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What is Sensitivity Analysis

Question:

1 Management of a Company could use sensitivity and
   Scenario in theair corporate decision Makings.
      A.Sensitivity Analysis


      B.Sccenario Anaslysis


2.Explain and identify similarities and differences between
   the following two models.


      A.Capital Asset Pricing Model.


      B.Capital market LIne .

A sensitivity analysis may be explained as an approach which helps to determine how the values of an independent variable affect another dependent variable in situations with a different set of assumptions.  The scope of use of these techniques is limited, for example, the effect of a change in interest rates on the bond prices. This analysis more commonly referred to as what-if analysis or also simulation analysis (Arnold, 2010). It helps to analyze how a decision can affect a certain range of variables. Also, using the same set of variables the analyst can determine the effect of a change in one variable on the other.

Let us assume Mr. X to be a sales manager who wants to understand the effect of more customers on sales.  Based on the study he understands that sales are a function of transaction volume and price of the goods.  The price of a good being $1000 per piece, X sold a total of 100 pieces last year, earning a total revenue of $100000. X studies the behavior of a number of customer’s sales and finds out that 10 percent increase in customer base would increase the sale by 5%. This data helps him to builds a financial equation and sensitivity analysis which are dependent on what-if situations.  Now, based on sales of the current year and estimates of what might be the increase in customer base, X can find out the sensitivity analysis of the given situation (Brigham & Ehrhardt, 2011). Therefore, in the given case we see that sales are highly sensitive to any changes in customer base.

Sensitivity analysis helps a person understand the effect on the project or a particular variable if the estimates turn out to be true or false. This analysis helps to measure the effect or impact of the change in the estimates on the finances or the variable. This helps the project managers or financiers understand the reasons for a particular event not generating expected results, which will help them further, analyze the decision of making an investment (Vaitilingam, 2010). In capital budgeting, sensitivity analysis finds out the changes in one assumption or an estimate on an entire project. Let’s just take an example, a business is expected to generate $ 500 in year one, two and three, so the investor invests $ 1500 in the beginning of the year 1. But the change in estimates after year one show that the business would generate $ 1000 in year two and three. This would break even the investments in year two itself (Guerard, 2013). Therefore, sensitivity analysis helps understand the effect in a better way.

Scenario analysis is a tool which helps analyze the uncertainty today by taking into account alternative possible outcomes. It should not be considered as a predictive mechanism it is just an analytical tool. There is an analyst who uses mainly three different scenarios for possible outcomes, the base case, best case and the worst case. The base case is the one with most the highest probability (Brigham & Ehrhardt, 2011). The best case is the one where it is assumed all things go right, and the worst case is the one with all the assumptions failing. So, the analyst will now take steps to cover the present risks which may lead to worst case scenarios. It does not provide exact situations, just the mere idea of a scenario.

How Sensitivity Analysis Works

For example, a project analyst may use scenario analysis in order to determine the net present value of a project under high, moderate and low inflation scenarios. Let us take another example to make scenario analysis clearer. A financial institution may forecast the possible scenarios of the economy, and further, it may try to analyze the scenario financial markets in that various forecast of the economy. The institution may also take into consideration, further scenarios.  The result of all these scenarios will help the institution allocate its resources in the most efficient manner (Brigham & Ehrhardt, 2011).

We are often unsure about the variables and assumptions we make in capital budgeting, in order to remove this uncertainty in decision making we extend the sensitivity analysis to scenario analysis. The decision of what assumption to change and how much to change totally depends on the situation. One method of scenario analysis in capital budgeting are using the ends in extreme scenarios (Damodaran, 2012). As discussed earlier the best and the worst case scenarios are compared with the base case in order to take preventive steps or steps which will lead to positive results. In case the actual results of a project are highly adverse, then taking this type of scenario analysis will help the investors take steps in order to avoid the situations which will lead to these adverse situations.

Both techniques of analysis, sensitivity and scenario can be useful while determining the best possible investment plan. But they both are not the same. A scenario analysis may involve the use of sensitivity analysis but it is not necessary that sensitivity analysis will use the scenario analysis. The sensitivity analysis will help the investor understand various uncertainties involved with the proposed investment project. Whereas the scenario analysis will help analyze various uncertainties and the way they would affect the investment or the outcome of a project. In a sensitivity analysis, the various assumptions are tested whereas in scenario analysis various outcomes are tested.  Though both the techniques are very different altogether, they go hand in hand in many circumstances. Use of both the techniques is very relevant in capital budgeting decision making. Therefore to conclude we can say that both the sensitivity and scenario analysis are important tools in making decisions in capital budgeting (Kandel & Stambaugh, 1995).

The capital asset pricing model is a model that helps in knowing about the investment’s fair value. When the asset return rate is calculated, the rate is used in discounting the cash flow of the future of an investment that pertains to the present value, hence moving to the fair value of the investment. The main role of CAPM pertains to two main factors that are providing a return rate that functions as a reference for evaluation of the investment and then enabling in projecting the expected return that needs to be traded.  Hence, we can differentiate the investment’s fair value with the market value and undertake decisions accordingly. This model projects the idea that securities are priced in a manner so that the returns that are expected will repay the investors for the expected ones (Choi & Meek, 2011).  The CAPM model holds the notion that the investors are required to be compensated in two main ways that are the time value and the risk.  The time value is denoted by rf that repays the investors for investing while the risk-free rate is the government bonds such as U.S Treasuries. Using the model of CAPM, the computation of the expected return for a particular stock can be done.

