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- 1. Chapter 11 Stand- Alone Risk Analysis
- 2. Outline Sources, measures, and perspectives on risk Sensitivity analysis Scenario analysis Break-even analysis Hillier model Simulation analysis Decision tree analysis Managing risk Project selection under risk Risk analysis in practice How financial institutions analyse risk
- 3. Techniques for Risk Analysis Techniques for Risk Analysis Analysis of Stand- Alone Risk Analysis of Contextual Risk Sensitivity Analysis Break-even Analysis Simulation Analysis Scenario Analysis Corporate Risk Analysis Market Risk Analysis Hillier Model Decision tree Analysis
- 4. <ul><li>Sources and Perspective of Risk </li></ul><ul><li>Sources of Risk </li></ul><ul><ul><ul><li>Project-specific risk </li></ul></ul></ul><ul><ul><ul><li>Competitive risk </li></ul></ul></ul><ul><ul><ul><li>Industry-specific risk </li></ul></ul></ul><ul><ul><ul><li>Market risk </li></ul></ul></ul><ul><ul><ul><li>International risk </li></ul></ul></ul><ul><li>Perspectives on Risk </li></ul><ul><ul><ul><li>Standalone risk </li></ul></ul></ul><ul><ul><ul><li>Firm risk </li></ul></ul></ul><ul><ul><ul><li>Market risk </li></ul></ul></ul>
- 5. Measures of Risk Risk refers to variability. It is a complex and multi-faceted phenomenon. A variety of measures have been used to capture different facets of risk. The more important ones are: <ul><li>Range </li></ul><ul><li>Standard deviation </li></ul><ul><li>Coefficient of variation </li></ul><ul><li>Semi - variance </li></ul>
- 6. Sensitivity Analysis (‘000) YEAR 0 YEARS 1 - 10 1. INVESTMENT (20,000) 2. SALES 18,000 3. VARIABLE COSTS (66 2/3 % OF SALES) 12,000 4. FIXED COSTS 1,000 5. DEPRECIATION 2,000 6. PRE-TAX PROFIT 3,000 7. TAXES 1,000 8. PROFIT AFTER TAXES 2,000 9. CASH FLOW FROM OPERATION 4,000 10. NET CASH FLOW (20,000) 4,000 NPV = -20,000,000 + 4,000,000 (5.650) = 2,600,000 RS. IN MILLION RANGE NPV KEY VARIABLE PESSIMISTIC EXPECTED OPTIMISTIC PESSIMISTIC EXPECTED OPTIMISTIC INVESTMENT (RS. IN MILLION) 24 20 18 -0.65 2.60 4.22 SALES (RS. IN MILLION) 15 18 21 -1.17 2.60 6.40 VARIABLE COSTS AS A 70 66.66 65 0.34 2.60 3.73 PERCENT OF SALES FIXED COSTS 1.3 1.0 0.8 1.47 2.60 3.33
- 7. Scenario Analysis <ul><li>Zen Enterprises is evaluating a Project. Depending on the response of the market-the factor which is the largest source of uncertainty for the success of the project-the management of the firm has identified three scenarios: </li></ul><ul><li>-The product will have the moderate appeal to a large segment of customers at a modest price. </li></ul><ul><li>-The product will have strong appeal to a large segment of market which is high price-sensitive. </li></ul><ul><li>-The product will appeal to a large segment of the market which will be willing to pay a high price. </li></ul><ul><li>Calculate the NPV, under each scenario. </li></ul>
- 8. Scenario Analysis Procedure 1. Select the factor around which scenarios will be built. 2. Estimate values of each of the variables for each Scenario 3. Calculate NPV / IRR under each scenario NET PRESENT VALUE FOR THREE SCENARIOS (RS. IN MILLION) SCENARIO 1 SCENARIO 2 SCENARIO 3 INITIAL INVESTMENT 200 200 200 UNIT SELLING PRICE (IN RUPEES) 25 15 40 DEMAND (IN UNITS) 20 40 10 REVENUES 500 600 400 VARIABLE COSTS 240 480 120 FIXED COSTS 50 50 50 DEPRECIATION 20 20 20 PRE-TAX PROFIT 190 50 210 TAX @ 50% 95 25 105 PROFIT AFTER TAX 95 25 105 ANNUAL CASH FLOW 115 45 125 PROJECT LIFE 10 YEARS 10 YEARS 10 YEARS SALVAGE VALUE 0 0 0 NET PRESENT VALUE (AT A DISCOUNT 377.2 25.9 427.4 RATE OF 15 PERCENT)
