Discuss about the Sensitivity Analysis for Decision Making Process.
Sensitivity analysis is widely used in the management decision-making process. The tool is normally used for capital budgeting process of the business. The tool provides an opportunity to evaluate the relationship between all the components of any project. The purpose of the sensitivity analysis is to measure the responsiveness towards the Net Present Value (NPV) of the projected return of the investments. The analysis of sensitivity provides the option to check the NPV of the investment in any project and forecast the change in every particular by changing any key item of the project. The risk analysis is done by using the sensitivity analysis of the investment. The change in the input variable changes the cash flow as well as the bottom line of the return of the project. It can be measured using the sensitivity analysis. The sensitivity measurement provides the degree of change or the degree of detraction of the bottom line (Ashokkumar, 2014). Sensitivity analysis may indicate the importance of the variable that might be used as the key indicator in the business.
The main advantage of sensitivity analysis for the managers is its simplicity to analyse the data. Sensitivity analysis does not need any special theory to understand for the managers. The management’s effort and concentration towards any investment related activity can be measured using the sensitivity analysis. The sensitiveness of particular item can be identified in this process, and the manager may target those items particularly to attain the objectives of the firm. Source of planning is provided with the sensitivity analysis as it provides the opportunity of professional judgment to be applied by the managers in decision-making. The sensitivity analysis allows the managers to use the automated system to analyse the different situation of the business due to change in any variable (Aslan, 2015). The process of quality checking is also possible by applying the sensitivity analysis. The quality checking of the item with a concentration on such one variable is possible as sensitivity provides the resource to verify the quality of the process by identifying the variable.
The main disadvantage of the sensitivity analysis is a dependency on the single variable. The change in one variable does not change other similar types of variables. The example of independency of the variable is changed in material price does not change the price of other products. However, the managers can change the price of the products due to change in the price of the materials. This is the main weakness of sensitivity analysis, and it can be overcome by applying the simulation in managerial accounting where more than one variable can be changed or tested by the managers. This type of analysis considers the extent of change in the value of the variable instead of the probability of change in that variable. So, sensitivity does not measure the relative change in the price or the variable. The last disadvantage of this analysis is not to provide any particular solution to the managers (Blau and Fuller, 2008). The sensitivity analysis does not provide any solution instead; it provides some indications and the further key to analyse of the situation.
Scenario analysis is the process to predict the possible future of some scenes in the business. It is based on the probability measurement and provides the opportunity to obtain an idea of possible outcomes of any action in operation to the managers (Campbell et al. 2012). Scenario analysis does not show any particular picture of the future instead; it shows different possibilities in future. The scenario analysis helps in measuring the expected return of any investment. It is applicable in investment strategy too. The standard deviation of the certain share can be measured, and the measurement of two or three standard deviations might provide a different return for the portfolio. The investor might obtain the different possibilities of return of the portfolio due to the different standard deviation of the stock or the risk associated with it. The analysis is also used in finance too. The tool is useful for appraising the project investment situation for the managers. The scenario analysis is helpful in the decision-making of the capital investment of any project. The scenario analysis helps the manager to determine the risk level they can go for and the level of risk they cannot afford to complete the project. Additionally, it might provide the opportunity to analyse the financial outcome of the project in different situations using the probable factor in the measurement (Creedy and Gemmell, 2012). The scenario analysis helps in maximising the profit and minimising the risk associated with the project. It provides the opportunity to the manager to assess three scenarios namely best, worst and normal case scenarios. The value or the expected values of these three scenarios are determined by some probable values from the experiences. The process of scenario analysis involves the scenario development by the management. There are six phases of activities in developing the scenario in the business by the manager. The first phase states the problem identity of the financial context. The description of the current situation is the next phase, and this phase also provides the identification of relevant factors of the situation. The next phase is to determine the classification, valuation and scenario elements of the situation. The next phase is important as it provides the opportunity to build up the scenario as in practice. The fifth phase, analyse and interpret the scenario with suitable reasoning. The last phase provides the opportunity to make a strategic decision to the management concerning the different scenario.
