1. How their decision making could be related to capital budgeting techniques such as, Internal Rate of Return,in relation to Capital Budgeting Techniques?
2. Explain and identify similarities and differences between the following two models?
Capital budgeting can be defined as looking forward. When dealing with the anticipated resources and demands uncertainty forms the major factor. Sensitivity analysis can be defined as the statistical tool, which helps in determining the consequential deviations from the anticipated value (Bodie, Kane and Marcus 2014). Sensitivity analysis is concerned with locating the amount through which one can change the data input to generate the output of the liner-programming frameworkto comparatively remain unchanged. This assists in defining the sensitivity of data that is supplied for the problem and helps in measuring the optimum levels required for each of the input. Supporting the process of decision making and developing necessary recommendations required for decision makers. This helps in facilitating proper testing of robustness of the results derived.
Sensitivity Analysis helps in enhancing the process of communication from the “modelers” to the decision makers by providing recommendations towards more trustworthy, comprehensible, convincing or credible decisions. Sensitivity analysis helps in increasing the understanding and quantification of the systems which helps in understanding the relationships amid the input and output variables (Borgonovo and Plischke 2016). Sensitivity analysis helps in development of model, which helps in searching and locating errors in the model.
It is noteworthy to denote that to perform a sensitivity analysis all the necessary inputs and considerations are associated with aprocedure in order to yield the output (Dantzig 2016). For instance, a framework of inputs and considerations of an organisation is concerned with producing a new product mightcomprise of information concerning the projectedobtainability of raw material, rate of inflation aamount of workforce engaged in the research and development activity. The output will be regarded as the new profit, which will be produced by the fresh product (Vanderbei 2015). Therefore, sensitivity analysis involves altering each variable and monitors what compels changes in the output. Usually only one variable is alteredat once and all the other factors remained fixed at their base value. This helps in easy determination of number of variable effecting the output. However, it is noteworthy to denote that not all the inputs may be independent as changing inputs at one period is not concerned with the interaction amid the inputs.
Figure 1: Figure reflecting sensitivity analysis
(Source Cohon2013)
Sensitivity analysis helps the management as the methodology of measuring the quantity of risk involved in the projected venture. Sensitivity analysis also facilitates computing the effect of variations relating to several quantifiable elements of a plan and helps the management to recognise the probable pitfalls (Luenberger and Ye 2015). Management can made the use of the sensitivity analysis, as a tool to locate the elements of a plan if changed to some extent will create an influence on the consequence of a project. It is noteworthy to denote that business engages in several form of risk since there is no assurance that a business will earn revenue. Organisationendeavours to reduce the degree of risk as much as possible by remaining engaged in actions that helps the business in earning profit. Managers usually makes the use of sensitivity analysis before executing the plan of a new venture of business activity. Sensitivity analysis helps the managers to assess the factors that would help the project to yield profit by creating an impact on the net profit value of the planned activity (Charneset al. 2013). Managers determine the process of administering risk involved in the new project or activity provided their indulgence following the use of sensitivity analysis.
Scenario analysis can be defined as the procedure of approximating the anticipated value of portfolio on a given period. This involves assuming the exact deviations in the values of the portfolio securities or key elements that takes place such as the change in the rate of interest (Gelmanet al. 2014). Scenario analysis is largely used to govern the changes in the value of portfolio concerning the unfavourable event, which might be used to evaluate a theoretical worst scenario.
As a tool, sensitivity analysis consists of computing different rate of reinvestment for anticipated returns that is reinvested during the investment horizon. Depending upon the principles of statistics scenario analysis provides a procedure to estimate deviation in the value of portfolio. Such deviation is based on the occurrence of numerous circumstances which is referred as scenarios following the principles of what if “analysis”. The scenario analysis can be used as tool to measure the amount of risk, which is present in the given investment associated with variety of probable events ranging from highly probability to highly improbable (Luenberger and Ye 2015). Based on the results derived from analysis, an investor can measure the degree of risk present falling within his comfort zone.
