Unit Learning Outcome:
A) Assessed through student ability to apply knowledge of multivariate functions, data transformations and data distributions to summarize data sets.
B) Assessed through the student ability to analyze datasets by interpreting summary statistics, model and function parameters.
C) Assessed through student ability to develop software codes to solve computational problems for real world analytics.
D) Assessed through student ability to demonstrate professional ethics and responsibility for working with real world data.
Assignment Objective: The aim of this assignment is to analyze the behaviour and performance of one company using historical data. You can use statistical data analysis and features you have been learning in R.
Assignment Task: Problem Solving
The aim of this assignment is to analyze the behaviour and performance of financial share prices of one company from the listed five Telecom & Utilities companies below:
AGL Energy Limited (AGL.AX)
Telstra Corporation Limited (LS.AX)
TPG Telecom Limited (TPM.AX)
Spark New Zealand Limited (SPK.AX)
AusNet Services Ltd (AST.AX)
You can use Yahoo Finance to download the historical data of the company you have chosen.
By completing this assignment, you are required to write a case study report with 300 words, which should include the following information:
1. Your company's name and the "principal activities" of this company. Choose one company from the list.
2. Collect share price data (daily closing price) for 3 consecutive months from 1 March 2018 to 31 May 2018 inclusive for the company of your choice. Visualize company's share price data (daily closing price) for this period. Describe the general trend of the share price for this period. Produce your own graph.
3. Research the information about modelling of share prices, particularly the stochastic modelling (based on Geometric Brownian motion), to describe the behaviour of the company's share price. A simplified version of the model is given below:
St = S0exp[(μ - (σ2/2)t],
where,
S0 is the initial value
St is the share price on day t (this would be the tth trading day in your analysis)
μ is the drift
σ is the volatility.
Assume that drift and volatility in this model is constants.
You are encouraged to research further the literature about σ and μ. Find and describe the meaning of the drift and volatility. Find appropriate formulas to compute their values - daily and annualized - from the historic data.
4. State the condition that share prices have to satisfy in order to be represented by a Geometric Brownian motion.
Do your data satisfy this condition? Produce appropriate evidence to support your answer.
Hint: the logarithm of the daily share prices change, ln (St/St-1), is normally distributed.
5. Calculate the parameter values σ and μ (daily and annualized values) for the selected periods of three months (March-May) and for the last third month (May). What is the effect of time on σ and μ? Are the parameters constants (with time)? How this could affect the model performance?
6. Estimate the expected value of the share price on 15 June 2018 using the formula given above. Use the parameters σ and μ values, computed from the three months of historical data. Compare with the published real data. Comment on the results.
7. Find information publicly available for your company, e.g. reports and news releases, and discuss their effect on the values of σ and μ, and the values of the share price. Comment on the model's performance and state the restrictions of the model. Link your results to the performance of the company. Summarize all results in the study report with 300 words.
All supporting information should be presented in the pdf report.
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Attachment:- Real World Analytics.rar