this presentation describes the case study " pedigree vs grit". Here in the case study, AMBTPM’S Larry Debolt is retiring and there are two possible candidates-Robert Bob Smith and Putney X Rockefeller for the position of Debolt. Regression analysis is performed to decide Who will be the best fit?
Pedigree Vs. Grit; Predicting Mutual Funds Performance
1. PEDIGREE VS. GRIT
PREDICTING MUTUAL FUNDS PERFORMANCE
Presented by
Reetam Ghosh (M1721)
Shaubhik Mondal (M1724)
Sayandeep Chandra (M1725)
Course: Security and Portfolio Management
2. 2
• AMBTPM’S Larry Debolt is retiring.
• Robert Bob Smith and Putney X Rockefeller are probable contestant for the
post.
• CEO Jack Beam a Kellogg MBA supports thirty two years Ms Rockefeller a
MBA from Princeton.
• Larry Debolt supports thirty five years Bob Smith a Ohio state graduate.
• Who will be the best fit?
Case briefing
• RET: The excess return of the fund in the year of the observation.
• GRI: Dummy variable related with fund.
• SAT: Score related with undergraduate degree.
• TEN: The tenure of the manager at the fund in whole number of years.
3. QUESTION 01
3
(a) Why do you disagree with Jack’s
comments about the uselessness of
the regression due to the low R-
squared?
(b) Can you think of a situation in
which a useless regression has a high R-
squared?
(c) There are techniques to determine the
validity of a regression model—in
particular, whether the relationship is
linear and the error terms display equal
variance (homoskedasticity). Does the
regression in Table 1 violate either of
these two assumptions? Justify your
answer.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.208203733
R Square 0.043348794
Adjusted R Square 0.034391386
Standard Error 8.354657572
Observations 540
4. HOMOSKEDASTICITY
4
homoskedasticity, which refers
to a situation where the error
has the same variance
regardless of the value(s)
taken by the independent
variable(s).
-40
-20
0
20
40
0 0.5 1 1.5
GRI
Y
Predicted Y
Linear (Y)
Linear (Predicted Y)
-40
-20
0
20
40
0 500 1000 1500
Y
X Variable 2
SAT
Y
Predicted Y
Linear (Y)
Linear (Predicted Y)
-40
-20
0
20
40
0 50 100
Y
X Variable 4
AGE
Y
Predicted Y
Linear (Y)
Linear (Predicted Y)
-40
-20
0
20
40
0 10 20 30 40
Y
X Variable 5
TEN
Y
Predicted Y
Linear (Y)
Linear (Predicted Y)
-40
-20
0
20
40
0 0.5 1 1.5
Y
X Variable 3
MBA
Y
Predicted Y
Linear (Y)
Homoskedasticity seems to be absent in this case.
5. QUESTION 02
5
(a) Estimate the excess return
(RET) of the funds that
Bob and Putney currently
manage. Assume that
Princeton’s average
composite SAT score is
1355, while Ohio State’s
is 1042. Between Bob and
Putney, who is expected
to obtain higher returns at
their current funds and by
how much?
(b) Between Bob and Putney,
who is expected to obtain
higher returns if hired by
AMBTPM and by how
much?
a) Between Bob and Putney, Putney is expected to obtain higher returns.
MBA degree has a significance of 81.139% that is higher than the other
variables
Coefficient GRI MBA
Dummy variables =1; if fund is growth &
income
=0; else
=1; if have MBA degree
=0; else
COEFFICIENT RET GRI SAT MBA AGE TEN
-2.110460859 0.005734797 -0.180646966 -0.06889255 -0.11872167
BOB -1.781808925 1 1042 0 35 5
PUTNEY 0.395378312 1 1355 1 32 2
6. QUESTION 02
6
(a) Estimate the excess return
(RET) of the funds that
Bob and Putney currently
manage. Assume that
Princeton’s average
composite SAT score is
1355, while Ohio State’s
is 1042. Between Bob and
Putney, who is expected
to obtain higher returns at
their current funds and by
how much?
(b) Between Bob and Putney,
who is expected to obtain
higher returns if hired by
AMBTPM and by how
much?
b) RET at 0 Tenure,
Thus Putney will always get higher returns than Bob at AMBTPM
COEFFICIENT RET GRI SAT MBA AGE TEN
-2.110460859 0.005734797 -0.180646966 -0.06889255 -0.11872167
BOB -1.188200575 1 1042 0 35 0
PUTNEY 0.632821652 1 1355 1 32 0
7. QUESTION 03
7
(a) Can you prove at the 5
percent significance level
that if Bob had attended
Princeton instead of Ohio
State, then the return of
his current fund would be
greater?
(b) Can you prove at the 10
percent level of
significance that if Bob
were managing a growth
fund instead of a growth
and income fund, then he
would achieve at least 1
percent higher average
returns?
Coefficients Standard Error t Stat P-value
Intercept -2.642159211 3.346530759 -0.789521867 0.430157473
GRI -2.110460859 0.738857893 -2.85638264 0.004451873
SAT 0.005734797 0.002659567 2.156289466 0.031506887
MBA -0.180646966 0.756643724 -0.238747723 0.811392803
AGE -0.06889255 0.041817798 -1.647445675 0.100054737
TEN -0.11872167 0.083502131 -1.421780125 0.155673864
coefficient of SAT is positive (0.005735) and since it is positively
correlated, attending Princeton instead of Ohio State will
increase the return of Bob.
