MT217 Unit 8 Risk and Return Analysis Example

Course	MT217 Finance
Unit	Unit 8
Paper type	Risk and return analysis
Length	About 880 words, 6 pages double-spaced
Format	APA 7 student paper
School	Purdue University Global
Program	BS in Business Administration
Updated	October 2026

Free sample paper for MT217 Unit 8

Garbage Trucks and Chip Tools: A Unit 8 Risk and Return Analysis of Two Very Different Companies

[Student Name]

Purdue University Global

MT217: Finance

Unit 8 Risk and Return Analysis

[Instructor Name]

[Date]

Both companies, the economic states, probabilities, returns and betas are composite and illustrative; the sources cited are real.

What this part is doingChoosing two companies at opposite ends of the risk range lets every measure show a clear contrast.

The Two Companies

Tri-County Disposal is a composite waste hauler whose revenue comes mostly from multiyear contracts with cities and counties. People produce trash in recessions and booms alike, so its results barely move with the economy. Arclight Process Tools is a composite maker of equipment used to deposit thin films on silicon wafers. Its sales depend on how much chipmakers spend on new factories, which rises sharply in good years and collapses in bad ones.

Part One: Expected Return and Standard Deviation

The analysis uses three economic states for the coming year: weak, with a probability of 0.25; normal, 0.50; and strong, 0.25. The estimated one-year returns on Tri-County's stock are 6 percent in a weak economy, 9 in a normal one and 11 in a strong one. Arclight's are minus 18 percent, 14 and 38.

The expected return is each state's return weighted by its probability. For Tri-County, 0.25 times 6 plus 0.50 times 9 plus 0.25 times 11 gives 8.75 percent. Arclight's weak, normal and strong outcomes, weighted the same way, give 12.00 percent.

Standard deviation measures how far returns are likely to stray from the expected figure. For each state, the gap from the expected return is squared and weighted by its probability; the sum of those weighted squares is the variance, and its square root is the standard deviation. Tri-County's works out to about 1.79 percentage points. Arclight's is about 19.90.

What it means: Arclight offers about 3.25 points more expected return, but its range of outcomes is more than ten times as wide. In a weak year its shareholders could lose almost a fifth of their investment, while Tri-County's would still earn about 6 percent.

Part Two: Risk per Unit of Return

Dividing each company's standard deviation by its expected return, the coefficient of variation, shows how much uncertainty comes with every point of return. Tri-County's is 1.79 divided by 8.75, about 0.20. Arclight's is 19.90 divided by 12.00, about 1.66.

What it means: an Arclight shareholder takes on roughly eight times Tri-County's uncertainty per point of return. On this measure alone, Tri-County looks like the better deal, but the measure treats each stock as if it were held alone.

What this part is doingPointing out what the coefficient of variation ignores sets up the reason for the pricing model in Part Three.

Part Three: What the Pricing Model Says

Most investors hold many stocks, and Markowitz (1952) proved that what an investor bears in a portfolio turns on how its holdings move together, so mixing stocks that rise and fall at different times lowers the swings of the whole. What matters to a diversified investor is not a stock's total variability but how much it moves with the market as a whole. Sharpe (1964) built that idea into the capital asset pricing model, which measures a stock's market sensitivity with beta.

For this analysis, Treasury bills are taken to yield 4.2 percent and the market is assumed to pay 5.5 points above that; each company's required return adds its beta's share of those 5.5 points to the 4.2. Tri-County's beta is estimated at 0.65, so its required return is 4.2 plus 0.65 times 5.5, or about 7.78 percent. Arclight's beta is 1.70, giving 4.2 plus 1.70 times 5.5, or 13.55 percent.

Comparing these with the expected returns from Part One, Tri-County's 8.75 percent is above its required 7.78 percent, so the stock appears to offer a little more than its market risk demands. Arclight's 12.00 percent falls short of its required 13.55 percent, so at its current price it does not compensate investors enough for its sensitivity to the market.

Arclight promises more return, but not enough more for how hard it swings with the market.

Limits

Three cautions apply. The probabilities and state returns are estimates, and small changes to the weak-state return for Arclight would change the conclusion. Betas estimated from past returns may not hold in the future, especially for a company like Arclight whose customers' spending cycles have changed with new chip factories. Finally, Fama and French (2004) reported that beta, on its own, accounts for differences in stock returns far less fully than the model claims, so the required returns above are a starting point for judgment rather than an answer.

What a Portfolio Would Do

An investor who held equal amounts of both stocks would see the swings partly cancel. In a weak year the portfolio would lose about 6 percent, an average of Tri-County's gain and Arclight's loss; in a strong year it would gain about 24.5 percent. The expected return would be about 10.4 percent, with a standard deviation well below Arclight's alone, because Tri-County's returns barely move when Arclight's swing. That is the practical meaning of diversification for someone choosing between these two companies: the choice does not have to be one or the other.

