CHAPTER 5 FINANCIAL FORECASTING FINANCIAL FORECASTING

5,531 views
5,186 views

Published on

0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
5,531
On SlideShare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
156
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide

CHAPTER 5 FINANCIAL FORECASTING FINANCIAL FORECASTING

  1. 1. CHAPTER 5 FINANCIAL FORECASTING
  2. 2. FINANCIAL FORECASTING <ul><li>Percent of Sales Method </li></ul><ul><li>Linear Trend Extrapolation </li></ul><ul><li>Regression Analysis </li></ul>
  3. 3. PERCENT OF SALES METHOD <ul><li>A. PERCENT OF SALES METHOD </li></ul><ul><li>Simplest forecasting method </li></ul><ul><li>Forecasting the income statement and balance sheet items as percentages of sales forecast </li></ul><ul><li>Sales forecast is assumed to be given </li></ul>
  4. 4. PERCENT OF SALES METHOD <ul><li>1. Forecasting Income Statement </li></ul><ul><li>2. Forecasting Assets on Balance Sheet </li></ul><ul><li>3. Forecasting Liabilities on Balance Sheet </li></ul><ul><li>4. Discretionary Financing </li></ul>
  5. 5. PERCENT OF SALES METHOD <ul><li>1. Forecasting Income Statement </li></ul><ul><li>- Use Common-Size Income Statement </li></ul><ul><li>- Determine items that will change with sales: </li></ul><ul><li>i. Cost of Goods Sold </li></ul><ul><li>ii. Selling and G&A Expenses (maybe) </li></ul><ul><li>- Assume that the sales forecast is given </li></ul>
  6. 6. PERCENT OF SALES METHOD <ul><li>2. Forecasting Assets on Balance Sheet </li></ul><ul><li>We can not use common-size balance sheet for forecasting assets </li></ul><ul><li>Decide on the Assets that may change with Sales : </li></ul><ul><ul><li>Cash Balance </li></ul></ul><ul><ul><li>Accounts Receivable </li></ul></ul><ul><ul><li>Inventory </li></ul></ul><ul><ul><li>Plant and Equipment : </li></ul></ul><ul><ul><li>Accumulated Depreciation </li></ul></ul>
  7. 7. PERCENT OF SALES METHOD <ul><li>3. Forecasting Liabilities on Balance Sheet </li></ul><ul><li>We need to categorize liabilities into two groups: </li></ul><ul><li>a) Spontaneous Sources of Financing </li></ul><ul><li>Arise in ordinary course of business, change with sales </li></ul><ul><li>Example: Accounts Payable, Other Current Liabilities </li></ul><ul><li>b) Discretionary Sources of Financing </li></ul><ul><li>These sources of financing requires great effort. Involve upper-level management decisions. Do not change with sales. </li></ul><ul><li>Example: Bonds, Bank loans, Common and Preferred Stock </li></ul>
  8. 8. PERCENT OF SALES METHOD <ul><li>Accounts Payable and Other Current Liabilities </li></ul><ul><ul><li>Change with sales </li></ul></ul><ul><li>Retained Earnings </li></ul><ul><ul><li>Previous year level + Additions in this year (from Income Statement </li></ul></ul><ul><li>Other Items of Liability Section </li></ul><ul><ul><li>Assume same level as previous year </li></ul></ul>
  9. 9. PERCENT OF SALES METHOD <ul><li>4. Discretionary Financing </li></ul><ul><li>Balance sheet plug: </li></ul><ul><ul><li>Total Assets – Total Liability and Owner’s Equity </li></ul></ul><ul><li>A negative value forecasts a surplus of discretionary financing </li></ul><ul><li>A positive value, forecasts a deficit of discretionary financing, and means that more discretionary funds will be needed. </li></ul>
  10. 10. LINEAR TREND EXTRAPOLATION <ul><li>B. LINEAR TREND EXTRAPOLATION </li></ul><ul><li>In the percent of sales method, we assumed that you are given the sales forecast. </li></ul><ul><li>Assume that you are not given the sales forecast but you have to do it yourself </li></ul><ul><li>TREND function of Excel </li></ul><ul><li>TREND(Known_Y’s, Known_X’s, New_X’s, Constant) </li></ul><ul><li>Y is the variable we want to forecast ( dependent variable )(in our example it is Sales) </li></ul><ul><li>X is the variable we use to forecast Y ( independent variable ) (in our example, it is Years) </li></ul><ul><li>New_X is the new variable value to forecast Y </li></ul><ul><li>Constant is a TRUE/FALSE variable. If you want an intercept, write True, else write False. </li></ul>
  11. 11. Adding a Trendline to the Chart <ul><li>Double Click the X-Y scatter chart, click on the plot and right click the mouse </li></ul><ul><li>Choose Insert Trendline from the menu </li></ul><ul><li>Displaying the Trend Equation </li></ul><ul><li>- Right click the mouse on the trendline, choose Format Trendline, go to Options tab select Display Equation on the Chart - </li></ul>
