Financial Forecasting Models - Download as PowerPoint by isv11699

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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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