Univariate Statistics Worksheet

Part 1. Univariate Statistics.
Please go to Professor Kenneth French’s data library website and obtained monthly returns data on the
“Fama/French 3 Factors” and the risk free rate for the period from July 1963-December 2017 (654
months):
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
(Double click on the window to access the data on the excel spreadsheet)
DATE
Mkt-RF
SMB
HML
RF
196307
-0.39
-0.46
-0.81
196308
5.07
-0.81
1.65
196309
-1.57
-0.48
0.19
196310
2.53
-1.29
-0.09
196311
-0.85
-0.85
1.71
196312
1.83
-1.87
-0.1
196401
2.24
0.09
1.61
196402
1.54
0.3
2.82
•
•
•
0.27
0.25
0.27
0.29
0.27
0.29
0.3
0.26
RM-RF The return spread between the capitalization weighted stock market and “cash”.
SMB The return spread of small minus large stocks (i.e., the size effect).
HML The return spread of cheap minus expensive stocks (i.e., the value effect).
1. Split the sample in 3 equal periods and compute the average, SD, skew, and kurtosis for each
of the three “risk factors” for the full sample and the three different periods. Arrange these
values in a table similar to the one shown below.
Full Sample: 1963M07 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
First sub-sample: 1963M07 – 1981M08
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Second sub-sample: 1981M09 – 1999M10
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Third sub-sample: 1999M11 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
2. Do the statistics suggest to you that returns for those risk factors come from the same
distribution over the entire period?
3. Make a plot showing the growth of $1 in each of the three “risk factors (portfolios)” over the
full sample. (Recall, this is called an “equity curve”).
4. Which factor portfolio gives the lowest and highest future value (full sample)?
Part 2.
Go to Yahoo! finance site. Please download monthly adj. close prices from 12/1/2012 to 12/1/2017 for
S&P 500 index (^SP500TR) and the following funds:
•
•
•
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
1. Compute univariate descriptive statistics (mean, variance, standard deviation, skewness,
kurtosis) for each return series and comment.
a. Which funds have the highest and lowest average return?
b. Which funds have the highest and lowest standard deviation?
c. Which funds look most and least normally distributed?
2. Using a monthly risk free rate equal to 0.04167% per month (which corresponds to a
continuously compounded annual rate of 0.5%), compute Sharpe’s slope/ratio for each fund.
Arrange these values in a table from highest to lowest. Which asset has the highest Sharpe
ratios?
3. Compute and plot all pair-wise scatterplots between these 3 funds. Briefly comment on any
relationships you see.
4. Compute the sample covariance matrix of the returns on these 3 funds and comment on the
direction of linear association between the asset returns.
5. Compute the sample correlation matrix of the returns on these 3 funds.
a. Which funds are most highly correlated?
b. Which are least correlated?
c. Based on the estimated correlation values do you think diversification will reduce risk
with these assets?
Part 3. Estimating expected returns
In this section, you need to use information from Part 1 and Part 2.
Use the CAPM to estimate the expected returns of each of the funds from part 2:
o
o
o
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖,𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖,𝑡
𝑒
To simplify notation in the regression notice that 𝑅𝑖,𝑡
= 𝑅𝑖,𝑡 − 𝑅𝐹,𝑡 = is stock or portfolio 𝑖 𝑡ℎ excess return
𝑒
and 𝑅𝑀𝐾𝑇
= 𝑅𝑀𝑡 − 𝑅𝐹,𝑡 = is the excess return on a “stock market portfolio”
In order to do this follow three simple steps.
1. Step 1. Estimate the risk premia for each factor
𝑇
1
𝜆𝑀𝐾𝑇 = ∑(𝑅𝑀𝑡 − 𝑅𝐹,𝑡 )
𝑇
𝑡=1
𝒕𝒉
2. Step 2. Estimate the sensitivities of the 𝒊
stock to each of those factors.
