Sensitivity Analysis And Scenario Analysis: Understanding Investment Decisions

Discussion

Discuss about the Sensitivity Analysis and Scenario Analysis.

Sensitivity analysis and Scenario analysis are quite similar when an overview of the both the topics are taken. Thus, there is a very thin line between sensitivity and scenario analysis even when it comes to deep study and analysis of any capital expenditure.

The basic meaning of sensitivity analysis can be correctly judged from the word sensitivity (the very name of the analysis tool), which simply means the impact of change or rate of change in one or more independent variable due to change in favorable or unfavorable direction of other variable, dependent on the former Variable Sensitivity analysis, thus means the rate of change in outcome of a project of an entity due to change in various other independent factors, say change in life of a project, interest rate or any change in other factors affecting the final result at the decision making point. (Dow J,2015), The change in result or outcome is due to the positive or negative change in the related input variable affecting the decision making of the whole process. Scenario Analysis, on the other hand is the change in whole scenario of the decision making process in future which might affect the result or outcome. The scenario analysis tool is used to depict the extreme risk in future due to possible or slightly probable event so that decision can be taken by stakeholders, whether to get involved into such investment or not. Standard deviation, which is the risk related to return can be analyzed and accordingly decided, whether it is under the range of investor or not. This is very useful tool for risk-averse investors, who want to be aware of risk level before-hand. 

Capital Budgeting refers to a decision making process, where a decision is taken, whether a particular capital expenditure will result in positive results or negative results in future. Internal rate of return refers to the break even discount rate of a project (where the inflow and outflow becomes same), thus making thee net present value zero and net present value if the difference between the present value of inflow and outflow, which if positive gives green signal to go ahead with the project and vice versa. Sensitivity analysis can be used for such capital budgeting decisions with reference to internal rate of return or discount rate to calculate the present value of the cash flows in future out of the earnings of the project which is being judged with the help of sensitivity analysis tool. (Nielsen K,2010), This analysis is done by keeping one term variable and all others fixed to see the impact of change in variable term on the net present value. Say, change in the discount rate on upper side will take the the present value of cash flows downwards and thus have a negative impact on the results of the project and a change on the lower side will give a positive affect by increasing the  net present value of the project. Similarly, net present value of inflow and outflow, will change the decision related to carrying out the capital expenditure or not. The capital budgeting decision in respect of any expenditure depends upon future events and thus this analysis tool works as a ‘what if’ analysis. Scenario analysis details the investor or analyst about the risk in a portfolio whether a particular change in scenario, say unfavorable change in rate of interest will still benefit the investor or would yield the value of investment. (Scenario analysis, 2016.) It means the outcome on happening or non-happening of a particular event in future, which will hugely affect the results and thus increases the risk of the investor. Tools like standard deviation can be used for various scenarios to check if the risk taken is worth the returns provided by the project. It can be said that sensitivity analysis is a little smaller step than scenario analysis as scenario analysis is some level ahead than former. Scenario analysis uses probability by assigning weights to various factors such as internal rate of return, life of project, discount rate, etc. and then judging each scenario such as best case or worst case scenario, giving results about the project.

Conclusion

Conclusion

From the above overview of the topic, it can be said that analysts can use these two as weapons for giving better guidance to the investors whether to invest their valuable money and time in projects they are planning to invest into. It is very necessary to be well-informed before putting hands into any investment, thus understanding of sensitivity and scenario analysis aids the person better. To make a decision of whether to make an investment in a particular area can be taken by making the detailed analysis with the above tools. When an entity uses sensitivity or scenario analysis for a future project, it not only wants to find out the cash flow but the value of the project and whether the same will be beneficial for the entity or not. Above all, the values or variables are not fixed and are only assumptions based on experience and expertise of professionals which is accumulated after detailed study of topic and handling various investment cases or seeing the market fluctuations, thus they may vary in future which may cause significant changes in final outcome leading to unexpected results. So, there arises a need for such analysis tools to at least have an idea of the rate of change that may arise due to high level of uncertainty and fluctuation of various factors in the economic factors. These two models can do wonders by tweaking the numbers here and there a little bit and getting the outcome in form of well-informed decision. This will prevent the business from risks or say will act as an insulator for the investment. Being a bigger version, scenario analysis guides much better to decide whether a project or say making a capital expenditure will lead to positive revenue with the risk and return analysis in terms of cash flows.

Capital Asset Pricing Model and capital market line are the analysis tools both of which depict the risk and return. CAPM, on one hand, reflects the risk and return graph of a stock by considering the systematic risk i.e the market risk and the risk free rate which is derived from the current rate of similar government securities’ rate of return.

