Assessment task 2: Individual Report Analytical and Presentation Video Recording

Assessment Task 2

Returns Distribution, Its Factors, and Stock Market Efficiency

You have recently assumed the role of trading manager at an Investment Research Firm, entrusted with advising on investment strategies for Australian stocks. As a trading professional, your responsibilities include overseeing and managing trading activities, making strategic decisions to optimize performance, and mitigating risks.

Your role is pivotal in ensuring the success of your trading desk or investment team, heavily reliant on your ability to analyze vast financial data and derive actionable insights to achieve profitable trading outcomes.

Your inaugural task involves conducting a comprehensive analysis of the ASX market. Leveraging your expertise in data processing, technical knowledge, and computational skills, you are tasked with the following assignments.

a. To start, evaluate whether share prices on the ASX accurately reflect the underlying value of firms, which would indicate an efficient market with unpredictable price movements.

i. Using the financial data provided below, estimate the share price of the newly listed MAF210 firm on the ASX and comment on whether it appears to be overvalued or undervalued. The primary task is to calculate the terminal value (TV) at the end of 2028, which serves as the foundation for the valuation. The firm is currently in its second year since listing.

• The discount rate, represented by the weighted average cost of capital (WACC) considering the riskiness of future cash flows, stands at 7.5% (assumed to remain constant).
• The long-term free cash flow growth rate is set at 4% (presumed to remain constant).
• In the previous year’s (2023) financial statements, the firm reported cash reserves of $111 million and a debt of $4646 million. At the end of 2023, the share price and the number of outstanding shares were $65 and 170 million, respectively.
• Projected end-of-year cash flows (in millions of $) for the next 5 years (2024–2028) are 390, 483, 930, 630, and 640.

Tips: 

• Calculate the terminal value (TV) at the end of the year 2028 by assuming that the Free Cash Flow (FCF) of $640 million in the year 2028 will grow forever at a rate of 4% in the future. Write the TV as the geometric series of present values of FCF in the year 2028, representing a growing annuity forever.
• Add the TV at the end of the year 2028, which is the sum of the present values of all future FCFs from the year 2029 onward. This results in having the FCFs for years 2024–2028 as 390, 483, 930, 630, and 640 +TV.
• Calculate the firm value at the end of 2023, which is the present value of the FCFs for years 2024–2028 (since the TV has been added to FCF at the end of the year 2028).
• Adjust the firm value for cash and debt and divide it by the outstanding shares to derive the implied
  share price. Compare this with the reported share price at the end of the year 2023 to assess any
  differences.
• Using the information provided above, Figure 1 below sets up the problem to assist you in calculating
  the share price. Please be aware that when you input the TV and NPV, the “Implied value per share” will automatically adjust.

ii. Graph the continuously compounded returns of the All Ordinaries (AORD) index to extract information regarding the distributional characteristics of the returns. What distributional features do you observe?

iii. What patterns or characteristics stand out in the time series plot of AORD returns?

b. Utilizing the 68-95-99.7 rule of thumb for normal distributions, present evidence indicating that the distribution of the AORD returns deviates from the normal.

c. Compare artificially generated (normally distributed) returns data with AORD returns data through a valid graphical representation. Use the sample mean, sample standard deviation, and sample size of the AORD returns in Monte Carlo simulations. Which features of the returns offer evidence of differences between the two data sets?

d. What is your evaluation regarding the directional movement of the AORD index the following day under the following scenarios: 

1.  when the distribution is unknown,
2. when it follows a normal distribution,
3. when it follows a uniform distribution between the lowest and largest return values in the dataset?

e. How would you persuade your team to advise the client on whether to invest in the AORD index for the next 30 days? Construct your argument based on an acceptable hypothesis testing procedure, utilizing a 4-point hypothesis structure.

f. What insights can you provide about the efficiency of the ASX market? Ground your discussion on the following task: 

• Estimate and present the following regression (time series) models for the AORD returns:

1. rt = μ + ϵt Random Walk model with Drift (RWD)
2. rt = μ + ρrt−1 + ϵt Autoregressive model of order 1 (AR1)

g. Many of us have either invested or plan to invest based on our expectations of reaping returns from financial assets (such as bank deposits, shares, etc.) or real assets (such as homes, etc.). Based on your observations or expectations, formulate the null and alternative hypotheses for the relationship between each of the following variables and the continuously compounded returns of the AORD Index (AORD_returns):

1. Liquidity of the financial instrument.
2. Risk of the financial instrument.
3. Month January of the year.

Summary of Assessment Requirements

The assessment task required the student, in the role of a trading manager, to evaluate the returns distribution, its influencing factors, and stock market efficiency in the context of Australian equities. Key requirements included:

1. Valuation of a newly listed firm (MAF210) by calculating terminal value (TV), firm value, and implied share price, then comparing it with the reported share price.
2. Analysis of AORD index returns through graphs, distributional characteristics, and time-series plots.
3. Application of the 68-95-99.7 rule to test normality and highlight deviations from a normal distribution.
4. Comparison of real AORD returns with simulated normal data using Monte Carlo techniques.
5. Forecasting AORD index movements under different distributional assumptions.
6. Formulating an investment advisory stance using hypothesis testing with a 4-point structure.
7. Testing ASX market efficiency using time series regression models (Random Walk with Drift, AR(1)).
8. Developing hypotheses on the relationship between AORD returns and factors such as liquidity, risk, and seasonality (January effect).

Step-by-Step Mentoring Approach

The academic mentor guided the student through the assessment in a structured, step-by-step manner:

1. Understanding the Valuation Task

   • The mentor first explained the concept of free cash flow to firm (FCFF) and how the terminal value (TV) represents the present value of perpetual growth beyond the forecast period.
   • Together, they calculated the NPV of cash flows (2024–2028) and added the discounted TV.
   • Adjustments for cash and debt were discussed, followed by division by outstanding shares to find the implied price.
   • The student then compared this with the actual ASX price to assess overvaluation/undervaluation.

2. Exploring Distribution of Returns

   • Using AORD returns data, the mentor guided the student in plotting time series graphs and histograms.

   • Discussions focused on skewness, kurtosis, and volatility clustering—contrasting them with features of a normal distribution.

3. Applying the 68-95-99.7 Rule

   • The mentor explained how to calculate standard deviation ranges and check what proportion of returns lie within them.

   • The student identified deviations, proving the data was non-normal.

4. Monte Carlo Simulation & Data Comparison

   • The mentor introduced the concept of artificially generated normal distributions using AORD’s mean and standard deviation.

   • The student plotted both datasets, highlighting differences such as fat tails, skewness, and clustering in AORD returns.

5. Forecasting Scenarios

   • The mentor walked the student through forecasting under three assumptions: unknown distribution, normal, and uniform.

   • This step emphasized the uncertainty of predictions when real data deviates from normality.

6. Investment Decision Using Hypothesis Testing

   • A 4-point hypothesis framework (null, alternative, test, decision) was applied to build a structured argument for advising clients.

   • The mentor emphasized evidence-based decision-making.

7. Testing Market Efficiency

   • The mentor explained random walk and AR(1) models, teaching the student how to set up regressions and interpret coefficients.

   • Results were discussed in relation to weak-form efficiency of the ASX market.

8. Formulating Hypotheses on Key Factors

   • The student, with guidance, drafted null and alternative hypotheses linking liquidity, risk, and January effect with AORD returns.

   • The mentor ensured the hypotheses aligned with empirical finance research standards.

Outcome and Learning Objectives Achieved

By following this step-by-step guided approach, the student:

• Completed the firm valuation exercise and assessed relative pricing accuracy.
• Learned to analyze real-world return distributions and compare them with theoretical models.
• Applied rules of probability and hypothesis testing to financial data.
• Understood forecasting limitations under different distributional assumptions.
• Gained hands-on exposure to Monte Carlo simulations and regression analysis.
• Critically evaluated the efficiency of the ASX market using statistical and econometric tools.
• Developed the ability to frame clear investment hypotheses based on liquidity, risk, and seasonality.

In the end, the student demonstrated a strong grasp of both technical finance concepts and practical trading insights, bridging theory with application.

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