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Financial Modelling and Analysis
创建日期：2024-07-08 16:33:30
所属分类：金融Finance
标签： Financial
Financial Modelling and Analysis

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25705 Financial Modelling and Analysis


Case Study

This document is available on Canvas under “Assignments/ Case Study”.

25705 Case Study

Instructions

Please access the Stock Allocation - Autumn 2024.xlsx spreadsheet to get the details of the

stocks assigned to you (Main, Bench1 and Bench2) based on your Student ID.

•   Dataset1 | For all three stocks, please download daily data (Price, Cvol, Open,

High, Low) from Factsetfrom 31 December 2009 to 31 December 2023. Seminar 1 in-class activity showshow to do this.

•   Dataset2 | For your all three stocks and the S&P/ASX200 (XJO-ASX), download weekly prices from 24 June 2016 to 30 June 2023.

•   Dataset3 | For your Main stock, also download half-yearly Income Statement from June 2005 to June 2023 (37 periods). If not available from June 2005, please download from the first available period. 

Report Format

•   Please submit your report in PDF format and your workings in an Excel spreadsheet.

•   The report should include your answers and conclusions, as well as the tables and charts you judge relevant.

o Please create a cover page for the report, containing subject number and name, report title, student name, ID, and UTS email.

o All text should be 1.5 lines space with 12-size font.

o The page limit is 10-A4 pages, excluding the cover and the reference list. Any materials beyond the page limit will not be considered.

o Please name the report by including your Student ID number after the original filename (e.g., 25705 Case Study 13333333.pdf)

•   The spreadsheet should contain all calculations and be formatted appropriately:

o One worksheet per Dataset (Dataset1, Dataset2, Dataset3)

o One worksheet per question. Each labelled Q1, Q2, etc.

o Input and calculation formats should be clearly identified

o Calculations should be transparent and show proficiency in Excel

. Hard-coded values are only appropriate for inputs or for outputs of  Data Analysis steps. Please clearly specify if you have used any Data Analysis steps in your calculation.

o Please name the spreadsheet by including your Student ID number after the original filename (e.g., 25705 Case Study 13333333.xlsx)

Submission: Both files (report and spreadsheet) should be submitted on Canvas before 11:59 pm on Friday, 3 May.

Penalty for non-compliance: Failure to follow the instructions on the report format carries a penalty up to 10 marks. A penalty of 10 marks will be exercised for each day (or part of) that the report is late.

Descriptive Statistics and Visual Analysis

Q1. [2 marks] For each of the three stocks you have been assigned, please use Dataset1 to:

•   Calculate daily returns and daily volatility (using the high/low measure).

•   Compute the descriptive statistics for returns, volatility, and volume for the entire period.

•   Compare results across stocks and comment on your findings.

Correlations

Q2. [2 marks] For each of the 3 stocks, please use Dataset1 to:

•   Compute the correlations across returns, volatility, and volume.

•   Compare results across stocks and comment on your findings.

Q3. [2 marks] Please use Dataset1 to:

•   Compute the correlations of returns across each pair of the three stocks:

o Main - Bench1

o Main - Bench2

o Bench1 - Bench2

•   Use a scatter plot chart to illustrate the correlations between each pair.

•   Compare results and comment on your findings.

•   Which of the two benchmark stocks provides more diversification benefits?

Hypothesis Testing

Q4. [2 marks] A colleague asks you to corroborate whether the difference in average returns for the Main stock and the Bench1 stock is statistically significant at the 1% level. Using

Dataset1, please:

•   Formulate the null and alternative hypotheses,

•   Specify if you need to perform a one or a two-tail test, and

•   Run ahypothesis test at the 1% level of significance and provide your conclusion.

Q5. [2 marks] A colleague asks you to corroborate whether the difference in average

volatility for the Main stock and the Bench2 stock is statistically significant at the 1% level. Using Dataset1, please:

•   Formulate the null and alternative hypotheses,

•   Specify if you need to perform a one or a two-tail test, and

•   Run ahypothesis test at the 1% level of significance and provide your conclusion.

Forecasting Volatility

Q6. [2 marks] Using Dataset1, please forecast daily volatility for your stock using an estimation period going from 1 January 2010 to 30 June 2022 and a hold-out period going from 1 July 2022 to 31 December 2023.

•   Implement the SES method to forecast volatility using an initial α defined by you. Use the estimation period volatility data and Excel’s Solver determine the optimal α .

•   Using the optimal SES parameter you obtained, calculate the MSE in the hold-out period and report it in the table provided in worksheet

•   Re-estimate the α using all the data and forecast volatility for 2 Jan 2024.

•   Report and discuss your main findings. Is SES an appropriate method to forecasting volatility?

Simple Linear Regression

Q7. [2 marks] Using Dataset2, please:

•   For each of the three stocks (Main, Bench1 and Bench2), estimate Beta (measure of systematic risk) for:

o The 156 weeks from 1 July 2016 to 21 June 2019

o The 156 weeks 10 July 2020 to 30 June 2023

•   Report and discuss your main findings.

Multiple Linear Regression

Q8. [2 marks] Using half-yearly Sales as reported in the Income Statement (Dataset3), please:

•   Build two alternative multiple regression models you believe will have explanatory power over your Main stock Revenue/Sales. You can source these independent variables from Factset or from other sources. The difference between the two models could be just one independent variable:

o Model 1: 

o Model 2: 

•   Use the first 32 periods as training, and the last 5 periods as test data.

Note: If your company had fewer than 37 periods, the total number of periods minus 5 as training data. For example, if you only have 24 periods, use 19 for training and 5 for testing.

•   Please report and discuss your main findings.

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