Capital Budgeting and Financial Planning

1. Capital Budgeting and Financial Planning Course Instructor: M.Jibran Sheikh Contact info: jibransheikh@comsats.edu.pk

2. Investment Appraisal under Uncertainty The problems with investment appraisals as we have seen so far are: All decisions are based on forecasts All forecasts are subject to varying degrees of uncertainty How to reflect uncertainty in financial decisions?

3. RISK DEFINED • Risk refers to the set of unique outcomes for a given event which can be assigned probabilities • Risk exists when the decision maker is in a position to assign probabilities to various outcomes i.e. a probability distribution is known to him. This happens when he has some historical data on the basis of which he assigns probability to other projects of the same nature

4. UNCERTAINTY DEFINED • Uncertainty refers to the outcomes of given event which are too unsure to be assigned probabilities • uncertainty exists when the decision maker has no historical data from which to develop a probability distribution and must make intelligent guesses in order to develop a subjective probability distribution Note: However, often risk and uncertainty are used (and will be used) interchangeably.

5. TYPOLOGY OF RISKS MARKET RISK Financial Risk CREDIT RISK LIQUIDITY RISK OPERATIONAL RISK RISKS Legal and regulatory Risk Business Risk Strategic Risk Reputation Risk

6. SUBDIVISION OF RISK General Market risk Equity price risk Trading risk Interest-rate risk Specific risk Gap risk Market Risk Foreign exchange risk Commodity price risk Financial Risk Issue risk Transaction risk Issuer risk Credit Risk Portfolio concentration Counterparty risk

7. Types of Project Risk Stand-alone risk: Project’s risk when held in isolation Corporate risk: Portion of the project’s risk that contributes to the firm’s risk Market risk: Portion of the project’s risk that contributes to shareholder risk Stand-alone risk Total (specific) risk of the project Diversification benefits not considered Measured by project’s Standard deviation & Coefficient of variation applied to NPV, IRR, PI, MIRR Inside the Firm: Project correlation with the firm’s other projects For Shareholders: Project’s correlation with shareholders’ other investments

8. Flatter distributions: • larger σ • larger stand-alone risk NPV 0 E(NPV) Measuring Stand-alone Risk

9. Corporate (Within Firm) Risk • Contribution of project’s risk to riskiness of the firm • Considers diversification benefits of the project within the firm • Correlation of the project with the rest of the firm • Measured by “Corporate Beta” [Beta computed by regressing project against firm (earnings, cash flow)]

10. MARKET RISK • Is the risk that changes in financial market prices and rates will reduce the monetary value (e.g. Rs., US $, UK £) of a security or a portfolio. • There are four major types of market risk • Interest Rate Risk: The simplest form of interest rate risk is the risk that the value of a fixed income security will fall as a result of an increase in market interest rates. • Equity Price Risk: The risk associated with volatility in stock prices. The general market risk of equity refers to sensitivity of an instrument or portfolio value to a change in the level of board stock market indices.

11. Market Risk-Continued • Foreign Exchange Risk: Foreign exchange risk arises from open or imperfectly hedged positions in a particular currency. These positions may arise as a natural consequence of business operation, rather than from any conscious desire to take a trading position in a currency. • Commodity Price Risk: The price risk of commodities differs considerably from interest-rate and foreign exchange risk, since most commodities are traded in market in which the concentration of supply in the hand of a few suppliers can magnify price volatility

12. Market Risk • Risk of the project for the firm’s shareholders • Impact of the project’s risk on the shareholder’s portfolio • Considers both corporate and market diversification benefits • Measured by “Project Beta” • Beta computed by regressing project against market Which Risk To Use? • Market risk is theoretically correct (firm’s should focus on shareholder wealth) • Corporate risk is of interest to other stakeholders (Creditors, suppliers, employees • Stand-alone risk • Easiest to compute, • Virtually same as corporate and market risk for most firms if firm’s lines of business are highly correlated • Only violated for firm’s with diverse product lines

13. Risk Adjusted Discount Rates • If a project is riskier than the firm (without the project), then it should offer a higher return than other projects the firm might invest in. • We have already done it when we calculated project cost of equity using estimated project beta and adjusted it for project leverage and tax • But as I said earlier it is better to adjust the project Cash Flows rather than adjusting the WACC for project-specific risk

14. Analyzing Stand Alone Risk The starting point for analysing a project’s stand-alone risk involves determining the uncertainty inherent in its cash flows. The nature of the individual cash flow distributions, and their correlations with one another, determine the nature of the NPV probability distribution and, thus, the project’s risk. Techniques for assessing a project’s stand-alone risk: (1) Sensitivity analysis, (2) Scenario analysis, and (3) Monte Carlo simulation (4) Accounting Breakeven (5) PV Breakeven