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖𝑡
3. Step 3. The expected returns can be calculated by combining the results of the previous steps.
𝐸(𝑅𝑖𝑒 ) = 𝛽̂𝑖,𝑀 𝜆𝑀𝐾𝑇
4. Which fund has the highest and lowest expected return?
5. Compare the factor betas and provide some comparisons between the two funds.
Part 1. Univariate Statistics.
Please go to Professor Kenneth French’s data library website and obtained monthly returns data on the
“Fama/French 3 Factors” and the risk free rate for the period from July 1963-December 2017 (654
months):
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
(Double click on the window to access the data on the excel spreadsheet)
DATE
Mkt-RF
SMB
HML
RF
196307
-0.39
-0.46
-0.81
196308
5.07
-0.81
1.65
196309
-1.57
-0.48
0.19
196310
2.53
-1.29
-0.09
196311
-0.85
-0.85
1.71
196312
1.83
-1.87
-0.1
196401
2.24
0.09
1.61
196402
1.54
0.3
2.82
•
•
•
0.27
0.25
0.27
0.29
0.27
0.29
0.3
0.26
RM-RF The return spread between the capitalization weighted stock market and “cash”.
SMB The return spread of small minus large stocks (i.e., the size effect).
HML The return spread of cheap minus expensive stocks (i.e., the value effect).
1. Split the sample in 3 equal periods and compute the average, SD, skew, and kurtosis for each
of the three “risk factors” for the full sample and the three different periods. Arrange these
values in a table similar to the one shown below.
Full Sample: 1963M07 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
First sub-sample: 1963M07 – 1981M08
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Second sub-sample: 1981M09 – 1999M10
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Third sub-sample: 1999M11 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
2. Do the statistics suggest to you that returns for those risk factors come from the same
distribution over the entire period?
3. Make a plot showing the growth of $1 in each of the three “risk factors (portfolios)” over the
full sample. (Recall, this is called an “equity curve”).
4. Which factor portfolio gives the lowest and highest future value (full sample)?
Part 2.
Go to Yahoo! finance site. Please download monthly adj. close prices from 12/1/2012 to 12/1/2017 for
S&P 500 index (^SP500TR) and the following funds:
•
•
•
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
1. Compute univariate descriptive statistics (mean, variance, standard deviation, skewness,
kurtosis) for each return series and comment.
a. Which funds have the highest and lowest average return?
b. Which funds have the highest and lowest standard deviation?
c. Which funds look most and least normally distributed?
2. Using a monthly risk free rate equal to 0.04167% per month (which corresponds to a
continuously compounded annual rate of 0.5%), compute Sharpe’s slope/ratio for each fund.
Arrange these values in a table from highest to lowest. Which asset has the highest Sharpe
ratios?
3. Compute and plot all pair-wise scatterplots between these 3 funds. Briefly comment on any
relationships you see.
4. Compute the sample covariance matrix of the returns on these 3 funds and comment on the
direction of linear association between the asset returns.
5. Compute the sample correlation matrix of the returns on these 3 funds.
a. Which funds are most highly correlated?
b. Which are least correlated?
c. Based on the estimated correlation values do you think diversification will reduce risk
with these assets?
Part 3. Estimating expected returns
In this section, you need to use information from Part 1 and Part 2.
Use the CAPM to estimate the expected returns of each of the funds from part 2:
o
o
o
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖,𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖,𝑡
𝑒
To simplify notation in the regression notice that 𝑅𝑖,𝑡
= 𝑅𝑖,𝑡 − 𝑅𝐹,𝑡 = is stock or portfolio 𝑖 𝑡ℎ excess return
𝑒
and 𝑅𝑀𝐾𝑇
= 𝑅𝑀𝑡 − 𝑅𝐹,𝑡 = is the excess return on a “stock market portfolio”
In order to do this follow three simple steps.
1. Step 1. Estimate the risk premia for each factor
𝑇
1
𝜆𝑀𝐾𝑇 = ∑(𝑅𝑀𝑡 − 𝑅𝐹,𝑡 )
𝑇
𝑡=1
𝒕𝒉
2. Step 2. Estimate the sensitivities of the 𝒊
stock to each of those factors.