- 9. <ul><li> Break-Even Analysis </li></ul><ul><ul><li>Accounting Break –Even Analysis </li></ul></ul><ul><li>Fixed Costs + Depreciation 1 + 2 </li></ul><ul><li>= = Rs. 9 million </li></ul><ul><li>Contribution margin ratio 0.333 </li></ul><ul><li> Cash flow forecast for Naveen’s flour mill project </li></ul><ul><li> (‘000) </li></ul><ul><li>Year 0 Year 1 - 10 </li></ul><ul><li>1. Investment (20,000) </li></ul><ul><li>2. Sales 18,000 </li></ul><ul><li>3. Variable costs (66 2 / 3 % of sales) 12,000 </li></ul><ul><li>4. Fixed costs 1,000 </li></ul><ul><li>5. Depreciation 2,000 </li></ul><ul><li>6. Pre-tax profit 3,000 </li></ul><ul><li>7. Taxes 1,000 </li></ul><ul><li>8. Profit after taxes 2,000 </li></ul><ul><li>9. Cash flow from operation 4,000 </li></ul><ul><li>10. Net cash flow (20,000) 4,000 </li></ul><ul><ul><li>Cash Break-Even Analysis </li></ul></ul>
- 10. <ul><li>Hillier Model </li></ul><ul><li>Uncorrelated Cash Flows </li></ul><ul><li> n C t </li></ul><ul><li>NPV = – I </li></ul><ul><li> t = 1 (1 + i ) t </li></ul><ul><li> n t 2 ½ </li></ul><ul><ul><ul><ul><ul><li> (NPV) = </li></ul></ul></ul></ul></ul><ul><li> t = 1 (1 + i ) 2 t </li></ul><ul><li>Perfectly Correlated Cash Flows </li></ul><ul><li>n C t </li></ul><ul><ul><ul><ul><ul><li> NPV = – I </li></ul></ul></ul></ul></ul><ul><li> t = 1 (1 + i ) t </li></ul><ul><li>n t </li></ul><ul><ul><ul><ul><ul><li> (NPV) = </li></ul></ul></ul></ul></ul><ul><li>t = 1 (1 + i ) t </li></ul>
- 11. Simulation Analysis Procedure 1. Choose variables whose expected values will be replaced with distributions 2. Specify the probability distributions of these variables 3. Draw values at random and calculate NPV 4. Repeat 3 many times and plot distribution 5. Evaluate the results
- 12. Obtaining Probability Distributions of Basic Variables <ul><li>Defining the probability distributions of basic variables is an important step in simulation. Two approaches may be used for obtaining probability distributions: </li></ul><ul><ul><li>Portrait approach </li></ul></ul><ul><ul><li>Building block approach </li></ul></ul>
- 13. Portrait Approach. The portrait approach is similar to the portrait method used for identifying suspects. According to this approach a standard probability distribution (normal, beta, chi-square, poisson, uniform, exponential, or any other) is drawn up, usually by a statistician, on the basis of the judgment expressed by the expert (informant). This is shown to the expert for his comments. The expert may suggest changes if the distribution does not conform with his judgment. For example, he may suggest that the probabilities at the tails should be greater or the probability of the modal value should be higher. The statistician modifies the earlier distribution to incorporate the changes suggested by the expert, till he is satisfied that the probability distribution represents his judgment well.
- 14. Building Block Approach. In the second approach, the ‘building block’ approach the probability distribution is defined by the expert. He attempts to quantify his judgment by a procedure which is as follows: (i) he chooses the range encompassing possible values; (ii) he divides the range into intervals which he thinks have different probabilities associated with them; (iii) he assigns probabilities to these intervals such that p i = 1; (iv) he may divide intervals into sub-intervals if he feels that the probabilities within an interval are different; and (v) he continues this process till he arrives at a distribution which represents his judgment well. This process often leads to a step rectangular distribution and has the following advantages (i) the expert has complete freedom in expressing his judgment; and (ii) it squares well with the principle of using all available information, no more no less.
