Question:
Discuss About The Financing Through Capitalization Structure?
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 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 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 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
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 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 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).
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 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 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
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.
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. 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.
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.
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
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