Scenario Analysis and Break-even Analysis in Investment Decisions

The assignment content discusses various techniques for investment appraisal and decision-making, including sensitivity analysis, break-even analysis, and scenario simulation. Sensitivity analysis is used to estimate the impact of changes in variables on an investment's NPV, while break-even analysis determines the point at which sales equal costs. Scenario simulation estimates a large number of probable outcomes based on conditional probability distributions and constraints. The techniques are useful for evaluating investment opportunities and making informed decisions.

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FIN200 ASSIGNMENT
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FIN200 Assignment
Introduction
Investment decision-making is very crucial for an investor willing to invest in feasible
investment. This entails conducting an investment analysis in order to determine whether a
given investment is worthwhile the financing through capitalization structure. Further,
identifying or making investment decisions is imperative and calls for better analysis
techniques that would assist in attaining organization’s management strategic business
objective (Rebiasz 2007). There are numerous investment opportunities in which an
organization could invest in, but due to limited time and resources, organizations
management needs to select an investment opportunity that would match their economic and
business objectives. In addition, investment cash flows are usually at risk and might not at
times be equal to estimates utilized in computing the NPV or forecasts made by human
beings could either be too pessimistic or optimistic while making cash flow forecasts (De
Kok, Van Donselaar & van Woensel 2008). Hence, there is need to conduct risk analysis to
guard against such type of biases while making investment decisions. Further, it is at times
reasonable in assuming that actual cash flows is equal expected cash flows utilised in
estimating investment’s NPV since there are numerous possible cash flow results for any
investments. Thus, a better risk or feasibility analysis is a crucial party in investment
decision-making since it provide actionable info regarding particular investment
opportunities as well as knowledge that the management could exploit towards noteworthy
decision-making facets. The key feasibility aspects that requited to be evaluated or analysed
is the economic feasibility which is assessed through various capital budgeting techniques or
through different financial techniques like break-even analysis, simulation analysis, scenario
analysis as well as sensitivity analysis. With these considerations, this paper would present

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description of the four different techniques of risk analysis that could be used by the
management in making their investment decisions. This would also be accompanied by some
of the relevant impacts these techniques could have on the capital budgeting techniques such
as NPV and IRR.
Sensitivity analysis
Sensitivity analysis is a common component of investment assessment utilized in determining
how dissimilar values of a given variable affect definite dependent variable. It computes
consequences of erroneously estimating the variables in an investment NPV analysis. In
addition, sensitivity analysis forces the management to identify variables underlying their
analysis and to focus on how variations to such variables could affect expected NPV (Rebiasz
2007). It is the approach that measures impact of change in specific variables; for instance,
variation in revenues as well as relative effect on the NPV. Therefore, in performing
sensitivity analysis all variables has to be fixed up to projected values and the variable that
would remain unaltered would be adjusted by particular percentages and resulting impact of
that on the NPV would be noted.
Basically, sensitivity analysis is forms part of initial risk analysis ain an investment appraisal.
It comprises of assessment of impacts of disparities in costs, sales on a given project. For
instance, assuming that the sale manager wishes to understand effect of client traffic on sales,
she determines that the total sales are the function of transaction volume and price. Price of
the widget is around $1,000 while managers sold around 100 units for about $100,000. In
addition, managers determines that 10% rise in the client traffic increases the transaction
volume by around 5% that permits him to form the financial models and the sensitivity
analysis within such equality based on the what-if notion. With these, it means that with
transaction of 100 today, 10%, 50% or the 100% rises in client traffic could equates to the

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rise in the number of transactions by around 5, 25 or 50. In this case, sensitivity analysis
shows that sales are greatly sensitive to variation in the client traffic.
To be more specific, sensitivity analysis is used in analysing impact certain variables could
have on given result. In essence, sensitivity analysis gives better understanding of
uncertainties of the future cash flow as well as it consequently present better understanding of
the reliability of NPV and IRR estimates (De Kok, Van Donselaar & van Woensel 2008).
Given all the type of uncertainties and risks associated with different capital investment
project, it is more likely that organization’s management or decision-makers best make
estimate of decision-criteria that would be more accurate. This calls for them to understand
how sensitive their decision criteria would be in variations in some of the chief variables. In
such case, sensitivity analysis gives some crucial insight or information into numerous
aspects that could influence eventual results of an investment opportunity (Rebiasz 2007).
Furthermore, it is employed in ascertaining resemblance of different model and in
ascertaining different elements that could greatly contribute to disparity in given output and
in ascertaining domain of the input components where disparity is highest. This technique is
utilised in ascertaining mutual happening between diverse components or in establishing
ideal and unbalanced domains.
Further, sensitivity analysis demonstrates the model output disparities in line with the model
input fluctuations as in Figure 1. Here, the model referred to as the sensitive model modifies
the model output.
Figure 1: sensitivity analysis of the output variables

