CASH MANAGEMENT

Forecasting Future Cash Receipts and Payments

Information

• Sales information• Production information• Accounting information• Forecast information

Forecasting Future Cash Receipts and Payments

• Time series analysis• Graphical presentation• Calculating a trend using moving averages• Calculating seasonal variations• Mark ups and margins (already done!)• Using time series analysis in cash budgeting• Dealing with inflation in cash budgeting• Using indices.

Time series analysis

• When preparing cash budgets a large number of figures are estimated– Sales– Purchases– Wages etc

• By looking at the past we can estimate the future trends and patterns

• A time series is simply a record of previous figures over a period of time

Time series analysis• Analysing these graphically can help visualise

these figures

Month Sales £'000January 2,030February 1,570March 1,620April 2,100May 2,080June 1,740July 1,690August 2,190September 2,150October 1,830November 1,780December 2,200

Time series analysis

The trend line

Moving averages

• The trend line previously shown could be extended into future months for an indication of future trends

• More technical methods are available• Moving averages is the average of each

successive group• Can be taken using either an odd or even

number of figures for each average• For this syllabus we use odd, a bit easier!

Moving averages

• From the previous example we calculate:– Three period moving average for sales Jan – March– Then the three period moving average for sales

Feb – April– Then the three period moving average for sales

March – May etc.– The moving average is placed against the period

which is the mid point of the range

Moving averages

Month Sales £'000 Moving average £’000January 2,030February 1,570 1,740March 1,620 1,763April 2,100 1,933May 2,080 1,973June 1,740 1,837July 1,690 1,873August 2,190 2,010September 2,150 2,057October 1,830 1,920November 1,780 1,937December 2,200

Moving averages

• From the previous data we can:– See a clear upward trend as time advances– Calculate the average increase across each month– (1,937 – 1,740) ÷ 9– £22,000 per month

• Task – review example page 30, complete activity 1 page 31, activity 2 page 32

Seasonal variations

• Figures in a time series are made up of a number of different elements:– Trend – general movement we have seen in the

time series– Cyclical variation – long term movements in the

economy– Random variation – unexplained random events– Seasonal variation – patterns during successive

periods (Christmas, bonfire night!)

Seasonal variations - additive model

• Each figures in a time series is made up as follows:

A = T + SA = Actual figureT = Trend figureS = Seasonal variation

∴ S = A - T

Using time series analysis in cash budgeting

• We have seen how to calculate a trend• Now we must use this in cash budgeting• We estimate future figures• Based upon past performance• Known as EXTRAPOLATION

Using time series analysis in cash budgeting

• Using the example before we calculated the trend as a monthly increase of £22,000

• To calculate January – April figures we add multiples of £22,000

• January’s figure is calculated by adding 2 x £22,000 to the November figure

• Each subsequent month by adding £22,000 to the result of the preceding month

Using time series analysis in cash budgeting

20X9 Period Trend £’000

January ((1,937 + (2 x 22)) 1,981

February (1,981 + 22) 2,003

March (2,003 + 22) 2,025

April (2,025 + 22) 2,047

Using time series analysis in cash budgeting

Seasonal variation

January February March April

£’000 £’000 £’000 £’000

Seasonal variation + 174 -170 -235 + 231

• Note we now have to take into account the seasonal variations

• Under the additive model the variation is either added to, or subtracted from the trend figure (variation figures are given below):

Using time series analysis in cash budgeting

20X9 Period Trend £’000 Seasonal variation £’000

Estimated sales £’000

January ((1,937 + (2 x 22)) 1,981 + 174 2,155

February (1,981 + 22) 2,003 -170 1,833

March (2,003 + 22) 2,025 -235 1,790

April (2,025 + 22) 2,047 + 231 2,278

Using time series analysis in cash budgeting

• Problems using time series analysis include:– The less historical data available the less reliable

the results will be– The further into the future we forecast the less

reliable the results will be– Assumes trend and seasonal variations from the

past will continue– Cyclical and random variations have been ignored.

Seasonal variations - additive model

• Example page 32• Activity 3 page 34• Activity 4 page 35• Workbook Activity 6 page 43• Task 2 - Handout

Indices

• A way of expressing price changes/figures and fluctuations over time

• Compared to a base year• Base Year is given an index of 100• First step is to determine the base year• If price is greater, then index will be > 100• If price is less, then index will be < 100

Indices

• A specific price index relates to a specific item• A general price index measures a variety of goods• Retail price index gives a good indicator of

inflation in the economy, base month of January 1987 – January 2012 figure was 238.0 (link)

• Index numbers can be used to forecast future data to be included in cash flows.

Indices - example

• Given the base price and the RPI we can estimate future months.

• General overheads in December £75k, increase in line with general inflation defined by the RPI. In December the RPI stood at 196.6. In January it is anticipated to be 197.2 and in February 198.1.

• What are the estimated overhead costs in these coming 2 months?

Indices - example

Current Index x Base CostBase Index

197.2 x 75,000 to calculate January196.6

198.1 x 75,000 to calculate February196.6

Indices – further example given prices we can use a similar method for calculating index values

(calculate % change over time)

Cost price of commodity

Year Unit price £ Calculation Index value

20X3 14.60 Base year 100.0

20X4 14.40 14.40/14.60x100 98.6

20X5 15.20 15.20/14.60x100 104.1

20X6 15.00 15.00/14.60x100 102.7

20X7 15.60 15.60/14.60x100 106.8

20X8 16.00 16.00/14.60x100 109.6

Current Costs x 100Base Costs

Student Tasks

• Look at Example page 36• Example page 38• Activity 5 p 39• Test your Knowledge – page 43 and 44• Test your Learning – BPP p 66-69