Dr Liam Bastick (Director of Corality, Melbourne) discusses common methods of financial forecasting, difficulties in forecasting and how to assess the accuracy of forecasts.

1. Corality
Forecasting Techniques and
Financial Modelling

2. Forecasting
 Finance professionals need to learn efficient and effective data
forecasting methods in order to make effective decisions
 Almost all managerial decisions are based on forecasts of
future conditions
 Forecasts are needed throughout an organisation – and they
should certainly not be produced by an isolated group of
forecasters
 Forecasting is never “finished”
 Forecasts are needed continually, and as time moves on, the
impact of the forecasts on actual performance is measured,
original forecasts are updated, variance analysis assessed and
decisions modified, etc.

3. Forecasting
Decision Maker
Decisions Actions
Resources Interaction Forecasting
Bottom Line Performance

4. Forecasting considerations
 Managers are required to make decisions under
uncertainty about the future
 In order to make those decisions, it is necessary to
forecast key variables
 The choice of forecast models can have a significant
impact on the accuracy of forecasts
 It is necessary to understand forecasting methods (and
their limitations) in order to make reliable and timely
business decisions

5. Common methods of financial forecasting

6. Rolling forecast: overview
 Typically, a 12-month
budget which is prepared
and revised on a regular
basis during the year
 Applications:
 12-month rolling forecasts
in material pricing
 Weekly projections for
cash-strapped companies

7. Regression analysis: overview
 Establish the linear
relationship between variables
 Predict the value of the
dependent variable from one
(or more) independent
variables
 Example applications:
 Predict sales from advertising
 Predict consumption from
income

8. Moving average: overview
 Average of data points from a
specified number of
consecutive periods
 Moving average is “updated”
when new information
becomes available
 Applications:
 Moving average cost
(inventory costing method)

9. Weighted moving average: overview
 There are two types: Weighted Moving Average (WMA)
and Exponentially Weighted Moving Average (EWMA)
 They are similar to the Simple Moving Average (SMA),
but they assign more weight to recent observations than
older observations
 WMA assigns more weight to recent events than SMA, and
EWMA assigns more weight to recent events than WMA

10. Assessing accuracy of forecasts
 Forecast errors represent differences between actual
values and the estimated values
 We need to analyse them to determine the accuracy of
our forecasts
 Measures of Forecasting Errors
 Mean Squared Error (MSE)
 Mean Absolute Deviation (MAD)
 Cumulative Forecast Error (CFE)
 Mean Absolute Percentage Error (MAPE)

11. Difficulties in forecasting
 Often we do not know the underlying nature of our data
(e.g. linear or non-linear)
 Forecasts made on the basis of historical data may be
biased and not forward-looking
 It may be difficult to choose appropriate forecasting
models
 It is imperative to validate the usefulness of the models
we use, and to test their appropriateness to our business
data

12. Thank you!
Sydney
Michael Michaelides
+61 2 9222 9222
sales@corality.com
Melbourne
Liam Bastick
+61 3 8610 6301
contact@corality.com