Fin650:Project Appraisal Lecture 6

1. Fin650:Project Appraisal Lecture 6 Sensitivity and Breakeven Analysis, and Simulation Concepts and Methods

2. Risk and Project Appraisal • People are generally risk-averse • Simply looking at the expected value of the net present value may not be enough Consider a choice between two prizes, you can have Tk. 100,000 for certain or a lottery ticket which will pay Tk. 200,000 with a probability of 0.5 and Tk. 0 with a probability of 0.5, which one will you choose? Please note that the two choices have the same expected value • Certainty equivalent An amount that would be accepted in lieu of a chance to receive a possibly higher, but uncertain, amount.

3. Risk and Project Appraisal (Contd.) • A graphical illustration • Risk aversion: decreasing marginal utility of wealth concave function relating wealth and utility • Risk neutral: constant marginal utility of wealth • Risk lover: increasing marginal utility of wealth • Utility function Consider two projects, A and B which have the following Payoffs

4. Risk and Project Appraisal (Contd.)

5. Risk and CBA (Contd.) • According to expected value criteria project A is preferred E[UA] = 0.6*(100,0001/2)=189.7 E[UB] = 0.6*(50,0001/2)+0.4*(50,0001/2) =223.6 We can calculate the certainty equivalent of the two projects For project A, For Project B, U(CE) = 189.7 U(CE) = 223.6 CE1/2= 189.7 CE1/2= 223.6 CE = 36,000 CE = 50,000 Because project B has the greater certainty equivalent, it is the preferable project If people are risk neutral, then expected net present value will give the right answer

6. Introduction: Sensitivity and Breakeven Analysis • Analyzing project risks by making mechanical trial and error changes to forecast values of selected variables. • Analyzing the risks of investment projects, by changing the values of forecasted variables. • Finding the values of particular variables which give the project a Breakeven NPV of zero.

7. Process of Analysis • Identification of those variables which will have significant impacts on the NPV, if their future values vary around the forecast values. • The variables having significant impacts on the NPV are known as ‘sensitive variables’. • The variables are ranked in the order of their monetary impact on the NPV. • The most sensitive variables are further investigated by management.

8. Management Use of Sensitivity and Breakeven Analysis Using Sensitivity: Sensitive variables are investigated and managed in two ways: • (1) Ex ante; in the planning phase; more effort is used to create better forecasts of future values. If management decides the project is too risky, it is abandoned at this stage. • (2) Ex post; in the project execution phase; management monitors the forecasted values. If the project is performing poorly, it is abandoned or sold off prior to its planned termination.

9. Management Use of Sensitivity and Breakeven Analysis Using Breakeven: • Forecasted calculated Breakeven values of variables are continuously compared against actual outcomes during the execution phase.

10. Terminology Within the Analysis • Sensitivity and Breakeven analyses are also known as: ‘scenario analysis’, and ‘what-if analysis’. • Point values of forecasts are known as: ‘optimistic’, ‘most likely’, and ‘pessimistic’. • Respective calculated NPVs are known as: ‘best case’, ‘base case’ and ‘worst case’. • Variables giving a ‘breakeven’ value, return an NPV of zero for the project.

11. Steps in Sensitivity Analysis • Calculate the project’s NPV using the most likely value estimated for each variable. • Select from the set of uncertain variables those which the management feels may have an important bearing on predicted project performance. • Forecast pessimistic, most likely, and optimistic values for each of these variables over the life of the project.

12. Steps in Sensitivity Analysis • Recalculate the project’s NPV for each of these three levels of each variable. While each particular variable is stepped through each of its three values, all other variables are held at their most likely values. • Calculate the change in NPV for the pessimistic to optimistic range of each variable. • Identify sensitive variables. Important: Selection of appropriate variables, and establishing valid upper and lower forecast values.

13. Selection Criteria For Variables in the Analysis • Degree of management control. • Management's confidence in the forecasts. • Amount of management experience in assessing projects. • Extrinsic variables more problematic than intrinsic variables. • Time and cost of analysis.

14. Real Life Examples Forecast Errors • Large blowouts in initial construction costs for Sydney Opera House, Montreal Olympic Stadium. • Big budget films are shunned by critics and public alike; e.g ‘Waterworld’: whilst cheap films become classics; eg.‘Easy Rider’. • High failure rate of rockets used to launch commercial satellites.

15. Developing Optimistic and Pessimistic Forecasts • Use forecasting –error information from the forecasting methods: e.g. - upper and lower bounds; prediction interval; expert opinion; physical constraints, are applied to the variables. This method is formalized, but arguable, slow and expensive.