What is Scenario Analysis

Expected return (ef) = Risk free rate + Beta*(Risk premium)

The capital market line is described as a graph that is derived from the capital asset pricing model (CAPM).  The CML is utilized to arrive at the return rate from the specific portfolio. The rate of return arriving from CML varies as per the risk-free rate of return and the risk level as determined by beta for a specific portfolio.  The CML is ascertained by stretching a line of a tangent from the point on efficient frontier to the point where the risk-free rate and expected return are equal in nature (Bodie et. al, 2014).  Further, it needs to be noted that the CML hold true for a portfolio that is efficient in nature and projects the behavior of the investor that pertains to the portfolio of the market and own portfolios.

The main similarity that can be witnessed in CAPM and CML is that CML is derived from CAPM. CAPM can be established when the CML is included. When it comes to the method of risk adjustment of an asset by using the risk-free rate then the investor can easily influence the profile of risk (Damodaran, 2010). Keeping into consideration the CML, the portfolio of the market is composed of the combined power of the assets that are risky utilizing the assets market value to ascertain the weights.

The CML is extracted from the CAPM and is used to compute the rate of return that is expected.  The CML is stronger than the efficient frontier and considers the concept of a risk-free asset in the portfolio.  The CAPM projects that the portfolio of the market is the efficient frontier (Brigham & Daves, 2012). Therefore, CML is a component of the CAPM and is a line used to signify the rate of return for efficient portfolio dwelling on the risk-free rate and the risk level. Therefore, the properties of CAPM are embedded in CML and hence the properties of the CAPM are present in it (Ferris et. al, 2010)

Ferris et. al, 2010


The CAPM provides the notion that securities can be priced so that the expected return will support the investors for the risks that are expected. CML can be stated as the major factor and an element for deriving the CAPM. CAPM is a model while CML is one of the forms of CAPM.

CAPM can be termed as the best method to ascertain the investor’s return where the compensation will be done for the systematic risk because diversification cannot be done. However, the return can be expected from the investment as per the risk involved (Kan & Zhang, 1999).  When it comes to a potential return, it is required to undertake risk that is more systematic. When the stock holding capacity is more, the business cycle comes to the forefront and therefore the return is derived on an average basis (Berk et. al, 2015).

The CML consider the addition of asset that is risk-free and due to this it is regarded as a higher than efficient frontier.  Every portfolio has a similar Sharpe ratio as compared to the portfolio of the market. In the case of buying or investing, it is important that the Sharpe and CML need to be ascertained of the portfolio (Da et. al, 2012).

Therefore, both the models are equally important and it is important to understand that both the models have more similarity in nature and is used to ascertain the risk profile.  Going by the entire discussion it can be said that the models are complementary to each other and can be used in conjunction with each other

Conclusion

An overall analysis of the report provides strong evidence that CAPM and CML plays an important role in determining the expected return. Both are complementary in nature and hence, hold strong similarities. Overall, it can be said that the concepts hold utmost importance. It is clear that if the expected return cannot beat the required rate of return then the investment must be ignored. Further, the above report also shed light on sensitivity and scenario analysis that are a powerful tool when it comes to capital budgeting. If used properly these analysis tools can help the investors determine uncertainties and take steps in order to eliminate them and have the most positive results from these outcomes.

References

Arnold, G 2010,  The Financial Times Guide to Investing,  Prentice Hall.

Berk, J., DeMarzo, P. & Stangeland, D 2015,  Corporate Finance,  Canadian Toronto: Pearson Canada.

Bodie, Z., Kane, A. & Marcus, A. J 2014, Investments,  McGraw Hill

Brigham, E. & Daves, P 2012,  Intermediate Financial Management , USA: Cengage

Brigham, E.F. & Ehrhardt, M.C 2011, Financial Management: Theory and Practice, USA: Cengage Learning.

Choi, R.D. & Meek, G.K 2011,  International accounting. Pearson Press .

Da, Z., Guo, R.J. & Jagannathan, R 2012, ‘CAPM for estimating the cost of equity capital: Interpreting the empirical evidence’,  Journal of Financial Economics vol. 103, pp. 204–220

Damodaran, A 2010, Applied Corporate Finance: A User’s Manual, New York: John Wiley & Sons

Damodaran, A 2012,  Investment Valuation,  New York: John Wiley & Sons.

Ferris, S.P., Noronha, G. & Unlu, E 2010, ‘The more, merrier: an international analysis of the frequency of dividend payment’, Journal of Business Finance and Accounting, vol. 37, no. 1, pp. 148–70.

Guerard, J. 2013, Introduction to financial forecasting in investment analysis, New York, NY: Springer.

Kan, R. & Zhang, C 1999,  ‘Two-pass tests of asset pricing models with useless factors’, Journal of Finance, vol. 54, pp. 203-235.

Kandel, S. & Stambaugh, R.F 1995, ‘Portfolio inefficiency and the cross-section of expected returns, Journal of Finance’, vol. 50, pp. 157-184.

Vaitilingam, R 2010,  The Financial Times Guide to Using the Financial Pages, London: FT Prentice Hall.

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