- 15. (a) Uniform Distribution (b) Trapezoidal Distribution (d) Normal Distribution (c) Step Rectangular Distribution Some Probability Distributions
- 16. Problem of Correlation and the Level of Disaggregation <ul><li>In practice, correlations may exist among the distribution of </li></ul><ul><li>several factors. When such a dependency exists the factors which </li></ul><ul><li>are correlated should be considered together. For this purpose, </li></ul><ul><li>the joint probability distribution of correlated factors have to be </li></ul><ul><li>developed. This adds immensely to the problem of estimation. </li></ul><ul><li>In this context we must consider the choice relating to the level of </li></ul><ul><li>disaggregation. Now the problem is, to which level of detail </li></ul><ul><li>should we go? </li></ul><ul><li>The choice of the level of aggregation or disaggregation would be </li></ul><ul><li>finally based on the trade-off between the advantages of clarity of </li></ul><ul><li>judgment and the complexities of disaggregated analysis. Since </li></ul><ul><li>the influence of correlations is more significant than that of the </li></ul><ul><li>shape of any particular distribution, it may be preferable to limit </li></ul><ul><li>disaggregation. </li></ul>
- 17. <ul><li>Issues in Applying Simulation </li></ul><ul><ul><li>What should the output be ? </li></ul></ul><ul><ul><li>Is project variability enough? </li></ul></ul><ul><ul><li>How should the extreme values be used ? </li></ul></ul><ul><ul><li>How should the results of simulation be used? </li></ul></ul>
- 18. World Bank’s Experience <ul><li>It may be instructive here to review the experience of World Bank. The following are its summary remarks on simulation. </li></ul><ul><ul><li>Simulation is a powerful technique which permits use of </li></ul></ul><ul><ul><li>a great deal of information which would otherwise be lost. </li></ul></ul><ul><ul><li>It is a highly efficient medium of communication </li></ul></ul><ul><ul><li>It is not a technique which replaces skilled judgment. On </li></ul></ul><ul><ul><li>the contrary, it often requires the use of far more judgment </li></ul></ul><ul><ul><li>than the traditional analysis. </li></ul></ul><ul><ul><li>Despite the method’s value, the treatment of correlations </li></ul></ul><ul><ul><li>between variables remains a major problem. It is clear that </li></ul></ul><ul><ul><li>results can be completely misleading if correlations are not </li></ul></ul><ul><ul><li>handled properly. </li></ul></ul>
- 19. Evaluation An increasingly popular tool of risk analysis, simulation offers certain advantages: <ul><li>Its principal strength lies in its versatility. It can handle </li></ul><ul><li>problems characterised by (a) numerous exogenous variables </li></ul><ul><li>following any kind of distribution, and (b) complex </li></ul><ul><li>interrelationships among parameters, exogenous variables, </li></ul><ul><li>and endogenous variables. Such problems often defy the </li></ul><ul><li>capabilities of analytical methods. </li></ul><ul><li>It compels the decision maker to explicitly consider the </li></ul><ul><li>interdependencies and uncertainties characterising the </li></ul><ul><li>project. </li></ul>Simulation , however, is a controversial tool which suffers from several shortcomings. <ul><li>It is difficult to model the project and specify the probability </li></ul><ul><li>distributions of exogenous variables </li></ul>
- 20. <ul><li>Simulation is inherently imprecise. It provides a rough </li></ul><ul><li>approximation of the probability distribution of net present </li></ul><ul><li>value ( or any other criterion of merit). Due to its imprecision, </li></ul><ul><li>the simulated probability distribution may be misleading when a </li></ul><ul><li>tail of the distribution is critical. </li></ul><ul><li>A realistic simulation model, likely to be complex, would most </li></ul><ul><li>probably be constructed by a management scientist, not the </li></ul><ul><li>decision maker. The decision maker, lacking understanding of </li></ul><ul><li>the model, may not use it. </li></ul><ul><li>To determine the net present value in a simulation run the risk- </li></ul><ul><li>free discount rate is used. This is done to avoid prejudging risk </li></ul><ul><li>which is supposed to be reflected in the dispersion of the </li></ul><ul><li>distribution of net present value. Thus the measure of net </li></ul><ul><li>present value takes a meaning, very different from its usual one, </li></ul><ul><li>that is difficult to interpret. </li></ul>
