Risk and Return Measures: Evaluating the Financial Performance of a Company

This article evaluates the risk and return measures of a company and explains the use of CAPM model to estimate expected return. It also discusses the portfolio creation to reduce risk and generate high returns. The article is relevant for finance students and researchers.

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Table of Contents
3.a Drawing on the expectations from their and incorporating the overall context of the
chosen company, while discussing and interpreting the risk and return measures:...................2
Reference and Bibliography:......................................................................................................6

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3.a Drawing on the expectations from their and incorporating the overall context of the
chosen company, while discussing and interpreting the risk and return measures:
The overall expectation theory mainly indicates the overall minimum returns, which is
expected by the company to provide over the period. The expectation theory is mainly reliant
on the CAPM formula, where the expected return of an organisation estimated by the
investor. The theory relevantly compliments the expectation theory, which allows the
investor to detect the minimum requirements from their investment. In this context, Aliu,
Pavelkova and Dehning (2017) stated that expectation theory only detects the behaviour of an
individual regarding certain measures, while it does not depict the exact response of the
individual. In addition, the expectation theory does not allow investor to understand the
minimum requirements, which needs to be provided, while it does not depict the actual
returns that might be generated by the organisation. Moreover, the use of CAPM model
relevantly evaluates the financial performance of new risk involved in investment with the
risk-free rate and market premium to determine the minimum return that could be generated
from investment.
The overall chosen company JH-HI-FI relevantly has a beta of 0.45, which is derived
from relevant source. Moreover, the beta indicates overall risk involved in investment, which
could directly affect returns from investment. In addition, beta evaluation of the company
indicates that the risk provisions of the company are relevantly low in comprising with the
overall market risk. Therefore, with the evaluation of beta overall expected return form
investment can be identified, which might help in improving the level of returns from
investment. In this context, Hoffmann and Post (2017) stated that with the expected theory
condition investors can evaluate the risk and return of an investment.
Particulars Value

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Risk-free rate proxy 2.75%
Market risk premium 6.50%
Case company beta 0.45
Case company expected return 4.44%
Hypothetical company beta -0.25
Hypothetical company expected return 1.82%
The above table mainly represents the overall risk and return attribute of JB-HiFi and
hypnotical company, which could help the investor in creating and adequate portfolio that
might reduce the risk from investment. In addition, the risk attribute of the overall companies
has mainly changed the expected return that is estimated by the investors. The beta of 0.45
for JB-HiFi has relevantly indicated an expected return of 4.44%, which could allow
investors to generate high returns from investment. However, the difference in beta has
mainly changed the overall expected returns of the hypothetical company, whose overall
expected returns is 1.82% with a beta of -0.25. This relevantly indicates that the company’s
risk attributes of the company play a vital role n determining the expected return form an
investment. Zhang, Liu and Xu (2014) stated that CAPM calculation mainly derives the
return generated from an investment is by evaluating their beta or risk. However, the
estimation or expectation for the investment return with only beta could not help in
determining the actual risk involved in investment. Moreover, with the help of portfolio
creation companies are mainly able to reduce their risk from investment and generate high
rate of return.

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Therefore, from the evaluation it could be understood that the investment in
hypothetical company is mainly adequate, where the overall risk is lower than 0. This
relevantly indicates that the hypothetical company will not be affected by the volatile capital
market and will adversely provide return to the investors in comparison with the price
movement of the capital market. Hung et al. (2018) stated that research and pharmaceutical
companies are mainly considered save bets, which relevantly has higher rate of return from
investment that is inversely correlated with the market. Furthermore, the correlation attributes
and beta detection is mainly conducted by the investor to create a position, which could
eventually help in minimising the risk from investment, while maintaining the level of returns
from investment. on the contrary, Aliu, Pavelkova and Dehning (2017) argued that during a
financial recession the risk attribute of a position greatly increases, which drastically hampers
the returns that could be generated from investment.
Particulars Expected
return
Beta Portfolio
weight
Contribution to portfolio
expected return
Contribution to
portfolio beta
Case company 4.44% 0.45 50% 2.21957500% 0.23
Hypothetical
company
1.82% -0.25 50% 0.90812500% -0.13
Total 100%
Portfolio expected return 3.13%
Portfolio beta 0.10
The above table mainly represents the overall portfolio creation that is conducted to
determine the expected return, which could be generated form the investment by detecting the
risk involvement. In addition, the portfolio weight is mainly distributed equally, which has

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substantially reduced the risk from investment, while raising the level of returns.
Furthermore, the created portfolio is also able to generate high rate of return in comparison
with both the stock, which allows the investor to increase their financial performance. The
overall portfolio returns are at the level of 3.13%, while the portfolio beta is at the levels of
0.10. The changes in weight could also alter the overall risk attributes of the investor for
generating high level of returns from investment. Therefore, it could be understood that the
risk attributes of the current portfolio are relevantly low, which changes with weight and
could allow the investor to generate high rate of returns from investment. Zhang, Liu and Xu
(2014) mentioned that portfolio is mainly created in accordance with the risk attributes of an
investor, which could help in generating high level of returns from investment.
In addition, the changes in weights of the portfolio could directly alter the risk and
return attributes of the portfolio. This relevantly indicates that investor according to their risk
attribute could alter the changes in their portfolio. In this context, Hoffmann and Post (2017)
stated that portfolio creation is mainly conducted based on risk and return attributes of an
investment, which could be combined to reduce risk from investment. On the other hand,
Hung et al. (2018) argued that the portfolio creation without conducting adequate research
could increase risk from investment and hamper the investment capital.

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Reference and Bibliography:
Aliu, F., Pavelková, D. and Dehning, B., 2017. Portfolio risk-return analysis: The case of the
automotive industry in the Czech Republic.
Reuters.com. (2018). ${Instrument_CompanyName} ${Instrument_Ric} Quote| Reuters.com.
[online] U.S. Available at: https://www.reuters.com/finance/stocks/overview/JBH.AX
[Accessed 18 Apr. 2018].
Hoffmann, A.O. and Post, T., 2017. How return and risk experiences shape investor beliefs
and preferences. Accounting & Finance, 57(3), pp.759-788.
Hung, K., Yang, C.W., Zhao, Y. and Lee, K.H., 2018. Risk Return Relationship in the
Portfolio Selection Models. Theoretical Economics Letters, 8(03), p.358.
investing.com. (2018). Australia 10-Year Bond Yield - Investing.com. [online] Available at:
https://www.investing.com/rates-bonds/australia-10-year-bond-yield [Accessed 18 Apr.
2018].
Zhang, W.G., Liu, Y.J. and Xu, W.J., 2014. A new fuzzy programming approach for multi-
period portfolio optimization with return demand and risk control. Fuzzy Sets and
Systems, 246, pp.107-126.