Coefficients Standard Error t Stat P-value
Intercept -2.64215921 3.346530759 -0.78952 0.430157
GRI -2.11046086 0.738857893 -2.85638 0.004452
SAT 0.0057348 0.002659567 2.156289 0.031507
MBA -0.18064697 0.756643724 -0.23875 0.811393
AGE -0.06889255 0.041817798 -1.64745 0.100055
TEN -0.11872167 0.083502131 -1.42178 0.155674
The coefficient of GRI (-2.11046) is negatively correlated with the return. Thus if he
was managing growth fund, he would have achieved a higher return.
COEFFICIENT RET GRI SAT MBA AGE TEN
-2.110460859 0.005734797 -0.180646966 -0.06889255 -0.11872167
BOB 0.328651934 0 1042 0 35 5
PUTNEY 0.395378312 1 1355 1 32 2
8. QUESTION 04
8
(a) Does the regression in Table
1 provide strong evidence
for the claim that fund
managers with MBAs
perform worse than
managers without MBAs?
What is being held constant
in this comparison?
(b) It has been suggested that
fund managers without
MBAs get higher expected
returns because they invest
in riskier stocks. If this were
true, what effect would
including an independent
variable, Beta (with higher
values corresponding to
higher levels of systematic
risk in the fund’s portfolio),
have on the coefficient of
MBA?
Coefficients Standard Error t Stat P-value
Intercept -2.642159211 3.346530759 -0.789521867 0.430157473
GRI -2.110460859 0.738857893 -2.85638264 0.004451873
SAT 0.005734797 0.002659567 2.156289466 0.031506887
MBA -0.180646966 0.756643724 -0.238747723 0.811392803
AGE -0.06889255 0.041817798 -1.647445675 0.100054737
TEN -0.11872167 0.083502131 -1.421780125 0.155673864
MBA is negative and p value is 81.139%. But there is no other evidence
which shows that a person with MBA will perform better than someone
without a MBA.RET = Intercept + Coefficient of MBA = 2.642159 + (-
0.180647) = -2.822806. considered GRI, SAT, AGE & TEN const.
GRI SAT MBA AGE TEN
-0.1214 0.0937 -0.0103 -0.0801 -0.0691
BETA WEIGHT FROM TABLE 1 IN THE CASE
9. QUESTION 05
9
(a) What is the lowest level of
significance at which you
can prove that the manager’s
age has a negative impact on
his or her fund’s
performance holding the
type of the fund, the
manager’s education, and
years of experience at the
fund constant?
Keeping the factors such as education, tenure, age constant,
the lowest significance level at which the manager’s age has a
negative impact on fund’s performance is 99.9%
Coefficients StandardError tStat P-value Lower95% Upper95% Lower1.0% Upper1.0%
Intercept 3.964403018 1.657421064 2.391910604 0.017103249 0.708593 7.220213075 3.943620125 3.985185911
AGE -0.101994141 0.036811336 -2.770726406 0.005786565 -0.17431 -0.029682571 -0.102455729 -0.101532552
Coefficients StandardError tStat P-value Lower95% Upper95% Lower0.01% Upper0.01%
Intercept 3.964403018 1.657421064 2.391910604 0.017103249 0.708593 7.220213075 3.964195194 3.964610841
AGE -0.101994141 0.036811336 -2.770726406 0.005786565 -0.17431 -0.029682571 -0.101998756 -0.101989525
10. QUESTION 05
10
(b) A survivorship bias is
thought to be present in
analysing fund manager
performance in which a younger
manager’s survival in the
industry is more closely linked
to his/her performance than an
older manager’s survival. In
other words, if a new manager
does not perform successfully,
he or she is not tolerated in the
industry for long, but a more
experienced manager may be
forgiven a year or two of poor
performance. Would the
presence of this survivorship
bias dampen or exacerbate the
effect seen in Part (a)?
RET has negative correlation with AGE, positive correlation with
AGE^2 and negative correlation with AGE^3.
RET will be low for young managers, higher for old managers
and it will slightly decrease for very old managers.
Coefficients Standard Error t Stat P-value
Intercept 78.06756169 21.87786301 3.568335795 0.000392
GRI -1.96854841 0.730371956 -2.695268348 0.007256
SAT 0.004854417 0.00262494 1.849343759 0.064963
MBA -0.51959299 0.750285908 -0.692526655 0.488909
AGE -5.49434276 1.39472459 -3.939374699 9.26E-05
TEN -0.09016561 0.083576584 -1.078838126 0.281149
AGE^2 0.117909946 0.029090646 4.053191087 5.81E-05
AGE^3 -0.00081885 0.000195166 -4.195644787 3.19E-05
11. QUESTION 06
11
(a) “Streamline” the
regression given in Table
1, that is, eliminate all
variables that are not
significant at the 15
percent level. Write down
the new regression
equation and check
whether the specification
satisfies the assumptions
of linearity and
homoscedasticity.
(b) Compare the coefficient
of AGE in the new and
the old regressions. What
can explain the sign
(direction) of the change
in this estimator? Discuss.
Coefficients Standard Error t Stat P-value
Intercept -2.642159211 3.346530759 -0.789521867 0.430157473
GRI -2.110460859 0.738857893 -2.85638264 0.004451873
SAT 0.005734797 0.002659567 2.156289466 0.031506887
MBA -0.180646966 0.756643724 -0.238747723 0.811392803
AGE -0.06889255 0.041817798 -1.647445675 0.100054737
TEN -0.11872167 0.083502131 -1.421780125 0.155673864
CoefficientsStandard Error t Stat P-value
Intercept -2.58392 3.34035 -0.77355 0.439539
GRI -2.11101 0.73858 -2.8582 0.004426
SAT 0.006242 0.002593 2.406971 0.016423
AGE -0.09596 0.036555 -2.62505 0.008911