Data
I got the data from famafrench
data library. Then merged it with our locally created market
returns data set (from MBA land). This generated the following two
JMP data sets:
Paper
Bob and I are working on a paper. A very very preliminary version is
here.
Regression of excess return based on which decile number
Y = excess return for each of 100 portfolios
Size= decile number of size (09)
B/E = book to equity ratio decile number (09)
This is the raw JMP output. It basically shows the same thing that
the orginal FamaFrench paper showed. Namely small stocks do
amazingly well. And high B/E ratio stocks to even better well.
Actual by Predicted Plot
Summary of Fit
RSquare  0.604354

RSquare Adj  0.596196

Root Mean Square Error  0.191787

Mean of Response  0.916007

Observations (or Sum Wgts)  100

Analysis of Variance
Source  DF  Sum of Squares  Mean Square  F Ratio


Model  2  5.4499712  2.72499  74.0843

Error  97  3.5678765  0.03678  Prob > F

C. Total  99  9.0178477   <.0001

Parameter Estimates
Term   Estimate  Std Error  t Ratio  Prob>t


Intercept   0.772248  0.046621  16.56  <.0001

size   0.039234  0.006677  5.88  <.0001

B/E ratio   0.0711808  0.006677  10.66  <.0001

Effect Tests
Source  Nparm  DF  Sum of Squares  F Ratio  Prob > F 


size  1  1  1.2699421  34.5260  <.0001 

B/E ratio  1  1  4.1800291  113.6426  <.0001 

size
Leverage Plot
B/E ratio
Leverage Plot
Confidence intervals for each of the 100 portfolios
Inspite of the impressive performance we got above, none of them seem
to beat chance by very much. The following graph gives +/ 2*SE for
what CAPM would predict each of hte 100 portfolios should have for
its return. Only about 10 are outside this bound. We would expect
that 5 would be outside these bounds. But since the tests aren't
independent, it isn't clear if this is impressive or not.
Distribution of the zstatistics for the 100 portfolios
CAPM says that any portfolio you create should have an intercept which
is exactly zero. The following shows the zstatistcs for alpha for
these 100 portfolios.
CAPM along with independence would suggest that this should look like
100 normal zeroone random variables. FamaFrench would suggest that
there are some serious outliers (namely those portfolios that are
either much better or much worse than expected.)
t(alpha)
Quantiles
100.0%  maximum  2.611

99.5%   2.611

97.5%   2.499

90.0%   2.030

75.0%  quartile  1.614

50.0%  median  0.763

25.0%  quartile  0.329

10.0%   1.147

2.5%   1.753

0.5%   2.306

0.0%  minimum  2.306

Moments
Mean  0.5850628

Std Dev  1.1989276

Std Err Mean  0.1198928

upper 95% Mean  0.822956

lower 95% Mean  0.3471695

N  100
