1. FIN 650: Project Appraisal
Lecture 2
Forecasting Cash Flows
Required readings
Exercises in financial modelling
Evaluation
Homework
Paper
Office hours: Immediately before and after the class

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.
The cash flows forms the basis of Project appraisal
If the cash flows are not reliable, the detailed investment analysis can easily lead, regardless of the sophisticated project appraisal technique used, to poor business decisions.

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

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.
Cash inflows (from sales)
outflows (expenses)

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
TS, CS, Panel data

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)
Drying time 8 6 4 2
Temperature
Scatter diagram
Temperature Drying time

10. Two Variable Regression Results
Sample size 10
The smaller the value of Prob(t), the more significant the parameter and the less likely that the actual parameter value is zero.
t value statistically significant and
Not significant, it cannot be distinguished from Zero
R2 = coefficient of determinant
The (adjusted) R2 value of 0.92 says that 92% of the variation in the dependent variable is explained by the independent or exploratory variable.

11. Class Exercise I Given the regression estimate
Y = -28, 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, ( households).
Assuming that a separate data set forecasts the number of households at 1795 for the year 2002, then:
Sales(year 2002) = -28, (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, X X2,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, (households) (Income)
Forecast of sales for the year 2002 is:
Sales in year 2002 = -24, (35,000)+
(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, , 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, , (Year)
Forecast of sales for the year 2005 is:
Sales in 2005 = -44, (2, *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)
Special case of weighted average, weight for the adjacent observation --- selected
Smoothing constant, ∞ = 0.2 could range from 0 to 1
0.1 and 0.3 are more frequently used
Smaller values assigned to variables in the past
The closer ∞ is to zero the less influence the current observation has on the forecast when ∞ is near 1, the current observation has the greatest impact on forecast.

33. Forecasting Using Smoothing Models
For year
F(t+1) = ∞Yt + (1 - ∞) Ft
= 0.2 x (1 – 0.2) 42744

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
T = trend
C = cycle
S = seasonal
I = irregular movement

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.
Facebook
NSU
Recall Q.T. is applicable
Past information for value forecast is available
The information could be quantitative
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.
Cabinet meetings

44. Qualitative Forecasting: Data From Expert Opinion
Using groups -
The Nominal Group Technique is a face to face Delphi method, allowing group discussion.
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.