Forecasting Ppt Lecture note: Forecasting involves making predictions about the future

orecasting involves making predictions about the future. In finance, forecasting is used by companies to estimate earnings or other data for subsequent periods. Traders and analysts use forecasts in valuation models, to time trades, and to identify trends. Forecasts are often predicated on historical data. Forecasting addresses a problem or set of data. Economists make assumptions regarding the situation being analyzed that must be established before the variables of the forecasting are determined. Based on the items determined, an appropriate data set is selected and used in the manipulation of information. The data is analyzed, and the forecast is determined. Finally, a verification period occurs when the forecast is compared to the actual results to establish a more accurate model for forecasting in the future. Forecasting Techniques
In general, forecasting can be approached using qualitative techniques or quantitative ones. Quantitative methods of forecasting exclude expert opinions and utilize statistical data based on quantitative information. Quantitative forecasting models include time series methods, discounting, analysis of leading or lagging indicators, and econometric modeling that may try to ascertain causal links.

1. Forecasting
Dr. Everette S. Gardner, Jr.

2. Forecasting 2
Judgment exercises
Exercise 1
Finished files are the result of years of scientific study
combined with the experience of years.
How many times does the letter F appear in the sentence
above? Count them only once; do not go back and count them
again. ________
How confident are you in your answer? Rate your confidence
on a scale of 0 to 100, where 0 means that you are sure you are
wrong, and 100 means that you are sure you right.
_________

3. Forecasting 3
Judgment exercises (cont.)
Exercise 2
Threatened by a superior enemy force, the general
faces a dilemma. His intelligence officers say his
soldiers will be caught in an ambush in which 600 of
them will die unless he leads them to safety by one
of two available routes. If he takes the first route,
200 soldiers will be saved. If he takes the second
route, there’s a one-third chance that 600 soldiers
will be saved and a two-thirds chance that none will
be saved. Which route should he take? ________

4. Forecasting 4
Judgment exercises (cont.)
Exercise 3
The general again has to choose between two escape
routes. But this time his aides tell him that if he
takes the first, 400 soldiers will die. If he takes the
second, there’s a one-third chance that no soldiers
will die, and a two-thirds chance that 600 soldiers will
die. Which route should he take? _______

5. Forecasting 5
Judgment exercises (cont.)
Exercise 4
Linda is 31, single, outspoken, and very bright. She
majored in philosophy in college. As a student, she
was deeply concerned with discrimination and other
social issues, and participated in anti-nuclear
demonstrations. Which statement is more likely?
a. Linda is a bank teller.
b. Linda is a bank teller and active in the feminist
movement.
_______

6. Forecasting 6
Human biases in forecasting
Company Politics
●Forecast what the boss wants to hear
Overconfidence
●Confidence has no relation to accuracy
Wishful thinking
●Optimistic forecasts more probable
Success/failure attribution
●Good forecasts due to skill, bad due to chance

7. Forecasting 7
Human biases in forecasting
Gambler’s fallacy
●Bad luck and good luck will balance out
Data presentation
●Misleading graphs/tables easily accepted
Conservatism
●Refusal to accept drastic change

8. Forecasting 8
Forecasting methods
Human judgment
●Subject to bias and inconsistency
●Models usually beat humans
Time series forecasting
●Based on analysis of past history
●Cheap and easy
●On average, most accurate method
●Should always be attempted

9. Forecasting 9
Forecasting methods (cont.)
Regression modeling
●Based on causal relationships
●Expensive and difficult
●Must forecast independent variables
Growth or market development models
●Based on assumed growth patterns
●Cheap and easy
●Difficult to validate

10. Forecasting 10
Can your data be forecasted
by a model?
Use common sense
●Abrupt turning points usually impossible to predict
Compare your accuracy to a naïve benchmark
●Forecast for next period is the same as the data
this period
●If you cannot beat a naïve benchmark, forecasting
is usually futile

11. Forecasting 11
Forecast profiles
Additive Multiplicative
Nonseasonal Seasonality Seasonality
Constant
Level
Linear
Trend
Exponential
Trend
Damped
Trend

12. Forecasting 12
Simple exponential smoothing
(1)Error in t = Actual data –Forecast for t
(2)Forecast for t+1 = Forecast for t + α(Error in t)
To get started:
Set first forecast equal to mean of first few data.
Smoothing weight (α):
In practice, αis usually 0.30 –0.50.
Effects of extreme αvalues:
If α= 0, the forecast never changes.
If α= 1, this is a naïve or random walk model.
Simple.xls

