Corporate Financial Management - Security Market Line & Capital Market Line

Running Head: CORPORATE FINANCIAL MANAGEMENT
CORPORATE FINANCIAL MANAGEMENT
Name of the Student
Name of the University
Author Note

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Table of Contents
Introduction................................................................................................................................2
Discussion..................................................................................................................................2
Security Market Line & Capital Market Line........................................................................2
Minimum Variance Portfolio Importance..............................................................................5
Relevancy of CAPM Equation...............................................................................................6
Conclusion..................................................................................................................................7
Reference....................................................................................................................................9

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Introduction
The management of corporate finance is the act or way of developing plans and
making investment decisions, which affects the operations of business positively. It is
consists of goals and plan setting for achieving it and then deciding best possible way for
paying them. In this concern, capital budgeting is defined as the formal process that entity
uses to evaluate potential expenditures or investments. It is related with decisions regarding
current funds (Lee & Su, 2014). Therefore, this report includes the discussion regarding the
way SML differs from CML. Further, discussion will be on MVP importance and lastly,
relevancy of CAPM equation will be discussed.
Discussion
Security Market Line & Capital Market Line
CML
CML is graphically depicted line. This line represents linear relationship in between
total amount of risk and expected return for efficient portfolios of risky as well as riskless
securities. It is graphically representation of the expected return of the portfolios that is
comprised of all the possible proportion between risk-free asset and market portfolios. There
is complete diversification, carries only the systematic risk and expected market return is
equal to expected return of the of market portfolio (Williams & Dobelman, 2017). Generally,
the calculation of expected return of particular portfolio is done by following formula:
“E(Rc) = y × E(RM) + (1 – y) × RF”
In this y is the market portfolio proportion, “E(RM)” is “market portfolio’s expected
return”, “(1-y)” is “risk-free asset’s proportion” and “Rf” is “risk free rate”. The return of
non-leveraged portfolios can be equal or less than market return, but leveraged portfolio’s

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return can significantly increase the return of market (Balteș & Dragoe 2017). The equation
of CML can be as follows:
E(Rc) = RF + SDc
E(RM) - RF
SDM
CML depicts all the possible portfolios combination that is comprised of various
proportions of portfolio of market and the risk-free market. The efficient frontier helps in
representing all the possible combinations of the efficient portfolios, consisting of only the
risky assets in the different proportions. CML’s intercept point and the efficient frontier calls
tangency or market portfolios. In case, if there is risk-averse or rational investor then higher
risk will be expected only in the situation when there is proportionate increase in the return. It
is from this particular standpoint, portfolio of tangency is considered to be the most efficient
portfolio (Hong & Sraer, 2016). CML is graphically depicted below:

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SML
In comparison to CML, SML is concerned with representing CAPM graphically,
which helps in demonstrating relationship between required rate of the return on individual
security, as systematic risk function, which is not diversifiable. It depicts various market
systematic risks levels of different plotted marketable securities against market expected
return at given period. In comparison to CML, in SML graph helps in describing non-
efficient and efficient portfolios. For any given security, market risk premium determination
is given where it is plotted on chart (Lee & Su, 2014). SML equation is stated below:
“E(Ri) = Rf + βi [E(RM) – Rf]”
The primary differences in between the CML and SML is measurement of the risk.
Further, measurement of risk of the CML is by standard deviation or total factor of risk. In
case of SML, measurement of the risk is done by beta, which helps in finding out security’s
contribution of risk for portfolio. The market is thought of as indicative index of market or
the universal assets. The beta equals to one then in that situation stock is having same level of
the risk as of market. If the beta is greater than one then in that situation it represents risky
assets compared to market. If beta is less than one then it represents risk that is less than the
market (Balteș & Dragoe, 2017). SML is graphically depicted below:

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Minimum Variance Portfolio Importance
MVP is defined as portfolios of the securities, which helps to reduce overall
portfolio’s price volatility. The term volatility is commonly used concept. MVP indicates the
portfolio, which comprises of the individually risky assets. Further, these risky assets are
being hedged, when their trading is done together. The outcome of this is lower possible risk
for rate of expected rate of the return. MVP leverages of each individual asset’s risk with the
offsetting of investment (Bodnar & Gupta, 2015). Therefore, it hedges total risk of portfolio
for accepted level of risk in respect of expected portfolio return rate. MVP is the risk based
approach for construction of portfolio. It means that rather than using return and risk
information as in portfolio selection of Markowitz, the construction of portfolio is done with
the help of using risk measures. The reason behind opting for risk-based approach by
investors is that the future or expected returns are difficult for estimating. However, the risk
is not easy to estimate. It lead towards more robust portfolio, which is less subject to the risk
estimation (Bodnar, Mazur & Okhrin, 2017).

