portfolio B Standard Deviation 2.94% 4.84% Sharpe Ratio 0.057 0.127 It can be observed that the Sharpe ratio for portfolio B is greater than portfolio A. It means that investing in portfolio B is financially more feasible and profitable. However, a Sharpe ratio higher than 1 is generally considered acceptable (CFI, 2020). Part 2 – Descriptive Analysis Table 4. Portfolios’ descriptive analysis Daily Annualised Risk-adjusted measures Portfolio Obs. Average Returns Standard Deviation Downside Risk Average Returns Standard Deviation Downside Risk Sharpe Ratio Sortino Ratio Portfolio A 126 0.17% 2.94% 2.08% 43.19% 46.75% 33.04% 0.91 1.29 Portfolio B 126 0.62% 4.84% 3.08% 155.97% 76.95% 48.94% 2.02 3.17 Portfolio B have more the daily average returns and the risk associated. Moreover, the annualised returns follow the same pattern as for the daily returns. The annualised returns for portfolio B are more than three times the annualised returns for portfolio A. Figure 1. Portfolio A Daily Returns The returns for portfolio A follow a normal distribution with the majority of the returns lie between 0 % and 2 %. Figure 2. Portfolio B Daily Returns On the other hand, the returns for portfolio B are mostly greater than -3% (from -3% to 4%). It may suggest that Portfolio B is more profitable but exhibits a greater variation (standard deviation). It also has a greater downside risk, as shown in table 3, suggesting greater financial risk associated with the portfolio (Sortino & Van Der Meer, 1991). Part 3 – Hypothesis Testing Figure 3. Portfolio A and S&P500 Using figure 3, the distribution of portfolio A and S&P 500 returns can be compared. Both the returns have a similar distribution with a mean value around 0. Similarly, the histogram of portfolio B is also compared with the S&P 500 (Figure 4). It can be observed that the mean value of portfolio B is greater than zero. Figure 4: Portfolio B and S&P500 To test the difference in the mean daily returns of the portfolios with S&P500 index returns, a t-test was conducted between portfolio A and S&P 500, and portfolio B and S&P 500. The hypothesis for the tests is as follows: Table 4. Portfolio A vs S&P500 Null hypothesis Alternative hypothesis Table 5. Portfolio B vs S&P500 uA = uS&P500 Null hypothesis uB = uS&P500 uA > uS&P500 Alternative hypothesis uB > uS&P500 The null hypothesis for the first test states that the mean returns portfolio A and S&P 500 are equal. Based on the results given below, the mean for portfolio A is 0.17 %, and the returns of the S&P 500 are 0.14 %. Moreover, since the p-value of the t-test is 0.467, which greater than the level of significance. Therefore, the null hypothesis is failed to be rejected at 5 % level of significance (t-test is applied using the excel T. Test function). The null hypothesis for the second test states that the mean returns portfolio B and S&P 500 are equal. In the second t-test, the mean return for portfolio B is 0.62 % in contrast to 0.14 % for the S&P 500, based on the data of daily returns. However, despite difference, the null hypothesis is again not rejected at 5% level since the p-value = 0.1703. It means that the mean daily returns of portfolio B is also not higher than mean returns of S&P500 statistically. Part 4 – Simple Linear Regression Portfolio B vs S&P500 20% 0% y = 1.0066x + 0.0003… -20% -20% -10% 0% 10% Excess Return S&P500 Figure 5: Scatterplot Portfolio A vs S&P500 20% 20% Portfolio B Portfolio A vs S&P500 Excess Return Portfolio A Excess Return In the first step, excess returns of both portfolios and S&P500 are computed. With the help of excess returns, the following charts are formulated. 0% y = 0.8533x + 0.005… -20% -20% -10% 0% 10% 20% Excess Return S&P500 Figure 6: Scatterplot Portfolio B vs S&P500 Based on figure 5 that is a positive linear relationship between returns of portfolio A and S&P 500. The data points are very close to the line of best fit. The R square shows that 92.6% of the data is explained by the equation/model above. However, in the case of excess returns of portfolio B and S&P 500 (Figure 6), the data points are more scattered. The trend line shows a positive linear relationship between the two variables. The R square is 24.56 % which shows that only 25 % of the data is explained by the equation above. The Beta for portfolio A is came out to be 1 and for portfolio B the beta was 0.853 (Shown in figure 5 and 6). For portfolio A, beta value indicates that its price activity is strongly correlated with the market. A stock with a beta of 1.0 has systematic risk. A beta value that is less than 1.0 in the case of portfolio B, this suggests that security is theoretically less prone to changes than the market. Inclusion of this stock in the portfolio makes it less risky than the same portfolio without the stock. Therefore, a 1% increase in an average excess return of the market would lead to a 1% increase in the average excess return of Portfolio A and 0.85% increase in the average excess return of Portfolio B. Part 5 – Multivariate Linear Regression To examine the impact of new COVID cases on portfolio returns, simple linear regression is done. For the first regression, the dependent variable is the return of Portfolio A. The result shows that a one per cent increase in COVID cases decreases the return of portfolio A be 0.03 %. The regression model only explains 9.6 % of the data. Moreover, the overall regression model is statistically significant due to low p-value, F = 13.15, p < 0.05 (Table 6). Table 6. Excess Return Portfolio A & 7-day Moving average % change Table 7. Excess Return Portfolio B & 7-day Moving average % change In portfolio B (Table 7), new COVID cases decrease the return of portfolio B by 0.049 % (based on the coefficient value). This regression model has an R square of 7.8 %, which is lower than the previous regression model. Moreover, the regression model is also statistically significant since the p-value is less than the level of significance, F =10.56, p < 0.05. It shows that an economic relationship exists between new COVID-19 cases and portfolio returns, which is found to be negative. That means the economy, with respect to stock performance, has declined due to changes in the COVID-19 cases. Table 8. Excess Return Portfolio A, Excess Return S&P500 & 7-day Moving average % change Table 9. Excess Return Portfolio B, Excess ReturnS&P500 & 7-day Moving average % change To determine the impact of new COVID cases and S&P 500 on portfolio return, multiple linear regression is conducted. Both S&P 500 and COVID cases have a positive impact on the returns of portfolio A. In this regression model, S&P 500 has a significant impact on the returns of portfolio A, whereas the new COVID cases are not significant in predicting portfolio A returns. The R square is also very high, which is around 92% (Table 8). It means that controlling for excess returns of the market index, the impact of COVID cases becomes insignificant in predicting individual portfolio returns. In the case of portfolio B, S&P 500 (Table 9) has a positive impact, whereas new COVID cases have a negative impact on the returns of portfolio B. Similar to the previous regression model, the S&P 500 is statistically significant, and new cases are not significant. The R square of the model was 26 %. Based on the regression results, new COVID-19 cases do not have significant impact on the returns of the portfolio. References CFI. (2020). Sharpe Ratio. Retrieved from https://corporatefinanceinstitute.com/resources/knowledge/finance/sharpe-ratiodefinition-formula/ Sortino, F. A., & Van Der Meer, R. (1991). Downside risk. Journal of portfolio Management, 17(4), 27. Appendix CE NOW CTAS SJM SNA NDAQ LLY HLT CNC AVGO 3/3/2020 -1.78% -4.29% -2.59% -0.46% -3.07% -0.05% -1.90% -3.24% -2.54% -3.76% 3/4/2020 3.41% 4.68% 5.04% 5.13% 3.75% 5.72% 7.73 ...
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