13. Forecasting 13
Error measures for
evaluating forecast models
●MAD = Mean absolute deviation (error)
●MSE = Mean squared error
●MAPE = Mean absolute percentage error

14. Forecasting 14
Smoothing a linear trend
(1) Error in t = Actual data -Forecast for t
(2) Level at end of t = Forecast for t + h
1(Error in t)
(3) Trend at end of t = Trend at end of t–1 + h
2(Error in t)
(4) Forecast for t+1 = Level at end of t + Trend at end of t
To get started, set:
Initial trend = Average growth in first four data
Initial level = First data observation –initial trend
Search for weights in the following ranges:
Level weight (h
1)0.10 to 0.90, increments of .10
Trend weight (h
2) 0.05 to 0.30, increments of .05
Trendsmooth.xls

15. Forecasting 15
The general trend model
(1) Error in t = Actual data –Forecast for t
(2) Level at end of t = Forecast for t + h
1(Error in t)
(3) Trend at end of t = ø(Trend at end of t –1) + h
2(Error in t)
(4) Forecast for t+1 = Level at end of t + ø(Trend at end of t)
Long-term forecasting:
Forecast for t+2 = Forecast for t+1 + ø
2
(Trend at end of t)
Forecast for t+3 = Forecast for t+2 + ø
3
(Trend at end of t)
Trend possibilities:
If ø < 1, the trend is damped.
If ø = 1, the trend is linear.
If ø > 1, the trend is exponential.
If ø = 0, there is no trend (same as simple smoothing).

16. Forecasting 16
Starting up the general trend model
Initial values
Initial trend = Average growth in first four data
Initial level = First data observation –Initial trend
Search for parameters in the following ranges
Level weight (h
1)0.10 to 0.90
Trend weight (h
2)0.05 to 0.30
Phi (ø) 0.60 to 1.00

17. Forecasting 17
Multiplicative seasonality
The seasonal index is the expected ratioof actual
data to the average for the year.
Actual data / Index = Seasonally adjusted data
Seasonally adjusted data x Index = Actual data
Multimon.xls

18. Forecasting 18
Multiplicative seasonal adjustment
1.Compute moving average based on length of seasonality
(4 quarters or 12 months).
2.Divide actual data by corresponding moving average.
3.Average ratios to eliminate randomness.
4.Compute normalization factor to adjust mean ratios so they sum
to 4 (quarterly data) or 12 (monthly data).
5.Multiply mean ratios by normalization factor to get final seasonal
indexes.
6.Deseasonalize data by dividing by the seasonal index.
7.Forecast deseasonalized data.
8.Seasonalize forecasts from step 7 to get final forecasts.

19. Forecasting 19
Additive seasonality
The seasonal index is the expected difference
between actual data and the average for the year.
Actual data -Index = Seasonally adjusted data
Seasonally adjusted data + Index = Actual data
Additmon.xls

20. Forecasting 20
Additive seasonal adjustment
1.Compute moving average based on length of seasonality
(4 quarters or 12 months).
2.Compute differences: Actual data -moving average.
3.Average differences to eliminate randomness.
4.Compute normalization factor to adjust mean differences so they
sum to zero.
5.Compute final indexes: Mean difference –normalization factor.
6.Deseasonalize data: Actual data –seasonal index.
7.Forecast deseasonalized data.
8.Seasonalize forecasts from step 7 to get final forecasts.

21. Forecasting 21
Forecasting simulations
Dynamic simulation
●Short-range (one-step-ahead) forecasting test
●Use data in fit periods to select model
●During forecast periods:
1. Make one forecast.
2. Observe error.
3. Adjust model.
4. Go to 1.
Static simulation
●Long-range forecasting test
●Use data in fit periods to select model
●Make all forecasts at once

22. Forecasting 22
Data transformations for
forecasting
Deseasonalize
Isolates trend
% Change
Isolates trend
Natural log
Converts exponential trend to linear
Square root
Reduce variance
Aggregate
Quarterly or monthly data to annual

23. Forecasting 23
Forecasting management
Organize for forecasting
●Pinpoint responsibility
●Only one corporate forecast
●Separate forecasting and planning
Monitor accuracy
●Choose a standard measure
●Keep a track record
●Benchmark
●Hold performance reviews
Scrub the data
●Adjust outliers
●Throw out unique data

24. Forecasting 24
Forecasting management (cont.)
Compare alternative forecasts
●Top-down vs. bottom up
●Monthly vs. quarterly data
●Deseasonalized vs. raw data
●Percent change data
●Time series forecasts
●Regression forecasts
Simulate forecasting
●One-step-ahead
●Long-range
Estimate confidence limits
●What is the range of forecast errors in past?