- 21. Decision Tree Analysis <ul><li>Decision tree analysis is a tool for analysing situations where </li></ul><ul><li>sequential decision making in face of risk is involved. </li></ul><ul><li>The key steps in decision tree analysis are: </li></ul><ul><li>1. Identifying the problem and alternatives </li></ul><ul><li>2. Delineating the decision tree </li></ul><ul><li>3. Specifying probabilities and monetary </li></ul><ul><li>outcomes </li></ul><ul><li>4. Evaluating various decision alternatives </li></ul>
- 22. Decision Tree <ul><li>The decision tree, exhibiting the anatomy of the decision situation, </li></ul><ul><li>shows : </li></ul><ul><li> The decision points (also called decision forks) and the alternative </li></ul><ul><li>options available for experimentation and action at these decision </li></ul><ul><li>points. </li></ul><ul><li> The chance points (also called chance forks) where outcomes are </li></ul><ul><li>dependent on a chance process and the likely outcomes at these </li></ul><ul><li>points. </li></ul><ul><li>The decision tree reflects in a diagrammatic form the nature of the </li></ul><ul><li>decision situation in terms of alternative courses of action and chance </li></ul><ul><li>outcomes which have been identified in the first step of the analysis. </li></ul><ul><li>A decision tree can easily become very complex and cumbersome if </li></ul><ul><li>an attempt is made to consider the myriad possible future events and </li></ul><ul><li>decisions. Such a decision tree, however, is not likely to be a very useful </li></ul><ul><li>tool of analysis. Over-elaborate, it may obfuscate the critical issues. </li></ul><ul><li>Hence an effort should be made to keep the decision tree somewhat </li></ul><ul><li>simple so that the decision makers can focus their attention on major </li></ul><ul><li>future alternatives without being drowned in a mass of trivia </li></ul>
- 23. Specification of Probabilities and Monetary Value of Outcomes Once the decision tree is delineated, the following data have to be gathered : Probabilities associated with each of the possible outcomes at various chance forks, and Monetary value of each combination of decision alternative and chance outcome. The probabilities of various outcomes may sometimes be defined objectively. For example, the probability of a good monsoon may be based on objective, historical data. More often, however, the possible outcomes encountered in real life are such that objective probabilities for them cannot be obtained. How can you, for example, define objectively the probability that a new product like an electric moped will be successful in the market? In such cases, probabilities have to be necessarily defined subjectively.
- 24. Evaluation of Alternatives Once the decision tree is delineated and data about probabilities and monetary values gathered, decision alternatives may be evaluated as follows : 1. Start at the right-hand end of the tree and calculate the expected monetary value at various chance points that come first as we proceed leftward. 2. Given the expected monetary values of chance points in step 1, evaluate the alternatives at the final stage decision points in terms of their expected monetary values. 3. At each of the final stage decision points, select the alternative which has the highest expected monetary value and truncate the other alternatives. Each decision point is assigned a value equal to the expected monetary value of the alternative selected at that decision point. 4. Proceed backward (leftward) in the same manner, calculating the expected monetary value at chance points, selecting the decision alternative which has the highest expected monetary value at various decision points, truncating inferior decision alternatives, and assigning values to decision points, till the first decision point is reached.
- 25. Vigyanik case The scientists at Vigyanik have come up with an electric moped. The firm is ready for pilot production and test marketing. This will cost Rs.20 million and take six months. Management believes that there is a 70 percent chance that the pilot production and test marketing will be successful. In case of success, Vigyanik can build a plant costing Rs.150 million. The plant will generate an annual cash inflow of Rs.30 million for 20 years if the demand is high or an annual cash inflow of Rs.20 million if the demand is moderate. High demand has a probability of 0.6; Moderate demand has a probability of 0.4. To analyse such situations where sequential decision making is involved decision tree analysis is helpful.
