Corporate Finance Analysis: Security Market Line vs Capital Market Line, Minimum Variance Portfolio, and CAPM Equation

This article discusses the difference between security market line and capital market line, importance of minimum variance portfolio, and relevance of CAPM equation in corporate finance analysis. It explains the formulas and graphs associated with these concepts and their applications in portfolio management. The article also provides references for further reading.

Corporate Finance Analysis

TABLE OF CONTENTS
Difference between security market line and capital market line..............................................3
Importance of minimum variance portfolio...............................................................................4
Relevance of CAPM equation....................................................................................................5
References..................................................................................................................................8

DIFFERENCE BETWEEN SECURITY MARKET LINE AND
CAPITAL MARKET LINE
The capital market line takes place from combining the market portfolio and the risk-free
asset. All related aspects along with CML have top-notch profiles of risk-return to the
portfolio on the optimal scope (Smith, 2015). CML deviation is assessed by marking a
tangent line straight from the intercept point over the efficient frontier unless and until the
possible return matches to the risk-free rate of return.
The formula for CML is E(Ri) = RF + SDc×{E(RM)-RF}/SDm.
Wherein the SDC is termed as the portfolio standard deviation C return, on the other hand,
the SDM is referred to as the standard deviation based on market return (Sharpe, 2017).
Figure 1: Capital market line graph
(Source: Sharpe, 2017)
 The line starting from RF to L is considered as the capital market line
 x=risk premium
 y=risk
 slope=x/y

 y-intercept=RF
The security market line is regarded as the representation of the CAPM in a graphical way. It
showcases the potential return rate of the individual security, acting as an element of
structural as well as non-divisible risk (Lee and Su, 2014). At the same time, when there is
the use of SML within portfolio management, then it reflects the scope for investments costs,
whereby the investment is aligned with the risk-free asset and market portfolio. The Y-
intercept of the SML is equal to the free of risk interest rate; the slope of SML is equal to the
premium related market risk, stating the tradeoff of risk-return at a specific period of time.
The formula for SML is E(Ri) = RF + bi×{E(RM)-RF}
Wherein the E (Ri) means the security’s expected return; RF herein is the risk-free rate, bi is
the abbreviation of beta coefficient of security, expected market return is abbreviated as E
(RM) (Vlad, 2016).

Figure 2: Security market line
(Source: Sharpe, 2017)
 beta=1.0 is engaged with more risk in the market
 Securities A and B are stated as intensely risky than the market
 Beta > 1.0
 Security C possess a lower risk in comparison to the market
 Beta< 1.0
Under-priced If the Expected return is higher in
comparison to the required return under
CAP, then it is on the top of the SML

Overpriced If the Expected return is lower in
comparison to the required return under the
CAPM, then it lies underneath the SML
(Shah and et al., 2014).
Priced in a correct manner When the Expected return is equalled to the
required return under the CAPM, and then it
lies with the SML.
The Capital Market Line (CML) shows the rates of return which is based on the risk-free
rates of share as well as the level of risk for a particular portfolio. Security Market Line
(SML) is also known as Characteristic Line; this shows the risk and return of market at a
given period of time by the help of graphical representation (Lee and Su, 2018).
One of the major difference between CML and SML is that how the factors of risk are
calculated. At the same time, the standard deviation is considered as the risk measure for
CML. On the other hand, the beta coefficient identifies the factor of risk related to SML.
CML determines the risk factor either from the standard deviation or from the total risk
factor. However, the Beta coefficient is used to determine the risk factor of SML, which
assists in finding out the risk of security for a particular portfolio (Williams and Dobelman,
2017). Although the CML graphs describe the efficient portfolio, and SML graphs describe
both efficient as well as non-efficient portfolios.
While computing the return, the portfolio’s expected return for CML is shown together with
the Y-axis. On the other hand, the return of securities for SML is shown together with the Y-
axis. The X-axis shows the standard deviation for the portfolio for CML, while, for SML the
Beta of security is shown on the X-axis.
While CML measures the risk-free assets and the market portfolio, and the SML measures all
security factors. Contrasting with the CML, SML demonstrates the expected returns of
individual assets. CML find out the return or risk for the efficient portfolios, and SML finds
the return or risk for an individual stock (Jylha, 2018). Therefore, the CML is considered
comparatively better as compared to the SML when it comes to measuring the factor of risk.