15. SENSIVITY ANALYSIS • It provides information as to how sensitive the estimated project parameters, namely, the expected cash flow, the discount rate and the project life are to estimation errors • The sensitive analyses provides different cash flow estimates under three assumptions: (i) the worst (i.e. the most pessimistic), (ii) the expected (i.e. the most likely), and (iii) the best (i.e. the most optimistic) outcomes associated with the project • But it has a limitation in that it does not disclose the chances of the occurrence of these variations. To remedy this shortcoming of sensitive analysis so as to provide a more accurate forecast, what is needed is that the probability of the variations occurring should also be given. Probability assignment to expected cash flow, therefore, would provided a more precise measure of variability of cash flow • The quantification of variability of returns involves two steps. First, depending on the chance of occurrence of a particular cash flow estimate, probabilities are assigned • The assignment of probabilities can be objective or subjective • The second step is to estimate the expected return on the project. The returns are expressed in terms of expected monetary values. The expected value of a project is a weighted average return , where the weight are the probabilities assigned to the various expected events, i.e. the expected monetary values of estimated cash flows multiplied by the probabilities.

16. Sensitivity analysis (What if analysis) Sensitivity analysis is a technique that indicates how much NPV will change in response to a given change in an input variable, other things held constant. This analysis is usually applied to one estimate at a time although it can be applied to each estimate simultaneously. (see Excel Sheet) We can use Solver Function in Excel to calculate values for different variables that give breakeven NPV (or accounting profit). We can also calculate breakeven values manually (see demonstration).

17. 1. Sensitivity analysis (What if analysis) We can check the sensitivity of NPV with respect to each variable by using the following method: %∆ Revenues = Revenues new – Revenues old Revenues old %∆ NPV = NPV new – NPV old NPV old To find percentage drop in NPV due to 1 % drop in Revenues: = %∆ NPV %∆ Revenues

18. 2. Scenario Analysis (Expected Values) When considering an investment decision it may be possible to make several predictions about alternative future outcomes (scenarios) and to assign probabilities to each outcome (scenario). An expected value is then computed by multiplying the value of each possible outcome (scenario) by the probability of that outcome (scenario) and summing the result. Example: Cash flows from a new venture may depend on whether a competitor decides to open up in the same area. We make the following estimates: Competitor opens up Probability Project NPV Yes 0.3 -10,000 No 0.7 20,000 Expected NPV of this venture would be: (0.3 x 10,000)+(0.7 x 20,000) = 11,000

19. 2. Scenario Analysis (Expected Values) Such an analysis is called Scenario Analysis. Remember however that expected value is (as shown in the example) simply the average of a probability distribution. It does not represent what the actual outcome will be, nor does it represent the most likely result. What it actually represents is the average payoff per occasion if the project were repeated may times (i.e. long-run average) The simple decision rule using expected values is to accept projects with a positive expected NPV. Problems with Scenario analysis: Project will not be repeated resulting in sizeable loss Probabilities are subjective estimates (on a scale of 0 to 1). There is often little data on which to base these estimates.

20. 2. Scenario Analysis (Expected Values) Normally begin with the base case, ormost likely set of values for the input variables and then specify a worst-case scenario and a best-case scenario. Typically base case has a probability of 50% while 25% assigned to each of the other scenarios. But this will depend upon the situation. And their may be more than just three scenarios. Expected NPV is calculated using this formula: SD of NPV is represented by : While Coefficient of variation is: The project’s coefficient of variation can be compared with the coefficient of variation of “average” project to get an idea of the relative risk of the proposed project.

21. 2. Scenario Analysis (Expected Values) Scenario Analysis Example Start Newsagents stocks a weekly magazine. Umer, the owner can buy the magazines for Rs. 15 each and sell them at retail price of Rs. 25 each. At the end of the week unsold magazines are obsolete and have no value. Umer has estimated a probability distribution for weekly demand as follows: Weekly demand in units Probability 10 0.20 15 0.55 20 0.25 If Umer is to order a fixed quantity of magazines per week, how many should that be? Assume no seasonal variation in demand.

22. 2. Scenario Analysis (Expected Values) Scenario Analysis Example - Solution 1) Pay off/magazine: No Sale > loss of 15 Sale > profit of 25 – 15 = Rs. 10/week 2) Payoff and Expected Payoff for each strategy: From the table it can be seen that the strategy which gives the highest expected payoff is to stock 15 magazines each week.