Capital budgeting and CAPM/ CML

Question: Discuss about the Capital budgeting and CAPM/ CML. Answer: Introduction: Corporation is comprised of complex set of activities in which various functions are performed. Sensitivity and scenario analysis play very pivotal role in business decision making. It is related with use of critical financial tools, assumptions, forecasting and analysis of available data in sophisticated approach. Ideally corporate decision making is related with purchasing plans and machinery, opting investment proposals (Merger and amalgamation), entering into contract with other parties, developing new ventures, diversification of business and choosing certain projects (Hornstein, 2013). Capital budgeting refers to the process by which a business determines or evaluates whether to take on a certain project. This analysis involves identifying the available resources (Cash outflow) a business wants to deploy in its business functioning and the amount of money (Cash inflow) a project will generate. In the business functioning due to scarcity of resources it is required to manage resources efficiently. It involves use of various financial tools e.g. net present value, internal rate of return, profitability index etc. However, this budgeting plan and its effectiveness could be enhanced with the use of sensitivity analysis and scenario analysis in certain manner. Management of company at the time of selecting project has to analysis and compares its positive and negative outcomes. Sensitivity analysis and scenario analysis provides relative assumptions and relative data in context with the changing factors of the market. It helps organizations to prepare back up plan in ca se of worst condition and productive plans in case of best conditions of the market (Tian Jiang, 2015). Sensitivity analysis It is an analysis which helps a business forecast what will happen to the project opted if estimates and assumption taken turn out to be unreliable and variable. It is accompanied with the activities which involves adapting assumptions or estimation in certain manner as per the required situation. It prepares organization to develop an effective business plan which could handle all the type of risk arises in different economic circumstances. However, this analysis is useful to prepare managers and other investors in case of opted project dose not generate expected cash. It helps in determining better analyzing the project before making an investment plan (Crestaux, et., al., 2009). Scenario analysis It is designed or prepared to see the result of an action under different factors. It assists in preparing an effective capital budgeting plan to evaluate how investments NPV would differ in different factors e.g. high and low inflation, worst case, best case or other factors as may be changed. (Seitzinger, et., al., 2010). A prepared scenario analysis should be feasible to identify the different set of outcomes in different plans. In capital budgeting it is used to analyzing possible different future events by considering alternative possible outcomes. Use of both analyses in capital budgeting technique Both analyses are used to handle the uncertainty in capital budgeting plan prepared by financial manager of the company. Sensitivity analysis provides analysis of effects of changes in sales, costs, initial investment, and interest on loan and present value factors. According to Drury NPV of the organization should be calculated under alternative assumptions to determine how sensitive they are to changing conditions. For example a business may expect to earn $ 1000, $2000 and $ 3000 (in case of IRR -10 %) in first three years of investment project plan. (Batra Verma, 2014). It means that if initial investment of $ 6000 is made then it will be recoup its expenses within three years. But if IRR is changed to 20 % due to market positive outlook then all the money will be recoup in less time and business will break even in shorter time period. IRR and NPV will be high if relative factors are positively influence the selected project plan and vice- versa (Lilburne Tarantola, 2009). On t he other hand Scenario analysis gives a particular fusion of assumption with a certain factors. In this analysis IRR and NPV may be different in different scenario. With the help of below illustration it could be said that in normal case NPV of the project would be moderate but in best case it would be increased by very high amount and at the same time if conditions are worst then it will be identified as loss making project. The purpose of scenario analysis is not to evaluate or identify the exact conditions or certain factors of opted project in capital budgeting but it just provides a guidance and prepare management for the all the conditions which could happen (Saxena, 2015). Factors Normal case Best case Worst case Yield - + 10 % - 20% Exchange rate - + 10 % - 10% Transportation cost - -5% +20% Marketing cost - -5% +20% Sales cost - + 10 % - 20% Sales price 1.03 1.05 1.00 Cash inflow 17 % 29 % 1 % NPV 1 2.2 -2.7 Risk in capital budgeting Capital budgeting is a financial tool which is related with making assumptions and estimates about the future performance. However, due to various factors these assumptions and estimates turn out to be wrong and project may depict negative results. For instance if IRR is considered to be 10 % in normal condition and due to worst conditions cost of capital is increased by 15% then IRR will also be increased at the same time. At the same time proposed NPV will also be affected if the same assumptions are changed due to uncertain market condition of the business. Therefore in order to mitigate risk in capital budgeting both analysis are very effective. This analysis depicts the actual impact on present value of cash inflow in case of 20% higher sales, base analysis and 20 % lower sales. With the help of both analyses it would be possible to indicate those variables to which NPV is most sensitive, and the extent to which these variables may be change in different particular situation. Besides, it is also helpful in capital budgeting to evaluate and control of all variables which might