What this part is doingBuilding a simple two-stock portfolio makes the abstract point about diversification concrete with the analysis's own figures.

Conclusion

For a diversified investor, Tri-County looks slightly underpriced and Arclight slightly overpriced, given these estimates. For an undiversified investor holding only one of them, the standard deviations matter more, and Tri-County's narrow range is the main attraction.

References

Fama, E. F., & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46. https://doi.org/10.1257/0895330042162430

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x

Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x

MT217 Unit 8 instructions, in plain terms

Unit 8 typically asks for a risk and return analysis of two or more investments using scenarios or historical data. Most prompts expect expected returns, standard deviations and coefficients of variation computed from probabilities and outcomes, followed by required returns from the capital asset pricing model using beta, a risk-free rate and a market premium. Comparing expected and required returns shows whether each asset appears fairly priced. Explain the difference between total risk, which matters for a single holding, and market risk, which matters for a diversified investor. Many sections reward a short portfolio example showing how combining assets reduces variability. Discuss the limits of the estimates and of the model. State all probabilities and inputs clearly so a grader can check the arithmetic. Most versions run four pages and cite Markowitz and Sharpe directly.

Inside the MT217 Unit 8 example

The analysis introduces the two companies and why their results move so differently with the economy. Part one sets out the three states and their probabilities, computes each company's expected return and explains standard deviation in plain words before giving the results. Part two divides risk by return, and a margin note points out that the measure treats each stock as if held alone. Part three brings in Markowitz on diversification and Sharpe's pricing model, computes each required return and compares it with the expected return from part one, with the highlighted line on the equipment maker falling short. A limits section notes how sensitive the conclusion is to the weak-state estimate and cites Fama and French on beta. A portfolio section averages the two stocks' returns in each state, showing a narrower range, with a second note. The conclusion separates the diversified and undiversified investor.

MT217 Unit 8 rubric: what earns full points

Risk and return analyses are usually graded on accurate statistics, correct use of the pricing model and sound interpretation. Expected returns and standard deviations must be computed from the stated probabilities, and graders often check one state's arithmetic in detail. The coefficient of variation should be interpreted, including what it ignores. Required returns from the pricing model must use the correct formula and inputs. Comparing expected with required return, and saying what the difference implies about price, shows the analysis has a purpose. Distinguishing total risk from market risk is central to the unit. A portfolio example, even a simple equal-weight one, demonstrates diversification concretely. Discussing the limits of probability estimates and beta shows judgment. The Markowitz and Sharpe articles and the Fama and French review should be listed in APA 7, with all inputs stated in the text.

Common MT217 Unit 8 mistakes, and how to avoid them

Risk and return papers often fall short by computing statistics without explaining what they mean for an investor. Another frequent mistake is using the coefficient of variation as the final answer, ignoring that diversified investors care about beta. Some students apply the pricing model with the market return in place of the market premium, which double-counts the risk-free rate. Others compare expected and required returns but never say which stock looks underpriced. Portfolio effects are frequently skipped. Probabilities sometimes fail to add to one. Watch the standard deviation calculation, where squaring and weighting each deviation is easy to get wrong. If your section uses historical returns instead of scenarios, the same interpretation applies. Send our writers your scenario data or the companies you are comparing, and they can help compute the measures and explain what they say about price.

Get an MT217 Unit 8 example written to your instructions

Upload the Unit 8 problem data, including any probabilities, returns and betas your instructor supplied, plus the rubric and any rounding rule. Expected return and dispersion are computed row by row, risk per unit of return is compared, and the pricing model is applied and interpreted. An initial sample costs nothing; allow roughly 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.

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MT217 Unit 8 questions, answered

Why does beta matter more than standard deviation?

Because much of a single company's dispersion can be removed by holding it alongside others, and investors are not paid for risk they could diversify away. Beta measures the part that remains, the sensitivity to the market as a whole. The sample shows both, then uses beta for the required return, which is the reasoning the pricing model rests on.

What does plotting above the security market line mean?

It means the expected return, from the scenario analysis, exceeds what the pricing model requires for that level of beta. In textbook terms the asset looks underpriced relative to its risk. The sample reports the waste hauler's result that way and notes that the conclusion is only as good as the scenario probabilities and returns behind it.

Should the probabilities come from real data?

In most sections the problem supplies them, and the task is the calculation and interpretation. If the prompt asks you to estimate your own, state the basis, such as how often the economy has been weak in recent decades. The sample's figures are illustrative, and the paper says so, which keeps the analysis honest about its inputs.

Where can I find a free MT217 Unit 8 sample paper?

This page holds a full MT217 Unit 8 risk and return analysis of a waste hauler and a chip equipment maker. It computes expected returns, standard deviations and required returns, builds a two-stock portfolio and includes margin notes.

Why does the equipment maker look overpriced in the sample?

Its expected return of 12.00 percent falls short of the 13.55 percent the pricing model requires for a beta of 1.70. At the current price, investors are not paid enough for how strongly it moves with the market.