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