Financial Accounting: Risk Assessment in Capital Budgeting Decisions

This financial accounting assignment delves into various capital budgeting techniques used to assess project risk and make informed investment decisions. The paper explores sensitivity analysis, which examines how changes in input variables impact Net Present Value (NPV) and Internal Rate of Return (IRR). It then discusses scenario analysis, which evaluates project outcomes under different sets of variable combinations, including best-case and worst-case scenarios. The assignment also covers break-even analysis, determining the production or sales volume needed to avoid losses, and simulation analysis, using Monte Carlo methods to model the probability distributions of project outcomes, providing a comprehensive view of potential results. The paper emphasizes the importance of these techniques in managing uncertainty and maximizing shareholder wealth.

Running head: FINANCIAL ACCOUNTING
Financial Accounting
Name of the Student
Name of the University
Authors Note
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Table of Contents
Introduction:...............................................................................................................................2
Relation of Capital Budgeting in Sensitivity Analysis..............................................................2
Relation of Capital Budgeting in Scenario Analysis:................................................................3
Relation of Capital Budgeting in Break Even Analysis:............................................................5
Relation of Capital Budgeting in Simulation Analysis:.............................................................6
Conclusion:................................................................................................................................7
Reference list:.............................................................................................................................9

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Introduction:
Every project has certain kind of uncertainty and the managers of the corporate firms
are required to determining how risk affects the process of decision-making (Burns and
Walker 2015). Large companies regularly uses the difficult method of incorporating risk into
the capital budgeting. However, every manager is required to understand the certain
techniques for assessing the uncertainty. The primary obligations of the corporate managers
are to maximise the wealth of the owners but at the same time insuring them from the
unwanted risks.
Relation of Capital Budgeting in Sensitivity Analysis
One of the techniques of determining risk is the sensitivity analysis that will help in
indicating the change in NPV concerning the given change of an input variable whereas on
the other hand other things remaining constant. For the purpose of sensitivity analysis,
application managers need to determine the base value of input variables (Hise and Strawser
2013). The base value symbolizes the most probable values and that the corporate manager is
anticipating occurring.
When the base values are determined then it is necessary to test the net present value
of cash flow sensitivity based on the variable variation concerning few percentage or the few
units that holds the other variable constant. It is noteworthy to denote that the values must be
changed under the correlation such as cost and the number of products sold. Arguably,
sensitivity analysis helps in demonstrating the impacts of changes in the assumptions
(Mukherjee and Rahahleh 2013). It is worth mentioning that sensitivity analysis helps in
undertaking numerous capital budgeting decision. Sensitivity analysis helps the managers in
ascertaining how the distribution of possible NPV or the internal rate of return for a project

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that is under the considerations is impacted by the consequent changes in one particular input
variables. Any time managers undertaking the decision of the capital budgeting they are
required to make assumptions concerning the project such as the number of units for sales,
time involved in completing the project and the amount of cost of capital involved.
The managers are required to understand the reliability of the assumptions and the
anticipated change in the result of the project if the managers make wrongs assumptions.
Sensitivity analysis can be regarded as the method of measuring the sensitivity of the results
that is involved in the assumptions of the project (Andor Mohanty and Toth 2015).
Sensitivity analysis alters one assumptions by leaving the other assumptions similar and
ascertains the process of changes relating to the NPV and IRR. At the time of assessing the
capital budgeting project managers are required to forecast the cash flows.
The methods involves in forecasting the cash flows is reliant on the sales forecast and
costs. The sales revenue reflects the functions of the sales volume along with the unit selling
price. Sales volume of the project is reliant on the market size and the organizations market
share (Sanchez et al. 2014). The NPV and the IRR of the project are derived by the managers
following the analysis of the after tax cash flows. This is arrived by combining the numerous
variables of the cash flows, life of the project and rate of discount. Therefore, the behaviour
of all the variables are most likely uncertain. The sensitivity analysis helps in locating the
how the sensitive of the numerous variables that are estimated for the project. Therefore,
sensitivity analysis reflects how sensitive is the NPV and the IRR of the project relating to the
given change in the particular variables.
Relation of Capital Budgeting in Scenario Analysis:
As evident from the scenario analysis, typically one variable is varied at a period.
Given that the variables are interrelated, which they are most likely to be it is necessary to