There are several different ways of approaching scenario analysis. One of the collective method is determining the standard deviation of day-to-day or monthly security returns and then calculating the anticipated value for each of the portfolio that generates return having standard deviation above or lower than the average return. Through this method aforecaster can obtain a sensible amount of certainty concerning the change in the value of a portfolio during a given time period.
Scenario analysis process is used to measure the potential investment scenarios, which can be applied to several financial circumstances to determine the shift in values based on theoretical situations. From a consumer’s perspective, an individual can use the scenario analysis to evaluate certain financial outcome while making a purchase on credit as opposed to saving funds for cash purchase. Businesses on the other hand can make the use of scenario analysis to determine the probable financial results of certain decisions (Charneset al. 2013). For example, selecting one of two facilities or storefronts from which business functions can be conducted. This may consist of considerations such as differences in rent, charges associated with utilities and insurance or any kind of benefit that may be available in one location but not in other location.
The capital asset pricing model is considered as the most well-known model for determination of anticipated return on securities and other financial assets. It is regarded as the asset-pricing model since the price of an asset can be backed off once the anticipated return is determined. Furthermore, the anticipated amount of return derived from the CAPM or any other kind of asset pricing model may be used to discount the future cash flows (Barberiset al. 2015). Hence, such discounted amount of cash flow is then added to determine the price of an asset. The capital asset pricing model presented by William Sharpe and John Lintner marks the establishment of an asset pricing theory. The attraction of CAPM is largely due to the simple logic and intuitively pleasing predictions concerning the procedure to measure the risk along with the relation between the anticipated rate of return and risk. The model assumes the risk of investors as averse and at the time of making portfolio selection, it considers the mean variance concerning one period investment return (Nghiem 2015). Precisely, the relevant portfolio choice by the investors is mean variance efficient, which reduces the variance in portfolio return.
The common notion behind the theory of CAPM is that the investors need to compensated in two ways namely, time value of money and risk. The time value of money is represented by the risk free rate and provides compensation to the investors for placing their money in any investment over a period (Dionne, Li and Okou 2013). The risk free rate can be considered as the yield on the government bond like US treasuries. The CAPM theory lays down that the anticipated amount of return of a security or the portfolio is equivalent to the rate of risk free rate with the risk of premium. If the anticipated rate of return fails to meet the required amount of return then investment are not taken into the consideration.
The capital market line can be defined as the kind of graph, which arises from the capital asset pricing model. The Capital asset pricing model is used to determine the theoretically suited required rate of return on a particular asset when such assets about to form a part of the existing and well performing portfolio (Huesecken, Overesch and Tassius 2016). The capital market line is a useful tool, which helps in determining the rate of return amount of efficient portfolios. The analysis of capital market line relies on the risk free rate of return with the amount of risk involved in the portfolio. The Sharpe ratio derived from certain computation highlights the proportion of risk and extra return, which a portfolio provides. Portfolio having the highest amount of Sharpe ratio is considered as the market portfolio. Eachportfolio, which is included in the market portfolio,is optimized for a specified sum of risk. Due amount of consideration is given to the sum of risk which is associated with the particular asset.
According to the capital asset pricing model the market portfolio lays down the efficient frontier. The efficient frontier can be considered as the ingathering of portfolios. When the market ratio is combined with the risk free asset, it is capable of generating higher rate of return than the efficient frontier (Christensen, Hail and Leuz 2016). The combination of market portfolio and the risk free rate paves the birth of capital market line. Several experts have expressed their opinion of preferring capital market line over the efficient frontier. The main reason behind such preference is that Capital Market Line takes into the consideration the additional risk of risk free asset in the portfolio.
Figure 2: Figure representing Capital Market Line
(Source: Christensen, Hail and Leuz 2016)
Capital asset pricing model provides a graphical return on the market risk and return on assets for a given time period. The capital market line acts in the form of liner combinations of the risk free asset and portfolio. Thus, portfolio under the capital market line is considered as mediocre since it reflects new efficient set (Lee and Su 2014). Capital market line is assumed to remain within the capital asset pricing model since it can highlight the rate of return for efficient portfolios, which in most probable scenarios faces the risk of market portfolio, and risk free rate of return.