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MVP is associated with modern theory of portfolio and efficient frontier. It is
considered to be unique portfolio, which is on efficient frontier. Most of MVP varies from the
traditional mix of portfolios of stocks and bonds. In comparison to investment in mix of the
lower risks bonds and higher risks stocks, it is the blend of highly volatile securities with the
lower correlation. The logic behind this is the fact that by combining set of the volatile
securities that don’t move with each other, investor hedges against losses, while increasing
the profit (Yang, Couillet & McKay, 2015).
MVP is model of the portfolio, which is combination of investment that are volatile
individually. However, most individuals perceives it as of having lower risk, when this is put
together. MVP mixes investment with the lower level of correlation. This correlation helps to
measure that how much two different investment moves with one another. Searching of exact
correlation has the requirement of advanced knowledge of data and mathematics. However,
when talked about MVP, this is good for learning about its utility. The application of MVP
technique is considered to be most popular to create strategies of equity and investment fund
as well as other similar uses (Xing, Hu & Yang, 2014).
Relevancy of CAPM Equation
In the subject of finance, CAPM is model used for theoretically determining
appropriate rate of the return required of the asset and making decisions regarding adding of
assets to the portfolios that is well-diversified. It helps in describing relationship in between
asset’s expected return and systematic risk, especially stocks. Throughout finance, CAPM is
used widely to price the securities that are risky and generating assets expected return, given
cost of capital and risk of those particular assets (Ruffino, 2014). The CAPM formula is as
follows:
“ERi=Rf+βi(ERm−Rf)”

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Investors expects for compensating for time value of the money and risk. The rate of
risk-free rate in formula of CAPM accounts for time value of the money. Further, other
components of formula of CAPM accounts for investor taking on the additional risk.
Moreover, beta of the potential investment is the measure of total investment’s risk will add
to the portfolio, which looks like market. In case, if the stock is risky than market, then beta
will be greater than one. Further, when stock is having beta that is less than one then portfolio
risk will be minimized (Alqisie & Alqurran, 2016).
There are different benefits of CAPM over the other methods of calculating the
required return. It takes consideration of only systematic risk. In this, there is diversification
of portfolio by different investors, which helps to decrease the unsystematic risk. The
technique of CAPM is considered as good equity cost calculating method in comparison to
growth model of dividend (Lee, Cheng & Chong, 2016).
However, rather than various benefits of CAPM, it is highly criticized for being
unrealistic due to the assumption on which model is based. These assumptions includes that
the investors generally holds diversified set of the portfolios, the transaction is in the terms of
single-period horizon, there is perfect capital market and lastly, investors lends and borrows
at risk-free rate of return (Bhattacharya, 2015).
Conclusion
Hence, this report reaches at the conclusion that CML is theoretical representation of
different blend of risk-free asset and portfolio of market for given Share ratio. Its basis is
drawn from CAPM and capital market theory. Generally analysts uses this to derive required
return, which may be expected by the investors to take certain amount of risk in the portfolio.
SML in comparison to CML is representing CAPM graphically for giving returns that are
expected from market. This is better method for making comparison of two investments or

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the securities. Although, this depends on the assumptions of the risk of market, risk-free rates
and the coefficients of beta. Moreover, the report concludes that the MVP helps in depicting
portfolio, which is well-diversified. It is consists of the risky assets, which are being hedge
when traded together. This results into decrease level of risk for expected rate of the return. It
ascertains lower bond of the efficient frontier. Further, despite of the criticism, CAPM is still
relevant compared to the other equations.

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Reference
Alqisie, A., & Alqurran, T. (2016). Validity of Capital Assets Pricing Model (CAPM)
(empirical evidences from Amman Stock Exchange). Journal of Management
Research, 8(1), 207-223.
Balteș, N., & Dragoe, A. G. M. (2017). Rentability and risk in trading financial titles on the
Romanian capital market. Theoretical and Applied Economics, 24(Special), 57-64.
Bhattacharya, R. R. (2015). Capital asset pricing model.
Bodnar, T., & Gupta, A. K. (2015). Robustness of the inference procedures for the global
minimum variance portfolio weights in a skew-normal model. The European Journal
of Finance, 21(13-14), 1176-1194.
Bodnar, T., Mazur, S., & Okhrin, Y. (2017). Bayesian estimation of the global minimum
variance portfolio. European Journal of Operational Research, 256(1), 292-307.
Hong, H., & Sraer, D. A. (2016). Speculative betas. The Journal of Finance, 71(5), 2095-
2144.
Lee, H. S., Cheng, F. F., & Chong, S. C. (2016). Markowitz portfolio theory and capital asset
pricing model for Kuala Lumpur stock exchange: A case revisited. International
Journal of Economics and Financial Issues, 6(3S), 59-65.
Lee, M. C., & Su, L. E. (2014). Capital Market Line Based on Efficient Frontier of Portfolio
with Borrowing and Lending Rate. Universal Journal of Accounting and
Finance, 2(4), 69-76.
Ruffino, D. (2014). A robust capital asset pricing model. Available at SSRN 2355950.

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Williams, E. E., & Dobelman, J. A. (2017). Capital Market Theory, Efficiency, and
Imperfections. World Scientific Book Chapters, 445-510.
Xing, X., Hu, J., & Yang, Y. (2014). Robust minimum variance portfolio with L-infinity
constraints. Journal of Banking & Finance, 46, 107-117.
Yang, L., Couillet, R., & McKay, M. R. (2015). A robust statistics approach to minimum
variance portfolio optimization. IEEE Transactions on Signal Processing, 63(24),
6684-6697.