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖𝑡
3. Step 3. The expected returns can be calculated by combining the results of the previous steps.
𝐸(𝑅𝑖𝑒 ) = 𝛽̂𝑖,𝑀 𝜆𝑀𝐾𝑇
4. Which fund has the highest and lowest expected return?
5. Compare the factor betas and provide some comparisons between the two funds.
Part 1. Univariate Statistics.
Please go to Professor Kenneth French’s data library website and obtained monthly returns data on the
“Fama/French 3 Factors” and the risk free rate for the period from July 1963-December 2017 (654
months):
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
(Double click on the window to access the data on the excel spreadsheet)
DATE
Mkt-RF
SMB
HML
RF
196307
-0.39
-0.46
-0.81
196308
5.07
-0.81
1.65
196309
-1.57
-0.48
0.19
196310
2.53
-1.29
-0.09
196311
-0.85
-0.85
1.71
196312
1.83
-1.87
-0.1
196401
2.24
0.09
1.61
196402
1.54
0.3
2.82
•
•
•
0.27
0.25
0.27
0.29
0.27
0.29
0.3
0.26
RM-RF The return spread between the capitalization weighted stock market and “cash”.
SMB The return spread of small minus large stocks (i.e., the size effect).
HML The return spread of cheap minus expensive stocks (i.e., the value effect).
1. Split the sample in 3 equal periods and compute the average, SD, skew, and kurtosis for each
of the three “risk factors” for the full sample and the three different periods. Arrange these
values in a table similar to the one shown below.
Full Sample: 1963M07 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
First sub-sample: 1963M07 – 1981M08
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Second sub-sample: 1981M09 – 1999M10
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Third sub-sample: 1999M11 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
2. Do the statistics suggest to you that returns for those risk factors come from the same
distribution over the entire period?
3. Make a plot showing the growth of $1 in each of the three “risk factors (portfolios)” over the
full sample. (Recall, this is called an “equity curve”).
4. Which factor portfolio gives the lowest and highest future value (full sample)?
Part 2.
Go to Yahoo! finance site. Please download monthly adj. close prices from 12/1/2012 to 12/1/2017 for
S&P 500 index (^SP500TR) and the following funds:
•
•
•
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
1. Compute univariate descriptive statistics (mean, variance, standard deviation, skewness,
kurtosis) for each return series and comment.
a. Which funds have the highest and lowest average return?
b. Which funds have the highest and lowest standard deviation?
c. Which funds look most and least normally distributed?
2. Using a monthly risk free rate equal to 0.04167% per month (which corresponds to a
continuously compounded annual rate of 0.5%), compute Sharpe’s slope/ratio for each fund.
Arrange these values in a table from highest to lowest. Which asset has the highest Sharpe
ratios?
3. Compute and plot all pair-wise scatterplots between these 3 funds. Briefly comment on any
relationships you see.
4. Compute the sample covariance matrix of the returns on these 3 funds and comment on the
direction of linear association between the asset returns.
5. Compute the sample correlation matrix of the returns on these 3 funds.
a. Which funds are most highly correlated?
b. Which are least correlated?
c. Based on the estimated correlation values do you think diversification will reduce risk
with these assets?
Part 3. Estimating expected returns
In this section, you need to use information from Part 1 and Part 2.
Use the CAPM to estimate the expected returns of each of the funds from part 2:
o
o
o
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖,𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖,𝑡
𝑒
To simplify notation in the regression notice that 𝑅𝑖,𝑡
= 𝑅𝑖,𝑡 − 𝑅𝐹,𝑡 = is stock or portfolio 𝑖 𝑡ℎ excess return
𝑒
and 𝑅𝑀𝐾𝑇
= 𝑅𝑀𝑡 − 𝑅𝐹,𝑡 = is the excess return on a “stock market portfolio”
In order to do this follow three simple steps.