- 26. D 1 c 1 D 2 c 2 D 3 D 11 : Carry out pilot production and market test -Rs 150 million D 12 :Do nothing C 12 : Failure Probability : 0.3 Probability : 0.7 C 11 : Success D 21 :Invest -Rs 20 million Probability : 0.6 C 21 : High demand Annual cash flow 30 million Annual cash flow 20 million C 22 : Moderate demand Probability : 0.4 D 22 : Stop D 31 : Stop Vigyanik Case
- 27. <ul><li>Vigyanik Case </li></ul><ul><li>The alternatives in the decision tree shown are evaluated as follows: </li></ul><ul><li>Start at the right-hand end of the tree and calculate the EMV at chance point C 2 that </li></ul><ul><li>comes first as we proceed leftward. </li></ul><ul><ul><ul><ul><ul><li>EMV(C 2 ) = 0.6 [30xPVIFA (20, 12%)] + 0.4 [20 x PVIFA (20, 12%)] </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>= Rs.194.2 million </li></ul></ul></ul></ul></ul><ul><li>Evaluate the EMV of the decision alternatives at D 2 the last stage decision point. </li></ul><ul><li>Alternative EMV </li></ul><ul><li>D 21 (Invest Rs.150 million) Rs.44.2 million </li></ul><ul><li>D 22 (Stop) 0 </li></ul><ul><li>Select D 21 and truncate D 22 as EMV(D 21 ) > EMV(D 22 ). </li></ul><ul><li>Calculate the EMV at chance point C 1 that comes next as we roll backwards. </li></ul><ul><li>EMV (C 1 ) = 0.7 [44.2] + 0.3 [0] = Rs.30.9 million </li></ul><ul><li>Evaluate the EMV of the decision alternatives at D 1 the first stage decision point : </li></ul><ul><li>Alternative EMV </li></ul><ul><li>D 11 (Carry out pilot production and </li></ul><ul><li> market test at a cost of Rs.20 million) Rs.10.9 million </li></ul><ul><li>D 12 (Do nothing) 0 </li></ul><ul><li>Based on the above evaluation, we find that the optimal decision strategy is as follows : Choose D 11 </li></ul><ul><li>(carry out pilot production and market test) at the decision point D 1 and wait for the outcome at </li></ul><ul><li>the chance point C 1 . If the outcome at C 1 is C 11 (success), invest Rs.150 million; if the outcome at </li></ul><ul><li>C 1 is C 12 (failure) stop. </li></ul>
- 28. Airways Limited Case Airways Limited has been set up to run an air taxi service in western India. The company is debating whether it should buy a turboprop aircraft or a piston engine aircraft. The turboprop aircraft costs 3500 and has a larger capacity. It will serve if the demand turns out to be high. The piston engine aircraft costs 1800 and has a smaller capacity. It will serve if the demand is low, but it will not suffice if the demand is high. The company believes that the chances of demand being high and low in year 1 are 0.6 and 0.4. If the demand is high in year 1, there is an 80 percent chance that it will be high in subsequent years (year 2 onward) and a 20 percent chance that it will be low in subsequent years. The technical director of Airways Limited thinks that if the company buys a piston engine aircraft now and the demand turns out to be high the company can buy a second-hand piston engine aircraft for 1400 at the end of year 1. This would double its capacity and enable it to cope reasonably well with high demand from year 2 onwards. The payoffs associated with high and low demand for various decision alternatives are shown in Exhibit 1.1.The payoffs shown for year 1 are the payoffs occurring at the end of year 1 and the payoffs shown for year 2 are the payoffs for year 2 and the subsequent years, evaluated as of year 2, using a discount rate of 12 percent which is the weighted average cost of capital for Airways Limited.
