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FIN 650: Project Appraisal Lecture 2 Forecasting Cash Flows

FIN 650: Project Appraisal Lecture 2 Forecasting Cash Flows. Forecasting: Techniques and Routes. Forecasting is the establishment of future expectations by the analysis of past data, or the formation of opinions. Forecasting expected cash flows is an essential element of capital budgeting.

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FIN 650: Project Appraisal Lecture 2 Forecasting Cash Flows

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  1. FIN 650: Project Appraisal Lecture 2 Forecasting Cash Flows

  2. Forecasting: Techniques and Routes • Forecasting is the establishment of future expectations by the analysis of past data, or the formation of opinions. • Forecasting expected cash flows is an essential element of capital budgeting. • Capital budgeting requires the commitment of significant funds today in the hope of long term benefits. The role of forecasting is the estimation of these benefits.

  3. Techniques Routes Top-down route Bottom-up route Qualitative Quantitative • Delphi method • Nominal group technique • Jury of executive opinion • Scenario projection • Simple regressions • Multiple regressions • Time trends • Moving averages Forecasting Techniques and Routes

  4. Cash Flow Estimation for Project Appraisal • Four stages: • Forecasting the capital outlays and operating cash inflows and outflows of the proposed project • Adjusting these estimates for tax factors and calculating after tax cash flows • Conducting Sensitivity analysis • Allocating further resources, if necessary to improve the reliability of the initial variables identified in the preceding stage. • Long term investment – look at annual rather than weekly or monthly cash flows.

  5. Quantitative Techniques • Use of quantitative techniques is possible, when • Past information about the variable being forecast is available; and • Information can be quantified • Use quantitative data and methods to estimate relationships between variables or to identify the behavior of a single variable over a period of time. • These relationships or behaviors are then used to make the forecasts.

  6. Forecasting with Regression Analysis • Data types • Dependent and independent (or explanatory) variables • Car sales, personal income, the price, price of its close substitute brand, advertising • Identify and collect historical values of the variables • OLS techniques • Two-variable regression model, one explanatory variable explaining the behavior of the dependent variable • Multiple regression model, two or more variables explaining the behavior of the dependent variable

  7. Forecasting with Regression Analysis

  8. The Two Variable Regression Model Y = α + βX + μ Where: Y= the dependent variable, desks sold X= The independent or explanatory variable, number of households α = a parameter of the regression equation called the regression intercept Β = a parameter of the regression equation called the slope or regression coefficient μ = stochastic disturbance or the error term

  9. Forecasting with Regression Analysis • Two variable regression model (Workbook 3.2)

  10. Two Variable Regression Results

  11. Class Exercise I • Given the regression estimate Y = -28,326 + 2.945 X, R2= 0.92 (-3.2) (9.9) Calculate desk sales for the year 2002.

  12. Forecasting with Regression Analysis

  13. Quantitative Forecasting Quantitative:Sales regressed on households. Predicting with the regression output. Regression equation is: Sales(for year) = -28,326 + 2.945 ( households). Assuming that a separate data set forecasts the number of households at 1795 for the year 2002, then: Sales(year 2002) = -28,326 + 2.945(35,000) = 74,749 units.

  14. Forecasting with Regression Analysis

  15. The Multiple Regression Model Y = α + β1X1 + β2X2 + μ Where: Y= the dependent variable, desks sold X1= The independent or explanatory variable, number of households X2 = The independent or explanatory variable, income α = a parameter of the regression equation called the regression intercept β1, β2 = parameters of the regression equation called the slope or regression coefficient μ = stochastic disturbance or the error term

  16. Multiple Regression Results

  17. Forecasting with Regression Analysis • The multiple regression model (Workbook 3.2)

  18. Class Exercise II • Given the regression estimate Y = -24,237 + 1.426 X1+0.944X2,R2= 0.96 (-3.86) (2.67) (3.09) X1 and X2 are number of households and income respectively Calculate desk sales for the year 2002.

  19. Forecasting with Regression Analysis

  20. Quantitative Forecasting:Using Multiple Regression Multiple regression equation is: Sales in year = -24,237 +1.426 (households) + 0.944(Income) Forecast of sales for the year 2002 is: Sales in year 2002 = -24,237 + 1.426(35,000)+ + 0.944(52,000) = 74,761 Units

  21. Forecasting with Regression Analysis • Forecasting using regression results (Workbook 3.2)

  22. Forecasting with Regression Analysis • Forecasting with time-trend projections

  23. Forecasting with Regression Analysis • Forecasting with time-trend projections

  24. Class Exercise III • Given the regression estimate Y = 44,535.67 + 2,550.788 T, R2= 0.87 Where T is the explanatory variable, time Calculate desk sales for the year 2005.