IMPORTANCE OF MINIMUM VARIANCE PORTFOLIO
A minimum variance portfolio shows the portfolio consists of individual risk assets that are
hedged at that time when traded jointly, as a conclusion it shows the minimum possible risk
in comparison to the rate of expected return (Yanushevsky and Yanushevsky, 2015).
With the help of this strategy, the best use of minimum variance portfolio is that investors
incorporate investments from or risk-based assets, and achieve higher returns without the
involvement of risk factor.
The reason for using OLZ investment related philosophy is that it is capable of making better
improvisation in the portfolio composition in terms of the asset classes. Measuring the
superior weighting of the own investment is based on the predicted risk parameters for
example relationship and volatilities (Maillet, Tokpavi and Vaucher, 2015). To this note,
OLZ tends to assess the prevailing findings and the results of financial market research.
Further, the structured portfolio optimization improves the efficiency and diversification,
thereby reducing risk and increasing return.
The risk factor is low because of the better diversification; the minimum variance portfolio is
placed close to the efficient frontier and an optimum return-risk ratio in comparison to the
index (Yang, Couillet and McKay, 2015). Thus, optimum diversification provides less
instability in a portfolio that significantly minimizes losses whether the market corrections
are conducted.
Higher return is achieved because of the lower velocity premium; through the empirical
financial market studies, it is proved that inventories with reduced prices up and down can
attain the higher returns (Bodnar, Parolya and Schmid, 2018). Additionally, the lower
velocity premium is utilized through the use of minimum variance portfolio in an optimum
way, as the correlation with own investments is considered as well.
RELEVANCE OF CAPM EQUATION
CAPM refers to the capital asset pricing model which represents the relationship between
expected return and systematic risk for investment, any asset or specific security (Mazzola
and Gerace, 2015.). In order to assess risk along with its measures to reduce risk; CAPM
method specify three terms: risk, risk aversion and the relationship between risk and return.
Risk refers to the potential of unexpected events to occur. Generally, an attempt is made to

avoid risk to the possible extent as an assessment of risk can be stated as one of the most
crucial tasks of a financial manager. However, in case, they have financial alternatives than
the alternative having a minimum risk is usually adopted by investors. The second component
aversion represents the rate or tendency with which risk can be avoided. A positive
relationship exists between risk and rate of return because the higher the risk, the more return
is expected from the investment.
The model is applied significantly throughout finance for pricing highly speculative
securities. It is used to generate an expected rate of return in accordance with the given risk
rate of an asset as well as the cost of capital. The fact cannot be denied that each investment
comprises a certain risk. For instance, even in case of equity, a variance exists between actual
and expected return. An investor is of a conservative nature. Thus he takes a decision relating
to investment when he agrees to accept risk for an expected return. As per the assertions of
Tompkins (2015), CAPM is believed more appropriate in comparison to other pricing models
as it considers both vital variants of investment decision which is cost as well as risk.
CAPM model provides assistance to the investor for ascertaining the price which should be
paid for specific security through providing its fair price (Chandra, 2017). The same can be
accessed through consideration of standard deviation, i.e. the risk of one security is believed
higher in comparison to another one. Further, the price of security will variant in accordance
with the risk specified. The specified concept is applied in order to ascertain the rate of return
of security, i.e. the higher rate represents a higher market price, and lower rate represents a
lower market price. CAPM assist an investor in evaluating investment risk as well as the
expected rate of return. The model initiates through adding return rate of risk-free securities
with the reward rate expected by the investors as they are accepting higher risk. Another
variant applied by CAPM is the beta coefficient which represents systematic risk relating to
stock which evaluates the sensitivity percentile of a specific stock in relation to changeability
in the market. Systematic risk can be specified as a risk which cannot be diversified. The
reason behind same is the implication of external factors of organization, and same is
uncontrollable in nature. Every model has its own pros and cons; the same is with the existing
model too. The main criticism which is attached to this traditional model is that historical
data applied to evaluate the expected return rate as well as systematic risk (Ho and
et.al.2017). CAPM is based on market symmetrical assumption and data available in the
market. The model permits an investor to quantify the expected return on investment at
specified investment risk, risk-free rate of return, beta (systematic risk) and expected market

rate of return. The main reason due to which CAPM is believed to be viable is as it focuses
on systematic risk in order to ascertain the rate of return.
Figure 3: Formula of CAPM
(Source: Lee and Lee, 2015)
Arbitrage pricing theory is also applied for the ascertainment of return of an asset. However,
unlike the CAPM arbitrage pricing theory does not indicate the identity as well as the existing
risk factors. As APT (arbitrage pricing theory) applies multiple factors which comprise non-
company factors and also requires asset’s beta in relation with each specified factor.
However, the theory does not provide what these specified factors could be, hence the
application of APT theory might lead to use to analytically implement the variants which
affect the rate of return of the asset. In accordance with the study of Berk and Van
Binsbergen (2016) APT develops a framework which represents the theoretical rate of return
of a security as a linear function of the risk of asset relating to set of factors capturing
systematic risk. On the contrary, the factor which is applied in the Capital asset pricing model
is the difference between the risk-free rate of return and expected market rate of return. As
CAPM is a one-factor model, it is comparatively easier in the application. The main
advantage which is attained through application of CAPM is that it considers systematic risk
as unsystematic risk can be diversified. Further, it also develops a theoretical relationship
between the rate of return and risk rate of security.

Figure 4: Capital Asset Pricing Model
(Source: Lee and Lee 2015)

REFERENCES
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