The two components of capital asset pricing model present the whole concept, which includes systematic risk represented by difference of market return and risk free return, the result of which is further multiplied with beta, measuring the total market risk of the stock. Beta (denoted by B), is a measure which represents the risk of the stock as compared to market risk i.e. the change in stock return if the market changes by 1; say, if the beta of a stock is 2 and there is a subsequent fall in market by 6% then the stock will fall by 12% (i.e. twice the market which is 6%). Thus capital asset pricing model is depicted by the formula: [R_f + (R_m – R_f)B]. CAPM, as reflected by the above described method and the same is also depicted in the form of a graph, which is known as Security market line or SML. (Holton G,2017), CAPM model runs on a belief by assuming that the present as well as potential investors are willing to take lower risk against higher risk and would prefer high returns instead of low returns. However, it is not the same always. There may be some gamblers, say who may be ready to take higher risk for lower returns comparatively as they are risk lovers and not risk averse. (Scenarioanalysis, n.d.)

Explanation of Two CAPM and CML

Capital market line is a tangent line drawn on a graph model to show the expected rate of returns plotted against the risk level (standard deviation) shown on x and y axis of the graph for a particular portfolio. The tangent line (CML) adjoins the efficient frontier intersection point with the point where the risk free rate of return and expected rate of return of the invested stock are equal.

Capital asset pricing model and capital market line have some similarities as well as differences between them which are depicted below. Some of the common features are as follows: CAPM and CML, both of them are tools containing risk and return as the guiding variables, comparing the risk with respect to return and thus judging if the stock should be held, sold or bought, depending upon the risk and return variables. If the risk of the stock or portfolio is higher than the return then the same should be sold or not bought. However, the same should be kept or bought if the return is higher than risk. Further, both the above methods measure the market return of the investment. Both of them are depicted in form of similar graphs. The differences on the other hand are as follows: The differences can be classified into various types, dividing them into frontiers, risk calculation, risk for portfolio or stock. Where CAPM model uses both systematic and non-systematic risk, whereas, CML model uses only systematic risk, not covering the other part. Where Securities market line uses beta as a measure of systematic risk, capital market line uses standard deviation which is also a way to measurement of risk. SML depicts the risk and return of a particular stock or asset only; however, CML reflects the risk and return of portfolio as a whole. Where capital market line shows efficient frontier only, securities market line shows both efficient and non-efficient frontiers. The graph showing CML depicts return of a portfolio of assets on Y-axis, whereas, Y-axis of SML graph reflects the return of the particular stock. Further, the X-axis of CML is marked as the standard deviation of the portfolio, whereas the X-axis of the SML graph depicts beta of stock, i.e. risk of stock as compared to market. (Myers, S.C. 512 )Capital market line is considered to be at higher step than capital asset pricing model. CAPM refers to the security points and CML, on the other hand, risk free assets and market bunch of investments.

Conclusion

Thus, it can be concluded from above discussion that capital asset pricing model and capital market line mark the return against an investment’s risk depending upon various factors, which may be market factors or domestic factors in CAPM. Both of these methods have their different usage with equal importance to determine whether, it is beneficial to invest in particular securities or portfolio respectively in CAPM and CML. Further, it is very clear that the CML line is the most efficient frontier and thus any return above this line is not possible to target, thus investors won’t opt for frontiers below this tangent as they can earn higher at efficient frontier and thus opt for that. CAPM is based on diversification and not depending too much on a particular stock creating a stock more dominating. Further, risk and return are closely related to each other, which is clearly evident from past theories and practical experiences. The greater the risk, greater will be the chances of higher return. Thus, very evidently, the higher the graph is on right end side of efficient frontier, the higher the risk and simultaneously higher returns for the risk loving investors.

References: 

Dow J,(2015),Topics in Capital Budgeting, Available at https://www.csun.edu/~jpd45767/303/8%20-%20Topics%20in%20Capital%20Budgeting.pdf ( Accessed on 22 January 2017)

Holton G,(2017),Capital Market Line. Available at https://www.glynholton.com/notes/capital_market_line/ ( Accessed on 1January 2017)

Nielsen K,(2010),Corporate Finance : Part II-Budgeting Financing Valuation,1^st Edition ,  Pg No 12 , Booknoon.com.

Scenarioanalysis,(2016) , Advantages of Performing a Scenario Analysis, Available at https://www.scenarioanalysis.net/Scenario-Analysis-Advantages.html [Accessed on 22/01/2017] 

Sharpe, William F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk, Journal of Finance, 19 (3), 425-442. 

Myers, S.C. (1512) ‘INTERACTIONS OF CORPORATE FINANCING AND INVESTMENT DECISIONS-IMPLICATIONS FOR CAPITAL BUDGETING’, The Journal of Finance, 29(1), pp. 1–25. doi: 10.1111/j.1540-6261.1974.tb00021.x.

Chan, K., Saltelli, A. and Tarantola, S. (2000) Statistics and Computing, 10(3), pp. 187–196. doi: 10.1023/a:1008950625967