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Estimating the beta of Facebook, Inc
Estimating the beta of Facebook, Inc. P.A 1. From http://finance.yahoo.com download monthly prices for the most recent five years for Facebook, Inc. (FB). Select the tab Historical Prices, than Time Period: 5Y, Frequency: Monthly, and download to spreadsheet. 2. Follow the same procedure for the S&P500 ("GSPC). 3. Calculate monthly returns for Facebook and for the S&P500. Use the last column of the data you downloaded from Yahoo (Adj close*). P. + Div -P- Returns are calculate as: R Fortunately, Yahoo adjusts prices for dividends and stock splits (if you want to know how the adjustment is done download daily prices and then compare “close" prices with “adj close” prices). Therefore, we can simplify the calculation and calculate returns as follows: P.-P.- R P- 4. Estimate Beta using a simple regression. Regression analysis can be done in excel: the Y- variable is the column range for the stock returns and X-variable is the column range for the market returns (S&P 500). Beta is the coefficient of the X-variable. 5. Compare the value obtained with the value of Beta for Facebook reported in Yahoo Finance. Discuss the meaning of this number.
Expert Solution
Part I and II
The data are collected and the computations are done based on the adj close price for the stock .
Part III returns % calculation
The data collected for the stocks and the S& P 500 index ,
The Monthly returns are calculated based on the closing prices of the Adjusted close price
For FB
FB Returns = New month price –Old month price / Old months price
Excel formula
=(B4-B3)/B3
For S&P 500
SP 500 returns = New month price –Old month price / Old months price
Excel formula
=(C4-C3)/C3
The returns for the FB and S&P are as follows
Part IV
Regression Model
The regression model is run using the data analysis regression in excel
Data – Data analysis --- regression .
X variable = SP500
Y variable = FB stock return
The output of the regression are as follows
|
SUMMARY OUTPUT |
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|
Regression Statistics |
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|
Multiple R |
0.657211237 |
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|
R Square |
0.43192661 |
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|
Adjusted R Square |
0.422132241 |
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|
Standard Error |
0.06132532 |
|||||||
|
Observations |
60 |
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|
ANOVA |
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|
|
df |
SS |
MS |
F |
Significance F |
|||
|
Regression |
1 |
0.165849112 |
0.165849 |
44.09948 |
1.17E-08 |
|||
|
Residual |
58 |
0.2181261 |
0.003761 |
|||||
|
Total |
59 |
0.383975212 |
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|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Lower 95.0% |
Upper 95.0% |
|
Intercept |
0.006828395 |
0.008139736 |
0.838896 |
0.404972 |
-0.00947 |
0.023122 |
-0.00947 |
0.023122 |
|
S&p500 |
1.22077411 |
0.183830918 |
6.640744 |
1.17E-08 |
0.852797 |
1.588752 |
0.852797 |
1.588752 |
So from the Highlighted part in the regression output in the table we see that
Beta of FB is 1.22 as is shown in the regression output box
Part V
The regression model originally estimed by the regression model
Regression model
Y= a+ Beta X
or
FB return = 0.0068 + 1.22 S&P500 return %
Beta reported
Beta reported in the yahoo finance is 1.27
So the significance of the beta is as follows
· Beta signifies that the returns of FB are 1.22 times more volatile than that of the inde returns in S&P 500
· If the index moves my 1 unit , FB returns will move by 1.22 unit
· The beta estimated by the yahoo finance includes the settlement accuracy of the prices and are computed on a real-time basis . hence the value os a bit on higher side at 1.27
· Beta measures the market risk in the portfolio consisting of this asset class
please see the attached file.for the complete solution.
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