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gauge into some of the plausible scenarios with each of the scenarios representing a
consistent combinations of the variables (Baucells and Borgonovo 2013). Scenario analysis is
regarded as the behavioural approach that is identical to the sensitivity analysis but carries a
wider scope. It assess the impact of an organization return of instantaneous change in the
number of variables such as cash inflow, cash outflow and cost of capital. For instance, the
organization might assess the impact of the both the high and low scenario on the NPV of the
project (Blobel and Fröhlich 2017). Each of the scenario helps in reflecting the organization
cash inflows, cash outflow and the cost of capital, which ultimately results in different stages
of NPV.
The managers can make use of this NPV estimation with the objective of evaluating
the risk that is involved in the project with regard to the degree of inflation. At the time of
making decision, managers are most often not sure where there are more than one
assumptions. There are certain assumptions that might change and the degree of such change
is reliant on the specifics of the problems. Although it is widely evident sensitivity, analysis
is the most largely used method of capital budgeting however; it does have certain limitations
(Borgonovo and Plischke 2016). However scenario analysis on the other hand provides the
answer to these uncertainties, it helps in transporting the probabilities of changes in the most
vital variables and allows the managers to alter more than one variable at a time.
Scenario analysis begins with the base case or the most probable set of values for the
input variables. It gradually moves towards the worst-case scenario and the best-case
scenario. It is noteworthy to denote that there are some enthusiastic managers that often are
carried away with the most probable results and forget the outcome that may take place if the
certain critical assumptions such as the state of economy or the competitor’s reactions that are
unrealistic (Gao et al. 2016). Therefore, it can be said that scenario analysis helps in

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establishing both the worst-case scenarios and the best-case scenarios in order to ascertain the
entire range of probable outcomes are considered.
The analysis critically involves four critical components. At the initial stages, it
involves determining the factors around which the scenarios are built. These factors generally
comprises of the conditions of the economy along with the response of the competitors on
any activities of the organization (Bodie 2013). The second component is based on the
determination of the number of scenarios to be analysed for each of the factor. Usually three
factors are considered namely, the best-case scenarios, average case scenarios and the worst-
case scenarios. The third component is based on placing focus on the critical factors and
creating a comparatively few scenarios for each of the factors (McNeil, Frey and Embrechts
2015). The final component is based on assigning the probabilities to each of the factors. The
assignment are usually based on the macro-economic factors such as the exchange rate,
interest rate and micro economic factors comprises of the competitors reactions etc. It can be
concluded that at the time of computing the NPV of the each scenario, a scenario analysis is
performed.
Relation of Capital Budgeting in Break Even Analysis:
In conducting a sensitivity, analysis managers often face the question of what might
happen to the project if the sales of the project declines or there is an increase in cost.
Financial managers will be keen on knowing the quantity to be produced and minimum
amount to be sold in order to make sure that the project does not lose money ( Grant 2016).
Such kind of exercise is known as the break-even analysis and the minimum amount of
quantity to produce to avoid the loss is known as the break-even point. In capital budgeting
process a project in accounting term that is breakeven is identical to a stock that provides an
individual with a return of zero percent.

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Under both the situations, an individual or a firm gets back their original investment.
However, an individual is not compensated for the time value of the money invested or the
amount of risk that is undertaken. Considering it in a different manner, managers forego the
opportunity cost of their capital (Brooks 2015). Therefore, it can be said that a project that
simply breaks even in accounting terms will be having negative NPV. Breakeven analysis can
be regarded as one of the better starting points but it overlooks some of the vital information.
It generally does not provides information relating to the probability of getting a specific
result or how good or bad an outcome can be. It might be worth it for the managers to risk
losing money if there is an opportunity of securing a big reward. It can be said that the Break-
even analysis emphasis on the NPV and not on the accounting profit.
Relation of Capital Budgeting in Simulation Analysis:
Sensitivity and scenario analysis can be regarded as the most useful model of
understanding the uncertainty of the investment projects. However, it is noteworthy to denote
that both the models does not take into the considerations the interactions between the
variables and does not illustrate the probability of the changes in the variables. The power of
computer can assist in incorporating the risk in the capital budgeting is through the help of a
method that is known as the Monte Carlo Simulation analysis (Zio 2013).
The expression Monte Carlo refers to the approach that comprises of the numbers that
is drawn randomly from the probability of distributions. It is generally known as the
statistically based approach that makes the use of the random numbers and probabilities that
are pre-assigned in order to simulate the outcome of the project and its return. It needs
sophisticated package of computing to function efficiently. Simulation analysis is different
from the sensitivity analysis in a manner that rather than estimating the value for a vital
variable, distribution of possible values for each of the variables are used.