The difference between the capital assets pricing model and the capital market line are is that the capital market line represents the line, which reflects the rate of return and relies on the risk free rate of return along with the level of risk for a particular portfolio (Pianosiet al. 2016). One of the major differences between the capital market line and the Capital asset pricing model is the procedure of measuring the risk factors. For capital market, line standard deviation is used to measure the risk whereas for Capital asset pricing model beta coefficient is used to determine the risk factors. One of major similarity between the capital assets pricing model and the capital market line is derived by drawing a tangible line from the point of interception where the anticipated rate of return is equivalent to the rate of risk free return.
The capital market line determines the risk by using the concept of standard deviation or by implementing the total risk factors (Jagricet al. 2015). On the other hand, the Capital asset pricing model measures the risk through beta, which helps in determining the risk for security contributing to the portfolio. Another key difference between the capital market line and the Capital asset pricing model is that while the capital market line graphs defines the efficient portfolios, the Capital asset pricing model determines both the aspects of efficient and non-efficient portfolios. At the time of computation of returns, the anticipated amount of return for the capital market line portfolio is highlighted on the Y-axis.
In contrary to this, the Capital asset pricing model the return for securities is represented along the X-axis (Gelmanet al. 2014). The standard deviation of the portfolio is highlighted on the X-axis for capital market line while the beta value of securities is determined on the X-axis for Capital asset pricing model. The market portfolio and the risk free assets are determined by the CML however, all the security factors is determined by the CAPM.
References
Barberis, N., Greenwood, R., Jin, L. and Shleifer, A., 2015. X-CAPM: An extrapolative capital asset pricing model. Journal of Financial Economics, 115(1), pp.1-24.
Bodie, Z., Kane, A. and Marcus, A.J., 2014. Investments, 10e. McGraw-Hill Education.
Borgonovo, E. and Plischke, E., 2016. Sensitivity analysis: a review of recent advances. European Journal of Operational Research, 248(3), pp.869-887.
Charnes, A., Cooper, W.W., Lewin, A.Y. and Seiford, L.M. eds., 2013. Data envelopment analysis: Theory, methodology, and applications. Springer Science & Business Media.
Christensen, H.B., Hail, L. and Leuz, C., 2016. Capital-market effects of securities regulation: Prior conditions, implementation, and enforcement. Review of Financial Studies, p.hhw055.
Cohon, J.L., 2013. Multiobjective programming and planning. Courier Corporation.
Dantzig, G., 2016. Linear programming and extensions. Princeton university press.
Dionne, G., Li, J. and Okou, C., 2013. An Extension of the Consuption-Based CAPM Model.
Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B., 2014. Bayesian data analysis (Vol. 2). Boca Raton, FL, USA: Chapman & Hall/CRC.
Huesecken, B., Overesch, M. and Tassius, A., 2016. Capital Market Reaction to Tax Avoidance: Evidence from Luxleaks.
Jagric, T., Podobnik, B., Strasek, S. and Jagric, V., 2015. Risk-adjusted performance of mutual funds: some tests. South-eastern Europe journal of Economics, 5(2).
Lee, M.C. and 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), pp.69-76.
Luenberger, D.G. and Ye, Y., 2015. Linear and nonlinear programming (Vol. 228). Springer.
Nghiem, L., 2015. Risk-return relationship: An empirical study of different statistical methods for estimating the Capital Asset Pricing Models (CAPM) and the Fama-French model for large cap stocks. arXiv preprint arXiv:1511.07101.
Pianosi, F., Beven, K., Freer, J., Hall, J.W., Rougier, J., Stephenson, D.B. and Wagener, T., 2016. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, pp.214-232.
Vanderbei, R.J., 2015. Linear programming. Springer.
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