16. Developing Optimistic and Pessimistic Forecasts • Usead hoc percentage changes: a fixed percentage, such as 20%,or 30%, is added to and subtracted from the most likely forecast value. This method is vague and informal, but fast, popular, and cheap. +20% ? -20%

17. Sensitivity Analysis • Sensitivity analysis • Partial sensitivity analysis • Worst and best-case analysis • Monte-carlo analysis • According to bridge supporters, the salvage value after 30 years will be $50 million. According to ferry supporters, the bridge will have zero salvage value after 30 years

18. Sensitivity Analysis

19. Sensitivity Analysis • Carry out sensitivity analysis changing • salvage value,e.g. plugging the bridge supporters salvage value in ferry supporters profile • the discount rate • the construction costs • annual benefit

20. Sensitivity analysis example: Delta Project

21. Delta Project: Sensitivity Analysis Results • Workbook8.1.xls

22. Delta Project: Sensitivity Analysis Results

23. Break-even Analysis: Product Unit Price

24. Break-even Analysis: Required Rate of Return

25. Break-even Analysis: Unit Production Cost

26. Outputs and Uses • Each forecast value is entered into the model, and one solution is given. • Solutions can be summarized automatically, or individually by hand. • Variables are ranked in order of the monetary range of calculated NPVs. • Management investigates the sensitive variables. • More forecasting is done, or the project is accepted or rejected as is.

27. Strengths and Weaknesses of Analysis • Strengths: • Easy to understand. • Forces planning discipline. • Helps to highlight risky variables. • Relatively cheap. • Weaknesses • Relatively unsophisticated. • May not capture all information. • Limited to one variable at a time. • Ignores interdependencies.

28. Simulation: an Introduction • Project risk analysis by simultaneous adjustment of forecast values. • Simulation allows the repeated solution of an evaluation model. • Each solution randomly selects values from predetermined probability distributions. • All solutions are summarized into an overall distribution of NPV values. • This distribution shows management how risky the project is.

29. Simulation Terminology • The treatment of risk by using simulation is known as ‘stochastic’ modeling. • Other names for our term ‘Simulation’, are - ‘Risk Analysis’, ‘Venture Analysis’,’Risk Simulation’, ‘Monte Carlo Simulation’. • The name ‘Monte Carlo Simulation’ helps visualization of repeated spins of the roulette wheel, creating the selected values. • Each execution of the model is known as a ‘replication’ or ‘iteration’.

30. The Role of Simulation • Follows the initial creation and basic testing of the representative model. • Is sometimes used as a test of the model. • Emphasizes the need for formal forecasting, and requires close specification of the forecast variables. • Draws managements attention to the inherent risk in any project. • Focuses attention on accurate model building.

31. Probability Distributions of Forecast Variables • Uniform: upper and lower bounds required. • Triangular: pessimistic, most likely, and optimistic values required • Normal: mean and variance required. • Exponential: initial value and growth factor required.

32. Process of Computation per Replication • A value of a variable is selected from its distribution using a random number generator. • For example: Sales 90 units; selling price per unit $2,350; component cost per unit $1,100; labor cost per unit $280. • These values are incorporated into the model, and an NPV is calculated for this replication. • The NPV for this replication is stored, and later reported as one of many in an overall NPV distribution.

33. Making the Replications • Each replication is unique. • Selection of values from the distribution is made according to the particular distributions • The automated process is driven by a random number generator. • Excel add-ons such as ‘@Risk’ and ‘Insight’ can be used to streamline the process. • About 500 replications should give a good picture of the project’s risk.

34. Using the Output • Management can view the risk of the project. • Probability of generating an NPV between two given values can be calculated. • Probability of loss is the area to the left of a zero NPV.

35. Benefits and Costs of Simulation • Benefits • Focuses on a detailed definition and analysis of risk. • Sophisticated analysis clearly portrays the risk of a project • Gives the probability of a loss making project • Allows simultaneous analysis of variables • Costs • Requires a significant forecasting effort. • Can be difficult to set up for computation. • Output can be difficult to interpret.

36. Economic Analysis -Results • Base case • NPV 10.26mn kina, EIRR 16.14% • Scenario analysis, with 10 percent import tariff and zero percent export tax • NPV 13.85mn kina, EIRR 17.2% (with private sector jobs, new business, and tourism benefits) • NPV 1.4mn kina, EIRR 12.6% (without private sector jobs, new business, and tourism benefits)

37. Sensitivity Analysis Capital costs Trade transaction cost rate

38. Sensitivity Analysis

39. Monte Carlo Simulation • Monte Carlo simulation conducted using four variables, which are: • Reduction in trade transaction costs • Capital costs • Import tariff • Benefits from social development program

40. Parameters of the Variables * The betaPERT distribution is derived from the beta distribution and is commonly used in project risk analysis. It is also sometimes used as a "smoother" alternative to the triangular distribution. It is a continuous probability distribution. The parameters for the betaPERT distribution are minimum, likeliest, and maximum. When the sample size is high, the difference between the normal distribution and BetaPERT distribution is the kurtosis.

41. Results

42. Interpretation of the results NPV varies from –K29.78mn to K83.69mn. Probability of NPV being positive is 72.02% and negative is 17.98%. Mean NPV is K12.57mn and median NPV is K10.58mn. Both are higher than the base case. It shows that, the base was rather conservative. The probability of a ‘conservative’ base case delivering positive NPV is very high, meaning that the project is fundamentally sound. The project is highly influenced by the changes in reduction in trade transaction cost.

43. Activity Schedule: FIN650