Analysis Of Capital Budgeting Practices: Scenario And Sensitivity Analysis Techniques

Sensitivity analysis in capital budgeting

Discuss about the Analysis of Capital Budgeting Practices.

Capital budgeting is defined as the process by which a business organization makes a decision in determining its steps to undertake a certain project. According to Djulic Pindzo (2015), capital budgeting analysis involves the estimation of the amount of money the organization will invest as well as the possible revenue it can generate from the project. Corporate business decisions are adopted while carrying out the capital budgeting process of the firm where different techniques such as net pressure value, the rate of return, etc. are adopted. This paper focuses on the scenario and sensitivity analysis about capital budgeting techniques adopted in business investment plans. It also evaluates the differences and similarities that are identifiable in the capital asset pricing model and capital market line.

In his study, Vjetrov (2016) denotes that sensitivity analysis in capital budgeting involves a clear investigation and understanding of the risks and methods involved that can affect the business in one way or the other. For instance, the organization or business enterprise will require adopting different ways of methods in the evaluation of the possible financial effects on undertaking a particular project. In his study, Vjetrov points out that the management of the business needs to have an understanding of the time frame as well as the value of the project and compare the result with the required investment expenses. Such a strategy enables the company to calculate the time frame that the project requires so as to generate an income that is commensurate to the expenses invested earlier. The business will as well be able to estimate the cash flow from the project with time in comparing the possible results of the business whether it is profitable or will lead to a loss. The method adopted can as well be calculated from the rate of discount at which the project will break even. It hence means that sensitivity budgeting will assist the management of the business to have an additional understanding or insight in their decision-making strategies.

On the other hand, capital budgeting methods require assumptions as well as estimating on the possible performance of the project in the future. The results of the calculations are used by the business shareholders and managers in decision-making towards the profitability of the project. However, Vjetrov (2016) points out that care needs to be taken as the estimations and the assumptions made maybe not accurate. In such a case, the resulting impact is the failure of the project invested by the business. Sensitivity analysis is hence adopted by the organization in understanding the possible direction the project may take in case of the unreliability of assumptions and estimates. The estimates and assumptions made are then changed into calculations to see the financial impacts of the project through sensitivity analysis. Adopting the sensitivity analysis thus prepares the management of the business to better analyze the project before investing in case it is possible that the business will not generate the expected results. In capital budgeting, calculations in the sensitivity analysis hence involve changing one assumption to another to see the possible impact it can create in the changing results of the project. It hence helps in understanding the time of the year the business is likely to turn even after investing on the project.

Scenario analysis in capital budgeting

In their study, Djulic and Pindzo (2015) point out that scenario analysis is the process that estimates the expected or projected value of a business portfolio after a certain period.  However, it assumes the specific changes in the portfolio values and key factors or securities occur like the possible changes in the rate of interest for the business. The same study denotes that scenario analysis is adopted in the estimation of the changes in the value of the portfolio in responding to unstable or unfavorable business event and may be adopted in the examination of theoretical worst-case business scenarios. As a budgeting technique, Vjetrov (2016) in his review asserts that scenario analysis adopts the computation of different rates of reinvestment for the expected returns that are as well reinvested within the investment horizon. With a basis of the statistical and mathematical principles, a scenario analysis provides the process of estimation of shifts in the portfolio values based on different situation occurrences also called scenarios following the ‘what if’ analysis principle.

Vjetrov (2016) also denotes that a scenario analysis can be used in examining the possible amount of risks that can be experienced within an investment about potential events variations that ranges from highly improbable or highly probable values. The same study points out that there are various approaches that can be adopted in scenario analysis. For instance, the determination of the standard deviation of the monthly or daily security returns while computing the expected value of the portfolio in every return generated from the security that can be a more standard deviation than the average return. In such a case, it gives a reasonable certainty level for an analyst regarding the possible portfolio change in value within a certain period by the stimulation of the identified extremes.

The capital asset pricing model (CAPM) is a process of measuring the comparison between a systematic risk of the expected return and security. The security market often adopts the results calculated from the CAMP formula in determining if a business investment in a portfolio or security is reasonable. In his study, the CAMP formula is a rate of return that is free from a risk with the addition to the beta of the multiplied portfolio or security by the expected return in the market without the risk-free return rate. When adopted, it gives the projected return of the security as pointed out by Djulic and Pindzo (2015, p. 67).