The main difference between the sensitivity and scenario analysis is both provide different types of prediction of future to the managers. The former one presents the application and necessity of any variable in any capital management decision whereas the later one provides the information on different scenarios of investment. The scenario analysis evaluates the risk assessment containing in the market as well as the risk associated with the project itself (Moyer, 2012). However, the risk assessment in sensitivity analysis is associated with only one variable. The scenario can be of many types – functional and a cluster of scenarios. But, sensitivity analysis has no such classification in practice. Sensitivity analysis provides the key indicator in the business. However, scenario analysis does not bind its measurement to any variable instead; it provides holistic measures in different situations.
Comparison between two models: Capital Asset Pricing Model and Capital Market Line
Capital asset pricing model provides the idea on the relationship between expected return and systematic risk of the investments in the market. There are two types of risks present in any investment – systematic and unsystematic risk. The basic theory of CAPM assumes that portfolio of investments is diversified by market portfolio nullifying the unsystematic risks of the investments. However, the systematic risks cannot be eliminated in this way of diversification. The CAPM model measures the systematic risk of the investment compared to expected return on the investment (Davies et al. 2014). The equation of CAPM model is as follows:
Er = Rf + β * (Rm – Rf) (Goldstein and Hackbarth, 2014)
Er = expected return on investment
Rf = Risk-free rate
β = volatility of the security
Rm = expected return of the overall market
(Rm – Rf) = premium of equity market
The government’s ten-year bond yield is normally taken as the risk-free rate in this model whereas premium of the investment is the expectation of the investors of more return than that of the risk-free rate. The beta provides the relative volatility of the stock price and the index of the market. The value of beta can vary from 0 to any positive value (negative beta is rare in the stock market as it is possible for the gold beta). The theory of beta states that low beta or beta having a value of less than 1 can provide less movement in its share price compared to the market index. So, the return of these stocks is lesser than that of the stock index. The beta of 1 provides the equal return to the market whereas the having value more than 1 can move more than the market index. The investors must invest in the stocks of having a beta of more than 1 when the market is rising while the inverse is true when the market is going downwards. This model measures the risk of the investment by measuring the relative volatility in the stock with the index (Huang et al. 2015). The systematic risk of the investment is priced in this model (Mäntysaari and Mantysaari, 2010). The model provides the security market line after plotting in the graph. The line provides the information on the excess return of the investment compared to risk. The measurement of the SML provides the expected return by multiplying price and quantity risk of the investment.
The capital market line is extracted from the mean-variance portfolio theory. The mean-variance portfolio states that all the investors are rational and like to invest with the minimum variance in their investment. The CML can be drawn by drawing a tangent from the risk-free rate to market portfolio. The capital market line is the relation between expected return to volatility in the stock portfolio. The tangent indicates the capital market line where an investor might want to hold the risky asset on prorate basis with the market weight of that specific stock (Karpavi?ius, 2014). The tangency point with the curve is known as investments in the risky assets (Lin et al. 2012). The risk and return greater than the tangency point show that the investors have invested in high-risk assets. The following figure shows the capital market line of the investment. The point L shows the investment of fewer risk securities in the portfolio. The point M suggests that all the securities possess the risk of the market whereas point R says the high risk is leveraged to the invested assets.
The capital market line can be determined by empirical formula too. The expected return in capital market line is as follows:
E(R) = Rf + delta ( E(Rm) – Rf) / delta (market) (Kimyagarov and Shivdasani, 2013)
Delta is the standard deviation of the security and the market respectively. The Sharpe ratio is measured using the capital market line. The value of Sharpe ratio provides the value of excess return compared to volatility per unit of the securities. The ratio has the same value on a specific current market line.
