CoralityForecasting Techniques and

Financial Modelling

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.

Forecasting

Forecasting

Decision Maker

Decisions Actions

Interaction

Bottom Line Performance

Resources Forecasting

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

Common methods of financial forecasting

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

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

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)

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

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)

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

Thank you!

Sydney

Michael Michaelides

+61 2 9222 9222

sales@corality.com

Melbourne

Liam Bastick

+61 3 8610 6301

contact@corality.com