  25. Quantitative Forecasting: Regression Line Use Equation for the regression line is: Sales in year = -44,535.67 + 2,550.788(Year) Forecast of sales for the year 2005 is: Sales in 2005 = -44,535.67 + (2,550.788*14) = 80,247 Units

  26. Forecasting with Regression Analysis • Forecasting with time-trend projections

  27. Forecasting with Regression Analysis • Forecasting with time-trend projections (Workbook 3.3)

  28. Forecasting Using Smoothing Models

  29. Forecasting Using Smoothing Models • Simple moving average: First three year SMA = (39,000+30,500+45,000)/3 = 38,167 Second three year SMA = (30,500+45,000+50,000)/3 = 41,833 Calculated by dropping year 1 and adding year 4 Last three year SMA = (50,000+41,000+49,000)/3 = 46,667

  30. Forecasting Using Smoothing Models • Weighted moving average • In SMA each observation in the calculation receives equal weight • In WMA different weights are assigned to each observation in the time series. For example, more weight may be assigned to recent data. The weights must add up to 1 • Three year WMA for years 1-12 is • WMA = 50,000(0.1) 41,000(0.3)+49,000(0.6)= 46,700

  31. Forecasting Using Smoothing Models • Simple moving average (Workbook 3.4)

  32. Forecasting Using Smoothing Models • Exponential smoothing is a special case of WMA in which one weight-the weight for the most recent observation is selected. Weight assigned to the most recent observation is call the smoothing constant α Ft+1= αYt+(1- α)Ft Where: Ft+1= forecast value for period t+1 Ft= forecast value for period t Yt= actual value for period t α =the smoothing constant(0< α<1)

  33. Forecasting Using Smoothing Models

  34. Forecasting Using Smoothing Models • Exponential smoothing (Workbook3.5)

  35. More Complex Time Series Forecasting Methods • Classical time series approach separates an observed series for a variable into the components of trend, cyclical variation, seasonal movements and random variation • Y=T+C+S+I or Y=TxCxSxI • Modern time series analysis techniques • ARCH-Autoregressive conditional heteroscedasticity • GARCH- Generalized autoregressive conditional heteroscedasticity • ARIMA- Autoregressive integrated moving average • VAL-Vector autoregressive lag • ADL- Autoregressive distributed lag • Mechanical approach to forecasting

  36. Forecasting Routes • Top-Down • Where international and national events affect the future behaviour of local variables. • Project dealing with internationally traded commodity • Global macro level-international economic conditions- forecasts for the proposed project at the micro level • International RMG price trend, project output price • Production of RMG by the project • Operational expenditure forecast • Tax factors • Net after tax operating cash flows • Bottom-up • Small project dealing with local market

  37. Qualitative Forecasting Using expert opinion and collective experience to unlock the secrets of the future.

  38. The keys to employing qualitative forecasting are: • Data as an historical series is not available,or is not relevant to future needs. • An unusual product or a unique project is being contemplated. • Even when quantitative techniques are used, • estimates may be combined with qualitative • judgments

  39. Why use human judgement? • People may be better able to detect random variation. • People might be able to integrate external (non-time series) information in the forecasting process.

  40. Qualitative Forecasting:Data From Expert Opinion By Survey- • Data can be gathered by phone or in writing. • Data comes in three categories: • Highly valuable • Absolutely essential • Supporting material. • The survey group is known as the ‘reference population’.

  41. Qualitative Forecasting: Data From Expert Opinion • Obtaining information from individuals • Using groups to make forecasts • Jury of executive opinion • senior managers draw upon their collective wisdom to map out future events. These discussions are carried out in open meeting, and may be subject to the drawbacks of group think and personality dominance.

  42. Major Steps in the Survey Identify Information Needs ` Sampling design Forward links Develop questionnaire Collect data Backward links Analyze data Write report

  43. Qualitative Forecasting: Data From Expert Opinion Using groups- • The Delphi Method: drawing upon the group’s expertise by getting individual submissions, without the drawback of face to face meetings. The Delphi Method is named after a famous Oracle who prophesied in the ancient Greek city of Delphi. An Oracle (wise person) interceded between men and gods.

  44. The Nominal Group Technique is a face to face Delphi method, allowing group discussion. Qualitative Forecasting: Data From Expert Opinion Using groups - • The Devils Advocate method poses sub-groups to question the group’s findings. • The Dialectical Inquiry method poses sub-groups to challenge the group’s findings with alternative scenarios.

  45. Qualitative Forecasting: Using Expert Opinion Output from the group techniques is sorted into scenarios. • These scenarios are further reviewed by the group. • A final ‘consensus of opinion’ forecast is accepted by the group.

  46. Qualitative Forecasting: Summary • Qualitative forecasting is used when historical data is not available, or when the planning horizon is very long. • Qualitative forecasting uses expert opinion, collected in a variety of ways. • Collected expert wisdom has to be carefully managed. • Research shows that both the Delphi Method, and the Nominal Group technique, are reliable forecast methods.

  47. Forecasting: Summary • Sophisticated forecasting is essential for capital budgeting decisions • Quantitative forecasting uses historical data to establish relationships and trends which can be projected into the future • Qualitative forecasting uses experience and judgment to establish future behaviours • Forecasts can be made by either the‘top down’ or ‘bottom up’ routes.

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