Capital asset pricing model

Re = Rf +β (E (RM) – Rf)

Where Return on Equity, Rf = risk- free rate, E{RM} Expected rate of return on the market, β = beta coefficient, E{RM}– Rf is the difference that exists between the return rate in the expected market and the risk-free rate.

Beta is the measure of systematic risk of the stock. Beta describes the sensitivity of stocks returns to the changes in the market. An asset with a beta of zero means its return is independent of changes in the market return. The capital market line (CML) on the other hand is a graphical representation of the CAPM formula that plots the expected return of stocks on the y- axis and the beta on the x-axis. The intercept is the risk-free rate, and the slope represents the market premium. Individual securities expected return and risk are plotted on the CML graph. If it is plotted above the CML, it is undervalued as the investors expect a greater investment return for the same amount of risk (beta). In case it is plotted below the CML, it will be overvalued.

Therefore the Capital Market Line (CML) adopts the use of CAPM formula in calculating the expected return of a portfolio or security. It is a represents the CAMP formula in a graphical manner. In a free and competitive financial market as described in the CAPM, security cannot be sold for long at low values that can yield more than that of the appropriate SML return. In such a case, the security would be very attractive in comparison to other securities of similar risk. The investor would thus bid his price until the expected return can fall to an appropriate SML position. Conversely, the investors can as well sell off the available stocks at a price that is high enough to place the expected return below the appropriate position. In such a case, Lam and Oshodi (2015) denote that the resulting price reduction would continue till the projected return of the stock rising to the justified level by the systematic risk.

The company is required to earn the cost on equity-financed portion of the business investments to avoid the falling of its stock price. In case the organization doesn’t expect earning the equity cost, it should refund the shareholders, to earn return on different securities at similar risk levels within the business marketplace. With the equity costs involving a market expectation, measuring the available few techniques is difficult. The empirical SML appears less sloped as compared to the theoretical SML. As Exhibit VI illustrates, low beta securities helps in earning a return that is higher than CAPM predicts while high-beta stocks earn less than expected. A variety of deficiencies in CAPM and in the statistical methodologies employed have been advanced to explain this phenomenon. As a risk measure, beta appears to relate the past returns as a result of the close relationship between total and systematic risks. It is hence difficult to empirically distinguish their effects. Nonetheless, the inclusion of factors representing unsystematic risk appears to add little explanatory power to the risks/ returns relationship.

The measurement of the risk factors is one of the major differences between SML and CML. While standard deviation measures risks for CML, the major risk factor determinant for the SML is the Beta coefficient. The CML measures the risk through two main methods, i.e., the standard deviation and the use of a total risk factor strategy. CML is used in showing the return rates depending on the rates of return and risk levels for a special portfolio (Walker 2011, p. 138).

The non-coefficient and coefficient portfolios are measured by the security market line graphs while the SML graphs measure the risk through beta comparison that helps in finding the security’s risk contribution for the portfolio. Also called the Characteristic Line, SML shows a graphical representation of the return and risk market at a given period. The Capital Market Line graphs as well define the efficient portfolios as the Security Market Line graphs define both the efficient and the non-efficient portfolio. In the calculation of the returns, the expected portfolio return of the portfolio for the CML is shown in the Y- axis while for SML, the Y-axis shows the securities in return. The portfolio’s standard deviation is indicated along the X-axis for CML, whereas, the Beta of security is indicated along the X-axis for SML.

Djulic, K, & Pindzo, R 2016, ‘Analysis of the Capital Budgeting Practices: Serbian Case’, Management (1820-0222), 79, pp. 47-52, Business Source Premier, EBSCOhost, viewed 21 January 2017.

Lam, K, & Oshodi, O 2015, ‘The capital budgeting approaches ‘, Construction Management & Economics, 33, 7, pp. 587-600, Business Source Premier, EBSCOhost, viewed 21 January 2017.

Vjetrov, A 2016, ‘Analysis of the Capital Budgeting Practices: Serbian Case’, Management (1820-0222), 79, pp. 47-52, Business Source Premier, EBSCOhost, viewed 21 January 2017.

Walker, M 2011, ‘The Continuing Increase in the Use of Sophisticated Capital Budgeting Techniques’, California Management Review, 27, 1, pp. 137-148, Business Source Premier, EBSCOhost, viewed 21 January 2017.