1. Step 1. Estimate the risk premia for each factor
𝑇
1
𝜆𝑀𝐾𝑇 = ∑(𝑅𝑀𝑡 − 𝑅𝐹,𝑡 )
𝑇
𝑡=1
𝒕𝒉
2. Step 2. Estimate the sensitivities of the 𝒊
stock to each of those factors.
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖𝑡
3. Step 3. The expected returns can be calculated by combining the results of the previous steps.
𝐸(𝑅𝑖𝑒 ) = 𝛽̂𝑖,𝑀 𝜆𝑀𝐾𝑇
4. Which fund has the highest and lowest expected return?
5. Compare the factor betas and provide some comparisons between the two funds.
Part 1. Univariate Statistics.
Please go to Professor Kenneth French’s data library website and obtained monthly returns data on the
“Fama/French 3 Factors” and the risk free rate for the period from July 1963-December 2017 (654
months):
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
(Double click on the window to access the data on the excel spreadsheet)
DATE
Mkt-RF
SMB
HML
RF
196307
-0.39
-0.46
-0.81
196308
5.07
-0.81
1.65
196309
-1.57
-0.48
0.19
196310
2.53
-1.29
-0.09
196311
-0.85
-0.85
1.71
196312
1.83
-1.87
-0.1
196401
2.24
0.09
1.61
196402
1.54
0.3
2.82
•
•
•
0.27
0.25
0.27
0.29
0.27
0.29
0.3
0.26
RM-RF The return spread between the capitalization weighted stock market and “cash”.
SMB The return spread of small minus large stocks (i.e., the size effect).
HML The return spread of cheap minus expensive stocks (i.e., the value effect).
1. Split the sample in 3 equal periods and compute the average, SD, skew, and kurtosis for each
of the three “risk factors” for the full sample and the three different periods. Arrange these
values in a table similar to the one shown below.
Full Sample: 1963M07 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
First sub-sample: 1963M07 – 1981M08
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Second sub-sample: 1981M09 – 1999M10
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Third sub-sample: 1999M11 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
2. Do the statistics suggest to you that returns for those risk factors come from the same
distribution over the entire period?
3. Make a plot showing the growth of $1 in each of the three “risk factors (portfolios)” over the
full sample. (Recall, this is called an “equity curve”).
4. Which factor portfolio gives the lowest and highest future value (full sample)?
Part 2.
Go to Yahoo! finance site. Please download monthly adj. close prices from 12/1/2012 to 12/1/2017 for
S&P 500 index (^SP500TR) and the following funds:
•
•
•
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
1. Compute univariate descriptive statistics (mean, variance, standard deviation, skewness,
kurtosis) for each return series and comment.
a. Which funds have the highest and lowest average return?
b. Which funds have the highest and lowest standard deviation?
c. Which funds look most and least normally distributed?
2. Using a monthly risk free rate equal to 0.04167% per month (which corresponds to a
continuously compounded annual rate of 0.5%), compute Sharpe’s slope/ratio for each fund.
Arrange these values in a table from highest to lowest. Which asset has the highest Sharpe
ratios?
3. Compute and plot all pair-wise scatterplots between these 3 funds. Briefly comment on any
relationships you see.
4. Compute the sample covariance matrix of the returns on these 3 funds and comment on the
direction of linear association between the asset returns.
5. Compute the sample correlation matrix of the returns on these 3 funds.
a. Which funds are most highly correlated?
b. Which are least correlated?
c. Based on the estimated correlation values do you think diversification will reduce risk
with these assets?
Part 3. Estimating expected returns
In this section, you need to use information from Part 1 and Part 2.
Use the CAPM to estimate the expected returns of each of the funds from part 2:
o
o
o
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖,𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖,𝑡
𝑒
To simplify notation in the regression notice that 𝑅𝑖,𝑡
= 𝑅𝑖,𝑡 − 𝑅𝐹,𝑡 = is stock or portfolio 𝑖 𝑡ℎ excess return
𝑒
and 𝑅𝑀𝐾𝑇
= 𝑅𝑀𝑡 − 𝑅𝐹,𝑡 = is the excess return on a “stock market portfolio”
In order to do this follow three simple steps.
