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Forecasting Cash Flows

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Will Lag Trends in Actual Cash Flow. Exponential Smoothing. Ft 1 = axt (1 - a)Ft ... Yt = a b(Y t-1) e. Linear Regression is Used to Find the Optimal ... – PowerPoint PPT presentation

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Title: Forecasting Cash Flows


1
Forecasting Cash Flows
  • Chapter 11

2
Why Forecast?
  • Liquidity Management
  • Financial Control
  • Variation of Actual from Forecast
  • Strategic Objectives
  • Capital Budgeting
  • Cost Management
  • Optimize Cash Usage
  • Exchange Rate Exposure Management

3
Forecasting Concerns
  • Forecasting Horizons
  • Short-Term
  • Daily
  • Weekly
  • Monthly
  • Medium-Term
  • Up to 1 year
  • Long-Term
  • Over 1 year
  • Degrees of Certainty
  • Certain
  • Interest, Royalties
  • Tax Payments
  • Predictable
  • Collections
  • Payroll
  • Vendor Payments
  • Less Predictable
  • New Product Sales, etc.

4
Selecting a Forecast Method
  • Establishing Data Relationships
  • Relationships between available data and cash
    flows.
  • Past Experience
  • Trends
  • Operating Relationships
  • Selecting a Method
  • Accurate Cost Effective

5
Testing and Validation
  • In-Sample Validation
  • Data used in model development
  • Out-of-Sample
  • Historical data not used in model development
  • On-Going Validation

6
Forecasting Methods
  • Receipts Disbursements
  • Kinda Like a Cash Budget
  • Short Medium Term Forecasting
  • Cash Receipts Schedule
  • Collections, Maturing Investments, etc.
  • Cash Disbursements Schedule
  • Vendor Payments, Payroll, Interest

7
Receipts and Disbursements Method
  • Steps in the Process
  • Develop a Sales Forecast for the Period
  • Range of Possible Values
  • Estimate Receipts
  • Cash Sales
  • Collections from Credit Sales
  • Other Cash Receipts
  • Estimate Cash Disbursements for the Period
  • Payroll
  • Vendor Payments

8
Receipts and Disbursements Method
  • Net Cash Flow Receipts - Disbursements
  • Subtract the Target (or Minimum) Cash Balance to
    Obtain the Cash Surplus or Cash Shortfall.

9
Receipts and Disbursements Method
10
Forecasting Receipts from Credit Sales
11
Forecasting Receipts from Credit Sales
12
Forecasting Receipts from Credit Sales
  • July Sales 20,250,000
  • August Sales 19,500,000
  • Forecasted September Sales 15,750,000
  • Forecasted September Collections
  • 0.25 x 20,250,000 5,062,500
  • 0.50 x 19,500,000 9,750,000
  • 0.25 x 15,750,000 3,937,500
  • 18,750,000

13
Forecasting Methods
  • Distribution Forecast
  • Based on Historical Distributions of Cash Flows
  • Averages
  • Regression
  • Estimate Percentage of Cash Inflows or
    Disbursements on Particular Days or Weeks
  • Short-Term Forecasting

14
The Distribution Method of Forecasting
  • Forecasted September Receipts
    18,750,000
  • You have determined that the monthly receipts are
    typically distributed in the following manner
  • Week 1 - 26.7
  • Week 2 - 13.3
  • Week 3 - 32
  • Week 4 - 28

15
The Distribution Method of Forecasting
  • The Weekly Receipts Forecast Is
  • Week 1 0.2667 x 18,750,000 5,000,000
  • Week 2 0.1333 x 18,750,000 2,500,000
  • Week 3 0.32 x 18,750,000 6,000,000
  • Week 4 0.28 x 18,750,000 5,250,000

16
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17
Forecasting Methods
  • Percentage of Sales
  • Determine the Relationships between Sales Levels
    and Asset Accounts
  • Express Asset Accounts that Vary with Sales as a
    Percentage of Sales
  • Multiple Percentages and Forecasted Sales
  • Long-Term Forecasting

18
Percentage of Sales Forecasting
19
Percentage of Sales Forecasting
20
Time Series Forecasting
  • Simple Moving Average
  • Rolling Average of Past Historical Values
  • Will Lag Trends in Actual Cash Flow
  • Exponential Smoothing
  • Ft1 axt (1 - a)Ft

21
Moving Averages
  • Moving Averages Smooth the Past History Data
  • The Moving Average Eliminates Some of the
    Randomness in the Data
  • Each Average is Computed by Dropping the Oldest
    Observation and Including the Next Observation

22
Moving Averages
23
Moving Averages
  • Using Moving Average Smoothers to Estimate the
    Trend-Cycle
  • A Larger Number of Observations Included in the
    Moving Average will Result in a Smoother
    Trend-Cycle

24
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25
The Data with a 5 MA Smoother
26
Exponential Smoothing
  • Allows a Greater Weight for More Recent
    Observations
  • F t1 (? x Yt) (1 - ?) x (Ft)
  • ? Smoothing Constant
  • 0 lt ? lt 1
  • A smoothing constant closer to 1.0 places more
    weight on the most recent actual observation.
  • A smoothing constant closer to 0 places more
    weight on the most recent forecast.

27
Exponential Smoothing
28
Regression
  • Linear Regression Fits a Trend Line Through the
    Data
  • The intercept and slope of the line are chosen to
    minimize the sum of the squared error terms.
  • Yt a b(Y t-1) e
  • Linear Regression is Used to Find the Optimal
    Values of a and b.

29
Regression Models
30
Regression Models
31
Regression Models
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