- 29. C 2 C 3 C 6 C 5 C 7 C 1 D 2 C 4 D 1 Exhibit 1.1 Decision Tree High demand (0.8) Low demand (0.2) High demand (0.4) Low demand (0.6) High demand (0.8) Low demand (0.2) High demand (0.8) Low demand (0.2) High demand (0.4) Low demand (0.6) 7000 1000 7000 600 6000 600 2500 800 2500 800 High demand (0.6) 1000 Low demand (0.4) 200 Expand - 1400 Do not expand Low demand (0.4) 300 High demand (0.6) 500 Piston engine - 1800 Turboprop - 4000 Year 1 Year 2
- 30. <ul><li>If Airways Ltd. buys the turboprop aircraft, there are no further decisions to be made. The NPV will be </li></ul><ul><li>= -4000 + 0.6(1000) + 0.4(200) + </li></ul><ul><li>1.12 </li></ul><ul><li>+ 0.6 {0.8(7000)+0.2(1000)}+0.4 {0.4(7000)+0.6(600)} </li></ul><ul><li>(1.12) (1.12) </li></ul><ul><li>=389 </li></ul><ul><li>If Airways Ltd. buys Piston Engine Aircraft and demand in year 1 turns out to be high, a further decision has to be made with respect to capacity expansion. To evaluate the Piston engine Aircraft, proceed as follows: </li></ul><ul><li>First calculate the NPV of the two options, viz. expand and do not expand at Decision Point D2 </li></ul>
- 31. <ul><li>Expand NPV= 0.8(6000)+0.2(600) - 1400 = 2923 </li></ul><ul><li>1.12 </li></ul><ul><li>Do Not Expand </li></ul><ul><li>NPV= 0.8(2500) +0.2(800) = 1929 </li></ul><ul><li>1.12 </li></ul><ul><li>Truncate Do not expand as it is inferior. This means NPV at D2 will be 2923 </li></ul><ul><li>Third calculate the NPV of the Piston Engine Aircraft at D1 </li></ul><ul><li>NPV= -1800 + 0.6(500+2923) + 0.4(300) </li></ul><ul><li>1.12 </li></ul><ul><li>+ 0.4 {0.4(2500)+0.6(800)} </li></ul><ul><li>(1.12) (1.12) </li></ul><ul><li>= 613 </li></ul>
- 32. <ul><li>Since the NPV of the Piston Engine Aircraft (613) is greater than the NPV of the turboprop aircraft(389), the former is better bet. So the recommended strategy for Airways Ltd. is to invest in the Piston Engine Aircraft at Decision Point D1, and if the Demand in year 1 turns out to be high, expand capacity by buying another Piston Engine Aircraft. </li></ul><ul><li>OPTION TO EXPAND Note if Airways Ltd. does not have the option of expanding at the end of year 1, the NPV of the Piston Engine Aircraft, would be = </li></ul><ul><li>-1800+ 0.6(500) + 0.4(300) + </li></ul><ul><li>1.12 </li></ul><ul><li>+ 0.6 {0.8(2500)+0.2(800)}+0.4{0.4(2500)+0.6(800)} </li></ul><ul><li>(1.12) (1.12) </li></ul><ul><li>= 80 Thus the 0ption to expand has value 613-80=533 </li></ul>
- 33. Given the decision tree with abandonment possibilities, let us calculate the NPV of the turboprop aircraft and the piston engine aircraft. 0.6 [1000 + {0.8(7000) + 0.2 (1000)}/(1.12)] + 0.4 (200 + 3600) NPV (Turboprop) = -4000 + (1.12) = 667 0.6 (500 + 2923) + 0.4 (300 + 1400) NPV (Piston engine) = -1800 + = 641 (1.12) Note that the possibility of abandonment increases the NPV of the Turboprop aircraft from 389 to 667. This means that the value of the option to abandon is: Value of abandonment option = NPV with abandonment - NPV without abandonment = 667 - 389 = 278 For the piston engine aircraft the possibility of abandonment increases the NPV from 613 to 641. Hence the value of the abandonment option is 28.