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Source: Rebiasz (2007)
In essence, the technique is viewed as that means of analysing disparity in an investment’s
IRR or NPV for specific disparities in a particular variable. This shows how sensitive the
NPV of a given project would be to discrepancy in variables (Rebiasz 2007). It shows the
impacts of disparities in assumptions. The technique utilizes a wide range of scenario
probabilities in modelling in assessing substitute business decisions.
While conducting sensitivity analysis, following procedures has to be followed; first, the
management has to determine some of the variables which are likely to diverge from
estimated values. Secondly, the management has to select some of the probable increment
and range of changes for every variable. Further, one has to compute as well as plot values of
decision criteria resulting from varying above variables in their possible ranges. Through
assessment of the decision criteria that is said to emerge from variations in variables,
decision-makers would be in a position to identify variables to which decision criteria is more

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sensitive (Rebiasz 2007). With such understanding, decision-makers or the management has a
general know how of the type of variables they would concentrate on in a given investment
project and in the management of such investment once they commence. For instance, in an
electric scooter project, a sensitivity analysis examining how sensitive NPV would be to
variations in initial investment, that is, variable sales volume and manufacturing cost per unit,
following can be observed (De Kok, Van Donselaar & van Woensel 2008).
Figure 2: sensitivity analysis of the electric scooter project
Source: De Kok, Van Donselaar & van Woensel (2008).
In this graph horizontal axis shows the percentage change in particular variables and vertical
axis represents the project’s NPV. It can be seen from this graph that a 20 per cent decrease
in initial investment would leads to a 60 per cent increase in the project’s NPV from its
original amount (De Kok, Van Donselaar & van Woensel 2008). In addition, it can be viewed
that a 20 per cent increase in the sales volume would leads to 120 per cent increase in NPV
from its original amount. This graph shows that NPV is more sensitive to variations in

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variable cost per unit. With such hint, organization’s management could now evaluate what
likelihood of such and this could result in reconsideration on whether to invest in a given
project or no.
In spite of it being more reliable in investment decision, it is very cumbersome in case one is
analysing large number of the variables. In addition, whereas it provide good signal of the
effect of particular variables, it fails to provide clear signs of the investment’s overall risk in a
manner that one could explicitly utilise it in the decision-making (Swedberg 2008).
Scenario analysis
This is a basic tool that is implemented in evaluating uncertainty and risk about future
forecasts of a given investment opportunity. The technique comprises of changing numerous
variables at once in the NPV forecast (Shapiro 2009). The approach helps in assessing
influence of numerous alternatives on the project’s development. In addition, scenario
analysis is a technique of evaluating possible future events by taking in consideration of
alternative probable scenarios or consequences. It enables quality decision-making through
adoption of a more inclusive condition or scenario of the outcomes. For example, in finance,
companies try to calculate frequent probable results in respect to economy. The approach
also tries to predict or estimate the financial market yields for the stocks, in varied scenarios
(Bock & Trück 2011).
This is an extension of sensitivity analysis and provides numerous probable results through
an assessment of a wide range of probable situations like the best case, the most likely case as
well as worst case (Terry 2010). To be more specific, scenario analysis is that analysis
technique that assesses numerous probable scenarios which include worst case, most likely
case as well as best case scenario. Therefore, in scenario analysis, only three probable
outcomes are considered. In essence, scenario analysis could be used in determining or

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displaying some reasonable scenarios for decision-making procedure like optimistic, the
expected as well as the pessimistic (Brzakovic, Brzakovic & Petrovic, 2016).. Further, this
technique comprises of modelling of possible elective conducts of social or political
atmosphere as well as latent diplomatic risks. Under scenario analysis, numerous NPV values
for an investment project based on diverse scenarios are computed.
In scenario analysis, the NPV and IRR of the company under different cash flow are said to
behave differently. For instance, under best case scenario, the NPV looks at high revenues
and low costs while at worst case scenario it looks at high costs and low revenues. Further,
scenario analysis considers sub-sets of each and every probability (De Kok, Van Donselaar &
van Woensel 2008).
Basically, scenario analysis usually permits organization management to assign various
probabilities to base case, the worst case as well as to best case and to establish expected
values as well as standard deviation of investment’s NPV in order to getter better concept of
the investment’s risk. In essence, scenario analysis usually extends the risk analysis of a
given investment in two means; first it permits the management to change over one variable
at a given time and therefore see combined impacts of the variations in numerous variables
on the NPV and it permits the management in bringing in probabilities of variations in chief
variables (De Kok, Van Donselaar & van Woensel 2008). Under scenario analysis the
management are able to modify numerous inputs to worse or better than expected and they
can also select as many scenarios as possible. Furthermore, scenario analysis would assist in
assigning probabilities to best case, worst case and base case scenarios where after this
expected value of an investment’s NPV is determined.
Break-even analysis

