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Financial Forecasting Models - Download as PowerPoint

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					   Chapter 5:Financial Forecasting
          and Estimation
3 forecasting techniques discussed in the
   textbook:
       Percent of Sales Method, Trend function,
   and Regression function.
 Percent of Sales Method (Skip P140 – 148)
   – Forecasting the Income statement items
   – Items have the consistent relationship with sales
     over time.
 Trend Analysis
  – Linear Trend Extrapolation
Chapter 5
 Regression
     1. estimate a relationship among variables we
    are interested in. 2. Forecasting
     – Sales vs. interest rate
     – Stock return vs. market return
     – Spending vs. income
     – House prices vs. Attributes of a house
                    Chapter 5: Forecasting


 Where we find the Excel Regression Function??
  Excel 2003: TOOLS => Data Analysis => Regression
      If necessary, choose Add-ins and check on Analysis Toolpak


  Excel 2007: DATA => Analysis => Regression
      Click on the Ribbon and choose Excel Option and
      check Analysis Toolpak under Add-ins option.




  How to interpret the regression results??
 Regression

 Regression: fitting the best line to a data
  set
  1. Linear relationship between variables
  2. Forecasting

  There are two types of regression models
  depending the number of independent
  variables
Graph for regression line and
data
 For Excel 2007, use Scatter option in Charts
  under Insert Menu. For titles and names, use
  options under Layouts.

 For Excel 2003, use Wizard Chart and pick
  scatter diagram option. Use axis format for
  titles and axis names.
Regression Line
1. A simple regression

 There is only one independent variable.

 Y = a + bX, where Y is called an dependent variable
 and X is called an independent or explanatory variable

 Example: (1) Stock return = a + b Market return,
                b is stock beta
          (2) Sales = a + b interest rates
2. Multiple regression

 There are more than one variables.
  Y = a + bX1 +cX2 + dX3

 Example: Housing price appraisal.
  where Y is housing price and X's are
  characteristics of a house such as size,
  garage, pool, land size
More example
1. Wine consumption as a function of age
   and gender type

2. Baseball Salary Arbitration


Salary is a function of players’ records such
   as RBI, Homeruns, Steals, Hits, …….
Regression Equations
 Y = a + b*X in general
 IBM returns = 0.05 + 1.3*SP500returns
 Sales = 145 - 2300* Interest rates
 Wine Cons = 0.18 + 0.06*Age - 0.69Gender
 House prices = 65363 + 10802*landsize +
  18.9*Sqft + 4640*OpenPool + . . . . . . . .
Forecasting: An example
 Sales = 145 - 2300* Interest rates
   – This is the regression equation estimated given
     the data


 What is the sales units forecast when the
  interest rate is 5%?
   – Predicted sales = 145 - 2300*5% = 30 (units)

				
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