- 34. <ul><li>Corporate Risk Analysis </li></ul><ul><ul><li>A project’s corporate risk is its contribution to the overall </li></ul></ul><ul><li>risk of the firm </li></ul><ul><ul><li>On a stand-alone basis a project may be very risky but if its </li></ul></ul><ul><li>returns are not highly correlated – or, even better, negatively </li></ul><ul><li>correlated — with the returns on the other projects of the </li></ul><ul><li>firm, its corporate risk tends to be low </li></ul>
- 35. Managing Risk <ul><li>Fixed and variable cost </li></ul><ul><li>Pricing strategy </li></ul><ul><li>Sequential investment </li></ul><ul><li>Improving information </li></ul><ul><li>Financial leverage </li></ul><ul><li>Insurance </li></ul><ul><li>Long-term arrangements </li></ul><ul><li>Strategic alliance </li></ul><ul><li>Derivatives </li></ul>
- 36. <ul><li>Project Selection Under Risk </li></ul><ul><ul><ul><ul><li>Judgmental Evaluation </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Payback Period Requirement </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Risk Adjusted Discount Rate </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Certainty Equivalent Method </li></ul></ul></ul></ul>
- 37. <ul><li>Risk Analysis in Practice </li></ul><ul><ul><ul><li>Conservative Estimation of Revenues </li></ul></ul></ul><ul><ul><ul><li>Safety Margin in Cost Figures </li></ul></ul></ul><ul><ul><ul><li>Flexible Investment Yardsticks </li></ul></ul></ul><ul><ul><ul><li>Acceptable Overall Certainty Index </li></ul></ul></ul><ul><ul><ul><li>Judgment on three Point Estimates </li></ul></ul></ul>
- 38. Relative Importance of Various Methods of Assessing Project Risk A survey of corporate finance practices in India found the relative importance of various methods of assessing project risk to be as follows: <ul><li> % of companies rating it as </li></ul><ul><li> very important or important </li></ul><ul><ul><li>Sensitivity analysis 90.10 </li></ul></ul><ul><ul><li>Scenario analysis 61.60 </li></ul></ul><ul><ul><li>Risk-adjusted discount rate 31.70 </li></ul></ul><ul><ul><li>Decision tree analysis 12.20 </li></ul></ul><ul><ul><li>Monte Carlo simulation 8.20 </li></ul></ul><ul><li>Source : Manoj Anand “Corporate Finance Practices in India: A Survey”. Vikalpa, October –December 2000. </li></ul>
- 39. How Financial Institutions Analyse Risk To evaluate the risk dimensions of a project financial institutions calculate several indicators, the most important ones being the break even point, the debt service coverage ratio, and the fixed assets coverage ratio. In addition, they carry out sensitivity analysis. The break-even point for a project is calculated with reference to the year when the project is expected to reach its target(or expected) level of capacity utilisation, which is usually the third or the fourth operating year. Further, it is calculated in terms of capacity utilisation. So it is called break-even point capacity utilisation (BEPCU).
- 40. Summary <ul><li> Risk is inherent in almost every business decision. More so in capital </li></ul><ul><li>budgeting decisions as they involve costs and benefits extending over </li></ul><ul><li>a long period of time during which many things can change in </li></ul><ul><li>unanticipated ways. </li></ul><ul><li>Investment proposals, however, differ in risk. A research and </li></ul><ul><li>development project is typically more risky than an expansion </li></ul><ul><li>project and the latter tends to be more risky than replacement </li></ul><ul><li>project. </li></ul><ul><li> The variety of techniques developed to handle risk in capital </li></ul><ul><li>budgeting fall into two broad categories : (i) approaches that </li></ul><ul><li>consider the stand-alone risk of a project (sensitivity analysis, </li></ul><ul><li>scenario analysis, break-even analysis, Hillier model, simulation </li></ul><ul><li>analysis, and decision tree analysis). (ii) approaches that consider the </li></ul><ul><li>contextual risk of a project (corporate risk analysis and market risk </li></ul><ul><li>analysis). </li></ul><ul><li> Sensitivity analysis or “what if” analysis answers questions like : </li></ul><ul><li>“ What happens to NPV or IRR if sales decline by 5 per cent or 10 per </li></ul><ul><li>cent from their expected level ?” </li></ul>
- 41. In sensitivity analysis, typically one variable is varied at a time. If variables are inter-related, as they are most likely to be, it is helpful to look at implications of some plausible scenarios, each scenario representing a consistent combination of variables. Firms often do another kind of scenario analysis called the best case and worst case analysis. As a financial manager, you would be interested in knowing how much should be produced and sold at a minimum to ensure that the project does not ‘lose money’. Such an exercise is called break-even analysis and the minimum quantity at which loss is avoided is called the break-even point. The beak-even point may be defined in accounting terms or financial terms. Under certain circumstances, the expected NPV and the standard deviation of NPV may be obtained through analytical derivation; as proposed by H.S. Hillier. Sensitivity analysis indicates the sensitivity of the criterion of merit (NPV, IRR or any other) to variations in basic factors. Though useful, such information may not be adequate for decision making. The decision maker would also like to know the likelihood of such occurrences. This information can be generated by simulation analysis which may be used for developing the probability profile of a criterion of merit by randomly combining values of variables that have a bearing on the chosen criterion.