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The simulation model creation process commences from the computer that computes
the value randomly simultaneously for each of the variable recognized with similar to model
market, growth rate, price of sales, variable cost, life of the project etc. With the help of this
set of random values, a fresh sequence of cash flow is produced and a new NPV is computed
(Tavare 2013). The same procedure is repeated on numerous occasion perhaps as much as
one thousand times or even on a large amount for a very large project, by enabling the
managers to take decision on the distribution of the probability concerning the project’s NPV.
From the model of distribution it can be depicted that a mean NPV will be computed
and the related standard deviation will be put into use to measure the risk level of the project.
The distribution of probable results provides the decision maker with the opportunity of
viewing the continuum of probable results instead of a single estimation. It is noteworthy to
denote that the Monte Carlo Simulation draws together the sensitivities and probabilities
distributions (Rubinstein and Kroese 2016). The most essential appeal of this model is that it
provides the managers and the decision makers with the probability of the distributions of
NPV instead of the single point estimations of the anticipated NPV. Simulation analysis
strength lies in variability as it effectively handles the problems of several exogenous
variables following any sort of distribution. It compels the decision makers to explicitly take
into the considerations the inter-dependencies and uncertainties that features around the
capital budgeting projects.
Conclusion:
The study highlighted the techniques involved in capital budgeting based on the
assumptions of certainty and uncertainty have been stated. It is worth mentioning that the
investment decision that is made by the managers will be based on the determination of the

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number of the vital issues such as cash flow produced by the organization, dividends paid
out, market value of the organization etc.
As a matter of evidence, the techniques of capital budgeting allows the managers with
more informed findings with the carefulness that their application might turn out to be
problematic in the changing conditions of technological and economic circumstances. In such
a situations, some form of computer based simulation technique might turn out to be of great
practical use.

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Reference list:
Andor, G., Mohanty, S.K. and Toth, T., 2015. Capital budgeting practices: A survey of
Central and Eastern European firms. Emerging Markets Review, 23, pp.148-172.
Baucells, M. and Borgonovo, E., 2013. Invariant probabilistic sensitivity
analysis. Management Science, 59(11), pp.2536-2549.
Blobel, C. and Fröhlich, E., 2017. Scenario Analysis for Strategic Purchasing: Development
of a Scenario Simulation Tool for the Villeroy & Boch AG. In Supply Management
Research (pp. 275-294). Springer Fachmedien Wiesbaden.
Bodie, Z., 2013. Investments. McGraw-Hill.
Borgonovo, E. and Plischke, E., 2016. Sensitivity analysis: a review of recent
advances. European Journal of Operational Research, 248(3), pp.869-887.
Brooks, R., 2015. Financial management: core concepts. Pearson.
Burns, R. and Walker, J., 2015. Capital budgeting surveys: the future is now.
Gao, L., Bryan, B.A., Nolan, M., Connor, J.D., Song, X. and Zhao, G., 2016. Robust global
sensitivity analysis under deep uncertainty via scenario analysis. Environmental Modelling &
Software, 76, pp.154-166.
Grant, R.M., 2016. Contemporary Strategy Analysis Text Only. John Wiley & Sons.
Hise, R.T. and Strawser, R.H., 2013. Application of Capital Budgeting Techniques to
Marketing Operations. Readings in Managerial Economics: Pergamon International Library
of Science, Technology, Engineering and Social Studies, p.419.

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McNeil, A.J., Frey, R. and Embrechts, P., 2015. Quantitative risk management: Concepts,
techniques and tools. Princeton university press.
Mukherjee, T.K. and Al Rahahleh, N.M., 2013. Capital budgeting techniques in practice: US
survey evidence. Capital Budgeting Valuation: Financial Analysis for Today's Investment
Projects, pp.151-171.
Rubinstein, R.Y. and Kroese, D.P., 2016. Simulation and the Monte Carlo method (Vol. 10).
John Wiley & Sons.
Sanchez, D.G., Lacarrière, B., Musy, M. and Bourges, B., 2014. Application of sensitivity
analysis in building energy simulations: Combining first-and second-order elementary effects
methods. Energy and Buildings, 68, pp.741-750.
Tavare, N.S., 2013. Industrial crystallization: process simulation analysis and design.
Springer Science & Business Media.
Zio, E., 2013. The Monte Carlo simulation method for system reliability and risk analysis (p.
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