Regressions HSIEH HW 4 - Regressions Professor Hsieh...

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Risk Premium
Adjusted Return
Scatterplot of Adjusted Return vs Risk Premium
Results for: MCDONALDS_14.MTW
Regression Analysis: Adjusted Return versus Risk Premium
The regression equation is
Adjusted Return = 1.14 Risk Premium
Predictor Coef SE Coef T P
Noconstant
Risk Premium 1.1358 0.1500 7.57 0.000
S = 0.0421654
Analysis of Variance
Source DF SS MS F P
Regression 1 0.10194 0.10194 57.34 0.000
Residual Error 88 0.15646 0.00178
Total 89 0.25840
Unusual Observations
Risk Adjusted
Obs Premium Return Fit SE Fit Residual St Resid
10 0.069 -0.02337 0.07885 0.01041 -0.10222 -2.50RX
12 -0.055 0.01244 -0.06197 0.00818 0.07441 1.80 X
22 -0.095 -0.20356 -0.10758 0.01421 -0.09598 -2.42RX
23 -0.057 -0.07587 -0.06426 0.00849 -0.01160 -0.28 X
28 0.110 0.09868 0.12548 0.01657 -0.02680 -0.69 X
29 0.002 0.09187 0.00237 0.00031 0.08950 2.12R
39 0.092 0.19149 0.10491 0.01385 0.08658 2.17RX
66 -0.056 -0.06078 -0.06354 0.00839 0.00276 0.07 X
70 0.006 -0.07872 0.00712 0.00094 -0.08584 -2.04R
76 0.020 0.12789 0.02252 0.00297 0.10537 2.51R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
Regressions – Professor Hsieh
Homework Assignment 4
Part I.
2.
This plot suggests that there is some positive correlation between adjusted return and risk
premium. However, there are several outliers that appear to be influential. If we exclude these
points, the correlation is a lot less apparent, and looks a lot less significant.
3.

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Fitted Value
Standardized Residual
Versus Fits
(response is Adjusted Return)
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SRES1
Percent
Mean 0.1777
StDev 0.9963
N 89
AD 0.182
P-Value 0.911
Probability Plot of SRES1
Normal - 95% CI
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Index
COOK1
Time Series Plot of COOK1
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Index
HI1
Time Series Plot of HI 1
4. As justified by the attached hypothesis test, there appears to be a larger systematic risk
exposure in McDonald’s monthly stock return.
5.
In the versus fits graph, there appears to be four outliers that deviate visibly from the main
cluster of standardized residuals vs. fits. The outliers are data-points: 10, 22, 28, 39. The
standardized residuals appear to have no correlation and non-constant variance.