1. Step 1. Estimate the risk premia for each factor
𝑇
1
𝜆𝑀𝐾𝑇 = ∑(𝑅𝑀𝑡 − 𝑅𝐹,𝑡 )
𝑇
𝑡=1
𝒕𝒉
2. Step 2. Estimate the sensitivities of the 𝒊
stock to each of those factors.
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖𝑡
3. Step 3. The expected returns can be calculated by combining the results of the previous steps.
𝐸(𝑅𝑖𝑒 ) = 𝛽̂𝑖,𝑀 𝜆𝑀𝐾𝑇
4. Which fund has the highest and lowest expected return?
5. Compare the factor betas and provide some comparisons between the two funds.
Part 1. Univariate Statistics.
Please go to Professor Kenneth French’s data library website and obtained monthly returns data on the
“Fama/French 3 Factors” and the risk free rate for the period from July 1963-December 2017 (654
months):
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
(Double click on the window to access the data on the excel spreadsheet)
DATE
Mkt-RF
SMB
HML
RF
196307
-0.39
-0.46
-0.81
196308
5.07
-0.81
1.65
196309
-1.57
-0.48
0.19
196310
2.53
-1.29
-0.09
196311
-0.85
-0.85
1.71
196312
1.83
-1.87
-0.1
196401
2.24
0.09
1.61
196402
1.54
0.3
2.82
•
•
•
0.27
0.25
0.27
0.29
0.27
0.29
0.3
0.26
RM-RF The return spread between the capitalization weighted stock market and “cash”.
SMB The return spread of small minus large stocks (i.e., the size effect).
HML The return spread of cheap minus expensive stocks (i.e., the value effect).
1. Split the sample in 3 equal periods and compute the average, SD, skew, and kurtosis for each
of the three “risk factors” for the full sample and the three different periods. Arrange these
values in a table similar to the one shown below.
Full Sample: 1963M07 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
First sub-sample: 1963M07 – 1981M08
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Second sub-sample: 1981M09 – 1999M10
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Third sub-sample: 1999M11 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
2. Do the statistics suggest to you that returns for those risk factors come from the same
distribution over the entire period?
3. Make a plot showing the growth of $1 in each of the three “risk factors (portfolios)” over the
full sample. (Recall, this is called an “equity curve”).
4. Which factor portfolio gives the lowest and highest future value (full sample)?
Part 2.
Go to Yahoo! finance site. Please download monthly adj. close prices from 12/1/2012 to 12/1/2017 for
S&P 500 index (^SP500TR) and the following funds:
•
•
•
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
1. Compute univariate descriptive statistics (mean, variance, standard deviation, skewness,
kurtosis) for each return series and comment.
a. Which funds have the highest and lowest average return?
b. Which funds have the highest and lowest standard deviation?
c. Which funds look most and least normally distributed?
2. Using a monthly risk free rate equal to 0.04167% per month (which corresponds to a
continuously compounded annual rate of 0.5%), compute Sharpe’s slope/ratio for each fund.
Arrange these values in a table from highest to lowest. Which asset has the highest Sharpe
ratios?
3. Compute and plot all pair-wise scatterplots between these 3 funds. Briefly comment on any
relationships you see.
4. Compute the sample covariance matrix of the returns on these 3 funds and comment on the
direction of linear association between the asset returns.
5. Compute the sample correlation matrix of the returns on these 3 funds.
a. Which funds are most highly correlated?
b. Which are least correlated?
c. Based on the estimated correlation values do you think diversification will reduce risk
with these assets?
Part 3. Estimating expected returns
In this section, you need to use information from Part 1 and Part 2.