affect the NPV and IRR of the initial project investment plan. It is also observed that capital budgeting is very effective tools for determining the best project plan in certain business conditions. Sensitivity analysis and Scenario analysis provides a systematic plan to prepare managers to make changes accordingly if existing circumstances do not remains the same. CAPM model This model describes and establishes nexus between systematic risk and expected return for the identified assets (stocks, scriptures). Ideally investors use this CAPM model in order to evaluate time value of money and risk in their capital investment projects. The standard CAPM pricing model assist in determining the return which is required by the investors in particular risk undertaking venture. CAPM model helps in identifying the expected rate of return of security and portfolio. In simple words CAPM model provides that expected rate of return of a security match with rate on risk- free security plus a risk premium. Therefore if the given expected return does not meet the required rate of return then the investment project should not be undertaken. CAPM models takes into account following factors such as Ke (cost of capital) RF( return on government security) Rm ( Market risk) beta (related risk of the security with the changes in the market conditions) (Bornholt, 2013). The Risk free rate of return (Rf) is 4 % and beta of the same is 1.5 the expected market return over the period is 15 %. Therefore market risk premium would be 11% (15-4). After applying the formula (RF+ (RM-RF) B expected rate of return would be 20.5 %. This result is derived on the basis on assumption that all the investors are having homogenous expectation from the market. Capital market line It is the line that is used to depict the rate of return which is based on the risk free rate of return and particular level of risk (Standard deviation). This line helps investors to measure the risk with the use of calculation of standard deviation and helps investors to evaluate efficient and non- efficient portfolios. It is understood that capital market line is very effective tool to measure the risk associated with the particular scripts and portfolio. It is the efficient frontier including the possibilities of risk free lending and investing. It is found that CML does not consider that portfolios are well diversified but it considers both risks (systematic and unsystematic risk). However, when portfolio is well diversified then in that case only systemic risk will be taken into consideration in tangent line. Efficient frontier is the set of portfolio that offers the highest expected return with minimum risk. In simple words it could be said that CML is the line of efficient frontier portfolios which will provide high return with minimum level of risk. However, there are other line (CAPM, SML) which divulges all types of portfolio either efficient or non-efficient. This both lines provide clear idea about market portfolio, expected return, and beta coefficient (Zabarankin, et., al., 2014). Similarities between two models A line which is used in Capital Assets Price model is to describe the rate of return for efficient portfolios based on the risk free rate of return and standard deviation for portfolio. The CML is considered to be superior frontier as it takes into account only efficient portfolio. However, CAPM provides that the entire market portfolio is risk- free assets and efficient frontier (Dempsey, 2013). Both models provide a clear guidance to investors in graph format to assist investors to identify efficient frontier portfolio. In simple words it could be divulge that both are a measure of risk and return of script portfolio and allocate risk free assets in both equations. In addition, they also help in calculation of market return with the particular securities. SML manifests the role of CAPM in graph representation accompanied with set of unrealistic formula and use make graphical representation of CAMP formula. CML also based on assumption and depicts highly return showing portfolio based on given level of risk (Obrimah,et., al., 2015). Differences in two models (CAPM and CML model) Nature CAPM CML Presentation It is the graph representation of CAPM formula. It is efficient frontier risk free assets graphically present return and standard deviation with particular portfolio. Tangent It has systematic risk (Beta) on its X- axis. It has Total risk which is highly volatile on its X- axis. Assumption It is assumed that investors are well diversified and have systematic risk (Dempsey, 2013). It is considered that investors are unknown with market factors and it is better to evaluate systematic and unsystematic risk for better results. Risk factors In case of CAPM risk is shown as beta coefficient Risk is associated with the return of the portfolio and shown as sigma. It is also known as standard deviation. Portfolio assets It shows risk and return for individual assets ( Scriptures and stocks) It provides risk and return for group of assets e.g. portfolio of scriptures. Both above models are best evaluating tools for the investors to make investing decisions in scriptures and other stocks of the organizations. However, these models are used by different set investor as per their choice of actions based on several factors. Ideally it is assumed that CML model is very much useful for the investors who are less likely to take risk in their investment plans. References Batra, R. Verma, S. 2014, "An Empirical Insight into Different Stages of Capital Budgeting",Global Business Review,vol. 15, no. 2, pp. 339-362. Bornholt, G. 2013, "The Failure of the Capital Asset Pricing Model (CAPM): An Update and Discussion: The Capital Asset Pricing Model",Abacus,vol. 49, pp. 36-43. Cai, C.X., Clacher, I. Keasey, K. 2013, "Consequences of the Capital Asset Pricing Model (CAPM)a Critical and Broad Perspective",Abacus,vol. 49, no. S1, pp. 51-61 Crestaux, T., Le Ma?tre, O. Martinez, J. 2009, "Polynomial chaos expansion for sensitivity analysis",Reliability Engineering and System Safety,vol. 94, no. 7, pp. 1161-1172. 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