- 42. <ul><li> Decision tree analysis is a useful tool for analysing sequential decisions in </li></ul><ul><li>the face of risk. The key steps in decision tree analysis are : (i) identification </li></ul><ul><li>of the problem and alternatives. (ii) delineation of the decision tree. (iii) </li></ul><ul><li>specification of probabilities and monetary outcomes. (iv) evaluation of </li></ul><ul><li>various decision alternatives </li></ul><ul><li> A project’s corporate risk is its contribution to the overall risk of the firm. </li></ul><ul><li>Put differently, it reflects the impact of the project on the risk profile of the </li></ul><ul><li>firm’s total cash flows. On a stand-alone basis a project may be very risky </li></ul><ul><li>but if its returns are not highly correlated – or, even better, negatively </li></ul><ul><li>correlated – with the returns on the other projects of the firm, its corporate </li></ul><ul><li>risk tends to be low. </li></ul><ul><li>Aware of the benefits of portfolio diversification, many firms consciously </li></ul><ul><li>pursue a strategy of diversification. The logic of corporate diversification for </li></ul><ul><li>reducing risk, however, has been questioned. Why should a firm diversify </li></ul><ul><li>when shareholders can reduce risk through personal diversification? There </li></ul><ul><li>does not seem to be an easy answer. </li></ul>
- 43. Once information about expected return (measured as NPV or IRR or some other criterion of merit) and variability of return (measured in terms of range or standard deviation or some other risk index) has been gathered, the next question is, should the project be accepted or rejected. There are several ways of incorporating risk in the decision process : judgmental evaluation, payback period requirement, risk-adjusted discount rate method, and the certainty equivalent method. Often managers look at risk and return characteristics of a project and decide judgmentally whether the project should be accepted or rejected. Although judgmental decision making may appear highly subjective or haphazard, this is how most of us make important decisions in our personal life. In many situations companies use NPV or IRR as the principal selection criterion, but apply a payback period requirement to control for risk. If an investment is considered more risky, a shorter payback period is required. Under the risk profile method, the probability distribution of NPV, an absolute measure, is transformed into the probability distribution of profitability index, a relative measure. Then, the dispersion of the profitability index is compared with the maximum risk profile acceptable to management for the expected profitability index of the project.
- 44. The risk-adjusted discount rate method calls for adjusting the discount rate to reflect project risk. If the project risk is same as the risk of the existing investments of the firm, the discount rate used is the WACC of the firm; if the project risk is greater (lesser) than the existing investments of the firm, the discount rate used is higher (lower) than the WACC of the firm. Under the certainty equivalent method, the expected cash flows of the project are converted into their certainty equivalents by applying suitable certainty equivalent coefficients. Then, the risk-free rate is applied for discounting purposes. The methods of risk analysis commonly used in practice are : (i) conservative estimation of revenues, (ii) safety margin in cost figures, (iii) flexible investment yardsticks, (iv) acceptable overall certainty index, and (iv) judgment on three-point estimates. The analysis of risk factor in practice can be improved if the probability distributions of the key factors underlying an investment project are developed and information is communicated in that form.

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