Use the CAPM to estimate the expected returns of each of the funds from part 2:
o
o
o
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖,𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖,𝑡
𝑒
To simplify notation in the regression notice that 𝑅𝑖,𝑡
= 𝑅𝑖,𝑡 − 𝑅𝐹,𝑡 = is stock or portfolio 𝑖 𝑡ℎ excess return
𝑒
and 𝑅𝑀𝐾𝑇
= 𝑅𝑀𝑡 − 𝑅𝐹,𝑡 = is the excess return on a “stock market portfolio”
In order to do this follow three simple steps.
1. Step 1. Estimate the risk premia for each factor
𝑇
1
𝜆𝑀𝐾𝑇 = ∑(𝑅𝑀𝑡 − 𝑅𝐹,𝑡 )
𝑇
𝑡=1
𝒕𝒉
2. Step 2. Estimate the sensitivities of the 𝒊
stock to each of those factors.
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖𝑡
3. Step 3. The expected returns can be calculated by combining the results of the previous steps.
𝐸(𝑅𝑖𝑒 ) = 𝛽̂𝑖,𝑀 𝜆𝑀𝐾𝑇
4. Which fund has the highest and lowest expected return?
5. Compare the factor betas and provide some comparisons between the two funds.
Part 1. Univariate Statistics.
Please go to Professor Kenneth French’s data library website and obtained monthly returns data on the
“Fama/French 3 Factors” and the risk free rate for the period from July 1963-December 2017 (654
months):
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
(Double click on the window to access the data on the excel spreadsheet)
DATE
Mkt-RF
SMB
HML
RF
196307
-0.39
-0.46
-0.81
196308
5.07
-0.81
1.65
196309
-1.57
-0.48
0.19
196310
2.53
-1.29
-0.09
196311
-0.85
-0.85
1.71
196312
1.83
-1.87
-0.1
196401
2.24
0.09
1.61
196402
1.54
0.3
2.82
•
•
•
0.27
0.25
0.27
0.29
0.27
0.29
0.3
0.26
RM-RF The return spread between the capitalization weighted stock market and “cash”.
SMB The return spread of small minus large stocks (i.e., the size effect).
HML The return spread of cheap minus expensive stocks (i.e., the value effect).
1. Split the sample in 3 equal periods and compute the average, SD, skew, and kurtosis for each
of the three “risk factors” for the full sample and the three different periods. Arrange these
values in a table similar to the one shown below.
Full Sample: 1963M07 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
First sub-sample: 1963M07 – 1981M08
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Second sub-sample: 1981M09 – 1999M10
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Third sub-sample: 1999M11 – 2017M12
MKT_RF
SMB
HML
Mean
Std. Dev.
Skewness
Kurtosis
Observations
2. Do the statistics suggest to you that returns for those risk factors come from the same
distribution over the entire period?
3. Make a plot showing the growth of $1 in each of the three “risk factors (portfolios)” over the
full sample. (Recall, this is called an “equity curve”).
4. Which factor portfolio gives the lowest and highest future value (full sample)?
Part 2.
Go to Yahoo! finance site. Please download monthly adj. close prices from 12/1/2012 to 12/1/2017 for
S&P 500 index (^SP500TR) and the following funds:
•
•
•
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
1. Compute univariate descriptive statistics (mean, variance, standard deviation, skewness,
kurtosis) for each return series and comment.
a. Which funds have the highest and lowest average return?
b. Which funds have the highest and lowest standard deviation?
c. Which funds look most and least normally distributed?
2. Using a monthly risk free rate equal to 0.04167% per month (which corresponds to a
continuously compounded annual rate of 0.5%), compute Sharpe’s slope/ratio for each fund.
Arrange these values in a table from highest to lowest. Which asset has the highest Sharpe
ratios?
3. Compute and plot all pair-wise scatterplots between these 3 funds. Briefly comment on any
relationships you see.
4. Compute the sample covariance matrix of the returns on these 3 funds and comment on the
direction of linear association between the asset returns.