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Break-even analysis is viewed to some extent as just the extent of the sensitivity analysis
since it help in establishing some of the parameter value at which level a given investment
project become unattractive (Kew & Watson 2010). For instance, in case decision gauge is
NPV, the parameter’s break-even point would be that the value where NPV become nil and
elsewhere which the investment project becomes undesirable. It is a popular as well as
commonly used technique for analysing the relationship that exists in between profitability
and sales volume. It is usually founded on time series of the cash outflows and inflows. In
essence, break-even analysis is the time needed for discounted cash flows to generate back
the investment. Furthermore, it is used not just in the investment selection, but in managerial
accounting like cost-volume-profit analysis (Bierman & Smidt 2012) . In essence, break-even
analysis is usually the relations in between profits and cost volume at numerous stages of the
production, with some emphasis on break-even point (Tough Nickel 2016). Here, the break-
even point is that point at which an organisation receives neither loss nor profit, where the
total money from sales is equivalent to total expenditures as shown in Figure 3 below.
Figure 3: demonstration of break-even analysis

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Source: Bierman & Smidt (2012)
The technique is useful in looking at the relationship that exists in between income, profit and
costs. Further, break-even analysis is that technique utilised in determining point at which
sales equals costs of production (Bock & Trück 2011). It takes closer look at relevant profits
as well as at the total fixed costs of the project.
Simulation technique
It is an expanded scenario and sensitivity analysis. The technique can be used to estimates a
large number of probable outcomes on the basis of conditional probability distributions as
well as constraints for every variable (Kew & Watson 2010). Here, output is the probability
circulation for the NPV with approximation of the probabilities of getting optimistic NPV.
Furthermore, it comprises of estimation of dissemination of various outcomes. In addition,
simulation analysis is viewed as the statistically based behavioural tactic that relates the
prearranged probability circulations and the random numbers in valuing hazardous outcomes
(Bock & Trück 2011). This is the kind of the scenario analysis which employs relatively
prevailing financial planning software such as the interactive financial planning system.

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Basically, simulation analysis is employed in enabling the management to formulate
probability examination for decision criterion of merit through the assistance of random
unification of the variable (Bierman & Smidt 2012).
Conclusion
In conclusion, it is evident that in evaluating a certain investment opportunity or in making an
investment decision, sensitivity, simulation, break-even and scenario analysis play an integral
role. Furthermore, it can be concluded that sensitivity analysis stands as the most common
component of investment assessment utilized in determining how dissimilar values of a given
variable affect definite dependent variable since it computes consequences of erroneously
estimating the variables in an investment NPV analysis. In addition, it can be recommended
that in conducting an investment appraisal or in making investment decision, sensitivity
analysis stands as the best technique since it provides better understanding of uncertainties of
the future cash flow as well as it consequently present better understanding of the reliability
of NPV and IRR estimates. On the other hand, it can be concluded that break-even, scenario
and simulation analysis also helps in making investment decisions since they helps assessing
influence of numerous alternatives on the project’s development and assisting in evaluating
possible future events by taking in consideration of alternative probable scenarios or
consequences.

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REFERENCES
Bierman Jr, H & Smidt, S 2012, The capital budgeting decision: economic analysis of
investment projects. Routledge.
Bock, K & Trück, S 2011, ‘Assessing uncertainty and risk in public sector investment
projects,’ Technology and Investment, 2(02), 105.
Brzakovic, T, Brzakovic, A & Petrovic, J 2016, ‘Application of scenario analysis in the
investment projects evaluation,’ Ekonomika Poljoprivrede, 63(2), 501.
Bujoreanu, IN 2011, ‘What if (sensitivity analysis),’ Journal of Defense Resources
Management, 2(1), 45.
De Kok, AG, Van Donselaar, KH & van Woensel, T 2008, ‘A break-even analysis of RFID
technology for inventory sensitive to shrinkage,’ International Journal of Production
Economics, 112(2), 521-531.
Kew, J & Watson, A 2010, Financial Accounting: An Introduction 3e. OUP Catalogue.
Rebiasz, B 2007, ‘Fuzziness and randomness in investment project risk appraisal,’ Computers
& Operations Research, 34(1), 199-210.
Shapiro, AC 2009, Capital budgeting and investment analysis. Prentice Hall.
Swedberg, J 2008, “The Decision Point.” Credit Union Management. May 2008.

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Terry, E 2010, ‘The impact of scenario presentation on capital budgeting decisions,’
In Proceedings of Annual American Business Research Conference. Las Vegas: World
Business Institute.
Tough Nickel 2016, What Is Break-Even Analysis?: Viewed at 12th September 2017 from;
https://toughnickel.com/business/Breakeven-analysis.