Capital Budgeting Decision an Investment

Question: Discuss about the Capital Budgeting Decision an Investment. Answer: The capital budgeting decision is the evaluation of an investment based on the future returns. The evaluation of an investments potential is done on the basis of various assumptions and variety of factors. The estimated accuracy of future returns and therefore, the accuracy of the investment decisions would to a great extent depend upon the precision with which these factors are forecasted. There are strong reasons to believe that howsoever carefully these factors are forecasted, the actual returns will vary from the estimate. This is technically referred to as risk. Capital Budgeting decision involves two steps first, determination of relevant cash flows and second evaluation of cash flows. The NPV and IRR provide two major parameters for the capital budgeting decisions. NPV where considers the time value of money, IRR provides a compounded rate of return at which the firm will be indifferent between two projects. Sensitivity Analysis Sensitivity Analysis is a visualizing particularly the way change in the projects various investment factors like the NPV/IRR for a given range of variables. It is an analysis of a projects sensitivity as described by its NPV/IRR to a particular variable. It provides with the reasons to oversee that how sensitive are the parameters to the overall result of the project. Sensitivity analysis takes a number of possible outcomes and estimates the chances of errors while evaluating a project. The sensitivity analysis measures the cash flow estimates based on three assumptions which are mentioned under:- The NPV/IRR of project is calculated under these different assumptions. In most of the cases, the Worst assumption gives the least or negative NPV, the Expected assumption gives a neutral NPV and the Best assumption provides the highest possible NPV. This method of calculating NPV/IRR by evaluating under various estimates is called sensitivity analysis. The actual selection of project under above assumptions however, depends on what is the investors risk appetite. The decision maker could be a risk taker, risk averter or risk neutral. The sensitivity of the NPV provides the investor to take decisions based on his perception for risk under various situations. The more the NPV deviates, the more the variable is risky. It can be normally applied to number of variables, which is a part of input for the cash flows after tax. The one more added name for sensitivity analysis is break-even analysis. The investor is in no profit and no loss situation. Margin of Safety is another important term for break-even analysis. It shows the positive aspects of a project. The more the margin of safety, the more profitable is the project for the investor. One can ask what shall be the consequences of volume or price or cost changes. In capital budgeting decision one more variable i.e. life of the project is also included for sensitivity analysis. In other words, we can say, how much lower (Break even value) can the sales volume or sales price or project life become or how much higher costs (Break even value) can be incurred before the project becomes unprofitable in terms of NPV. Here break-even point means a point where NPV is zero. The decision rule is, higher the Margin-of-Safety (MOS), lesser the sensitivity of the variable. It can be performed on a spreadsheet format to provide easy and timely analysis for decision making.(Southampton, n.d.) Scenario Analysis Scenario Analysis, as the name suggests, is similar to sensitivity analysis in the way it is calculated. However, in practice, the variables on the basis of which a projects sensitivity analysis is done will be interrelated. Based on the interrelated variables, the decision maker can develop some expected scenarios. For example, by reducing unit-selling price, it may be possible to increase volume. This is called Scenario Analysis. Sensitivity analysis is an old and ever accepted measure but in spite of being crude it does not provide the decision maker an insight into more than one estimate of the projects outcome and the variability of the returns. Scenario analysis provides superior results compared to the single future forecast. It gives a more accurate precision about the variability of the returns. However, scenario analysis also has a limitation that the frequencies with which a variable may occur remain undisclosed. To remedy this shortcoming of scenario analysis, sometimes, probabilities are assigned to the possible variables. This gives a more accurate idea of the profit or loss from a given project under various possible situations. The capital budgeting techniques like a projects Net present value, Internal Rate of Return, Profitability Index or Accounting Rate of Return etc. provide us an insight into different projects profitability aspects.(ICAI, 2016). The scenario analysis, thus, brings the capital budgeting outcomes into more detailed versions providing us information about how different projects may perform under various scenarios like when the selling price gets reduced how much the sales volume is needed for profit or when the material costs increases how much the selling price needs to be increased for profit or when the labor availability gets scarce or when the labor gets costly how much more revenue the project needs to earn for required profitability. One of the important variables for analyzing a project is the capital costs required. The capital costs may include buying some machinery, equipment, setting up a plant or incurring the initial expenditure for the project. The capital costs included in a project is one of the significant factors for considering the feasibility of a project. Apart from availability and market factors, one of the important other factor is the absorption capacity of the revenue earned from the project for the amortization costs of the capital outlay. Scenario analysis takes into account all these factors and provides an investor an opportunity to invest his money in most profitable project. Scenario analysis is therefore an important capital budgeting tool and can be used by different investors for different purposes. The Capital Asset Pricing Model is introduced by Jack Treynor, William Sharpe, John Lintner and Jan Mossin and the Capital Market line is introduced by Henry Markowitz in his risk-return assessment articles; provide useful parameters for the portfolio management.