  12. 12. REGRESSION ANALYSIS <ul><li>C. REGRESSION ANALYSIS </li></ul><ul><li>Regression analysis is the method used to fit the best line to a data set </li></ul><ul><li>The best line is the line that minimizes the sum of squared errors. The errors are the difference between the actual data point and the one predicted by the model. </li></ul>
  13. 13. REGRESSION ANALYSIS <ul><li>Example: </li></ul><ul><li>Suppose you want to buy a Yahoo stock, and you want to know how the stock price moves with the market </li></ul><ul><li>You want to explain the return on Yahoo stock by the return on S&P 500 index </li></ul>
  14. 14. REGRESSION ANALYSIS <ul><li>First, you should collect data on the returns of Yahoo and S&P 500. </li></ul><ul><li>Enter these data on Excel (Most probably, the data you found will be price data) </li></ul><ul><li>Find the returns </li></ul><ul><li>Return = (Price Now) / (Price one period ago) - 1 </li></ul><ul><li>Note after finding the first returns of S&P 500, and Yahoo, drag the formulas to other cells </li></ul>
  15. 15. REGRESSION ANALYSIS <ul><li>Select the return range of S&P 500 and Yahoo , go to Chart Wizard , and create a Scatter Plot , choose Use 1st Column as X data , so S&P 500 returns will be on the X-axis, and Yahoo returns will be on the Y axis </li></ul>
  16. 16. REGRESSION ANALYSIS <ul><li>Analyzing the relation between the returns of Yahoo and S&P 500 from the Scatter diagram is a little difficult. You can possibly detect a vague positive relation between the Yahoo and S&P 500 returns. </li></ul><ul><li>You can add a trend line in the chart to help you see the linear relation. </li></ul>
  17. 17. REGRESSION ANALYSIS <ul><li>Regression Equation : </li></ul><ul><li>You want to explain the returns of Yahoo in relation to the returns of S&P 500. Similar to the Linear Trend Equation the equation is as follows: </li></ul><ul><li>Yahoo Return = a + b*(S&P500 return) + e </li></ul>
  18. 18. REGRESSION ANALYSIS <ul><li>Here Yahoo returns is the dependent variable that we want to explain </li></ul><ul><li>S&P500 returns is the independent variable we use to explain Yahoo returns </li></ul><ul><li>a is the intercept of the regression equation </li></ul><ul><li>b is the slope of regression equation </li></ul><ul><li>e is the error term: Error between the actual data and the fitted data </li></ul>
  19. 19. REGRESSION ANALYSIS <ul><li>Regression Analysis Using Excel </li></ul><ul><li>- Go to T ools Menu, click on D ata Analysis </li></ul><ul><li>- Select Regression </li></ul><ul><li>- When the regression dialog box appears, </li></ul><ul><li>Enter the range which covers Yahoo Returns in the Input Y Range ( Y is our dependent variable ) </li></ul><ul><li>Enter the range which covers S&P500 Returns in the Input X Range ( X is our dependent variable ) </li></ul>
  20. 20. REGRESSION ANALYSIS <ul><li>- If labels are included in the entered range in X and Y values, check the Label box </li></ul><ul><li>- Tell Excel to form a sheet for the regression results. To do this </li></ul><ul><li>Select New Worksheet Ply , and in the right box enter a name for sheet e.g.; Yahoo vs. S&P500 </li></ul><ul><li>-Press OK </li></ul>
  21. 21. REGRESSION ANALYSIS <ul><li>- Regression results will appear in the Yahoo vs. S&P500 sheet </li></ul><ul><li>- Look at the coefficients </li></ul><ul><li>Intercept is: </li></ul><ul><li>Slope is S&P500: </li></ul><ul><li>So, </li></ul><ul><li>Yahoo Return = ______ + _______*S&P500 return </li></ul>
  22. 22. REGRESSION ANALYSIS <ul><li>- Interpretation of Regression Outputs </li></ul><ul><ul><li>P-value: The probability of observing a co-efficient at least this magnitude if there were no relations between the dependent variable and the independent variables we observe. </li></ul></ul><ul><ul><li>Would a small p-value be a strong or weak evidence of a relation? </li></ul></ul><ul><ul><li>R-square: How much of the variation of the dependent variable is explained by the regression equation? </li></ul></ul><ul><ul><li>A larger R-square indicates better ability for the independent variables to explain the variations in Y. </li></ul></ul>
  23. 23. REGRESSION ANALYSIS <ul><li>Typically, there are two steps involved in regression forecast. </li></ul><ul><ul><li>Model estimation </li></ul></ul><ul><ul><li>Predictions with estimated parameters and new values of the independent variables </li></ul></ul>

×