The CAPM provides the information on the excess return of the investment in respect to price risk and quantity risk. The model only can provide the information on the systematic risk as the assumption of the model has stated to nullify all the unsystematic risks of the investment. The systematic risk beta is measured in the X axis of the security market line. The capital market line is the efficient frontier in the Markowitz portfolio theory where the risk-free asset is added to the minimum variance portfolio. The main comparison between CAPM and CML is as follows:
CML is the line on the efficient frontier of the portfolio whereas the CAPM and SML – both are possible in any portfolio. The portfolio only may provide the systematic risk, and expected return on the investment, but not the capital market line as the portfolio is inefficient. The reason is that not all the securities lie on the capital market line or the efficient frontier. The majority of the securities can lie behind the capital market line (Lendel et al. 2015). In capital market line, the risk-free lending and external lending – both are possible to make the portfolio efficient. The portfolio measured using the capital asset price also can be efficient by investing in the efficient securities. For the efficient portfolio, the CML and SML are the same lines whereas the inefficient portfolio has SML but not other. The capital market line accounts the entire risk of the investment whereas the capital asset pricing model measures the only systematic risk of the investment.
References
Ashokkumar, S. (2014) ‘Thoughts on business ethics and corporate social responsibility from Vedic literature’, Procedia Economics and Finance, 11, pp. 15–22.
Aslan, H. (2015) ‘Do lending relationships affect corporate financial policies?’, Financial Management, 45(1), pp. 141–173.
Blau, B.M. and Fuller, K.P. (2008) ‘Flexibility and dividends’, Journal of Corporate Finance, 14(2), pp. 133–152.
Campbell, T., Fisher, J.G. and Stuart, N.V. (2012) ‘Integrating sustainability with corporate strategy: A maturity model for the finance function’, Journal of Corporate Accounting & Finance, 23(5), pp. 61–68
Creedy, J. and Gemmell, N. (2012) ‘Measuring revenue responses to tax rate changes in multi-rate income tax systems: Behavioural and structural factors’, International Tax and Public Finance, 20(6), pp. 974–991.
Davies, T., Crawford, I., MR, T.D. and Etc, I.C. (2014) Corporate finance and financial strategy: Optimising corporate and shareholder value. Boston, MA, United States: Pearson.
Goldstein, I. and Hackbarth, D. (2014) ‘Corporate finance theory: Introduction to special issue’, Journal of Corporate Finance, 29, pp. 535–541.
Huang, K.-F., Dyerson, R., Wu, L.-Y.andHarindranath, G. (2015) ‘From temporary competitive advantage to sustainable competitive advantage’, British Journal of Management, 26(4), pp. 617–636
Joseph, J. (2011) Internal and external sources of finance. Available at: https://www.askwillonline.com/2011/04/internal-and-external-sources-of.html (Accessed: 19 January 2017).
Karpavi?ius, S. (2014) ‘Dividends: Relevance, rigidity, and signaling’, Journal of Corporate Finance, 25(3), pp. 289–312
Kieschnick, R. and Rotenberg, W. (2015) ‘Working capital management, the credit crisis, and hedging strategies: Canadian evidence’, Journal of International Financial Management & Accounting, 27(2), pp. 208–232.
Kimyagarov, G. and Shivdasani, A. (2013) ‘Managing pension risks: A corporate finance perspective’, Journal of Applied Corporate Finance, 25(4), pp. 41–49.
Lendel, V., Soviar, J. and Vodák, J. (2015) ‘Creation of corporate cooperation strategy’, Procedia Economics and Finance, 23(2), pp. 434–438.
Liang, G., Lütkebohmert, E. and Wei, W. (2015) ‘Funding liquidity, debt tenor structure, and creditor’s belief: An exogenous dynamic debt run model’, Mathematics and Financial Economics, 9(4), pp. 271–302
Lin, C., Lin, P. and Zou, H. (2012) ‘Does property rights protection affect corporate risk management strategy? Intra- and cross-country evidence’, Journal of Corporate Finance, 18(2), pp. 311–330.
Mäntysaari, P. and Mantysaari, P. (2010) The law of corporate finance: General principles and Eu law. Heidelberg: Springer Publishing Company.
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