(ICAI, 2016) Similarities between two Models: Henry Markowitz in his studies always considers risk as an important factor. Even the smallest investments have to bear some amount of risk. He assumes that there is nothing called as risk free investment from which returns could be earned without bearing any risks. In fact, in real market situation also, investors do shift to risky investments to obtain higher returns. Therefore, the CML gives a straight line depicting various risk-return scenarios. Similarly, the graphical presentation of CAPM expresses the basic theme of the model i.e. the expected return and risk of a security depends on each others frequency, as measured by Beta. This presentation is called Security Market Line (SML). The SML also shows a straight line depicting various scenarios of relationship between risk and return. Both considers risk benefits with risky securities In a Capital Market Line situation, the investor has different options. First is to invest the entire capital in risk free securities. This kind of investment will yield fixed returns with no risk at all. Second option will be to invest the entire capital in the risky securities available in the market which we call as Portfolio M.(ICAI, 2016). This investment will be the riskiest one where expected return will be equal to the anticipated risk of the portfolio. The last option will be to make different combinations of the both the securities i.e. the risk free security and the Portfolio M. In this situation, the investor may further borrow from the market at risk free rate and invest entire amount in the risky securities of market. Alternatively, he may divide the investment based on his risk return attitude. Similarly, the CAPM model also considers combining the return expected from a portfolio into risk free return plus risk premium. As it is seen, an investor will always be ready for more return and bear some risk for earning same. The security risk premium {(Rm-Rf)} can be calculated as the difference between the expected markets return and risk free rate which we multiply by the securitys risk factor.(ICAI, 2016) Both helps in gaining arbitrate benefits Both the CML and CAPM help the investors in gaining arbitrage benefits. The investor when finds it unprofitable for himself in the investment then he may switch to other options available in the given risk-return perceptions. The various risk-return scenarios can be adopted serially or the graph should be revised day after day to get a closer picture of the expected results. The investors in both the models may consider the graphs and update them according to the market situations to gain arbitrage benefits. Differences between two Models: The CML uses Standard Deviation as the measure of risk of various securities. The CML equation(ICAI, 2016), can be read as- Er = Rf + (Rm-Rf) x SDp The CML equation helps in calculating the amount of Portfolio M and risk free security in a portfolio for a given amount of return. Further, there is a thing called slope of CML which shows the rate of relationship between the expected return and risk. The CML means that for differently hedged portfolios, the systematic risk and the unsystematic risk yields the total returns. It believes that all unsystematic risk could be hedged and diversified and systematic risk has to be accepted. Therefore, the slope shows a linear relationship between risk and return. CAPM, on the other hand, provides a structure for calculating the systematic risk of an individual security. The systematic risk only could be related to the portfolio. The CAPM believes that the unsystematic risk can only be eliminated and the systematic risk cannot. Therefore, the returns are the results of systematic risk only. Therefore, while measuring the performance of a portfolio using CAPM the systematic risk is the only risk factor which contributes in earning the desired returns and it should be compensated. It means that greater expected returns accompany higher levels of Beta. Accordingly SML equation(ICAI, 2016), can be written as- Er = Rf + B (Rm-Rf) The CML, as discussed earlier, takes into account both risky and risk free securities and maintains a portfolio of a combination of both. The investor according to his risk appetite decides as to whether he should invest his 100% investment in the risky Portfolio M or he should make a combination of risky and risk free securities to play safe. This risk free and risky combination provides investor with the hedging facility. CAPM however only considers investing in risky securities and doesnt makes a combination of risky and risk free securities. It is based on the assumption that some portion of the portfolio will always be the risk free securities and the return from that part will be constant. Therefore, this model does not use weight factor in its equation. Unsystematic risk has nothing to do with CAPM The CML model uses Standard deviation as the risk measurement factor. Standard Deviation is an absolute measure, which can be applied when the projects are giving same NPV. Thus, it measure both systematic and unsystematic risk. According to the CAPM model, the performance of a security under various circumstances in the comparison of the market portfolio is the measure of the risk. Beta is a measure the systematic risk of a security. It measure how sensitive the price of a stock to market movements. The market portfolio beta factor is 1.0. Bibliography ICAI, 2016. Portfolio Theory. [Online] Available at: https://www.icaiknowledgegateway.org/littledms/folder1/chapter-7-portfolio-theory.pdf[Accessed 22 January 2017]. Southampton, n.d. Spreadshe et Sensitivity Analysis. [Online] Available at: https://www.southampton.ac.uk/~jps7/D8%20website/sensitivity%20analysis.pdf[Accessed 22 January 2017].