5. Compute the sample correlation matrix of the returns on these 3 funds.
a. Which funds are most highly correlated?
b. Which are least correlated?
c. Based on the estimated correlation values do you think diversification will reduce risk
with these assets?
Part 3. Estimating expected returns
In this section, you need to use information from Part 1 and Part 2.
Use the CAPM to estimate the expected returns of each of the funds from part 2:
o
o
o
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖,𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖,𝑡
𝑒
To simplify notation in the regression notice that 𝑅𝑖,𝑡
= 𝑅𝑖,𝑡 − 𝑅𝐹,𝑡 = is stock or portfolio 𝑖 𝑡ℎ excess return
𝑒
and 𝑅𝑀𝐾𝑇
= 𝑅𝑀𝑡 − 𝑅𝐹,𝑡 = is the excess return on a “stock market portfolio”
In order to do this follow three simple steps.
1. Step 1. Estimate the risk premia for each factor
𝑇
1
𝜆𝑀𝐾𝑇 = ∑(𝑅𝑀𝑡 − 𝑅𝐹,𝑡 )
𝑇
𝑡=1
𝒕𝒉
2. Step 2. Estimate the sensitivities of the 𝒊
stock to each of those factors.
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖𝑀 𝑅𝑀𝐾𝑇
+ 𝑒𝑖𝑡
3. Step 3. The expected returns can be calculated by combining the results of the previous steps.
𝐸(𝑅𝑖𝑒 ) = 𝛽̂𝑖,𝑀 𝜆𝑀𝐾𝑇
4. Which fund has the highest and lowest expected return?
5. Compare the factor betas and provide some comparisons between the two funds.
AD685 – Term Paper Project
Last Name, First Name:
ID#:
Part 1. Univariate Statistics.
The data below was obtained from Professor Kenneth French’s data library website:
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
The table below contains monthly returns of the “Fama/French 5 Factors” and the monthly
returns of the “Momentum factor” for the period from July 1963-December 2017 (654 months)
(Double click on the window to access the data on the excel spreadsheet)
DATE
Mkt-RF
SMB
HML
RMW
CMA
RF
196307
-0.39
-0.46
-0.81
0.72
-1.16
196308
5.07
-0.81
1.65
0.42
-0.4
196309
-1.57
-0.48
0.19
-0.8
0.23
196310
2.53
-1.29
-0.09
2.75
-2.26
196311
-0.85
-0.85
1.71
-0.34
2.22
196312
1.83
-1.87
-0.1
0.18
-0.31
196401
2.24
0.09
1.61
0.21
1.51
196402
1.54
0.3
2.82
0.04
0.87
•
•
•
•
•
•
MOM
0.27
0.25
0.27
0.29
0.27
0.29
0.3
0.26
0.99
1.08
0.13
3.14
-0.75
1.7
1.06
0.25
RM-RF The return spread between the capitalization weighted stock market and cash.
SMB The return spread of small minus large stocks (i.e., the size effect).
HML The return spread of cheap minus expensive stocks (i.e., the value effect).
RMW The return spread of the most profitable firms minus the least profitable.
CMA The return spread of firms that invest conservatively minus aggressively.
MOM The retun spread of firms with high prior return minus low prior return.
1. Split the sample in 3 equal periods and compute the average, SD, skew, and kurtosis
for each of the six “risk factors” for the full sample and the three different periods.
Arrange these values in a table similar to the one shown below. (5p)
Mean
Std. Dev.
Skewness
Kurtosis
MKT_RF
0.5309633
Full Sample: 1963M07 – 2017M12
SMB
HML
RMW
CMA
0.2507798 0.346881 0.26494 0.28752294
4.38802378 3.0266613 2.810079
MOM
0.659097859
2.18563
2.00418375
4.194893265
-0.542491
0.3810074 0.070495 -0.31576
0.298299
-1.340418209
2.0482132
3.2625745 2.123699
1.65572176
10.73985204
12.2462
Observations
654
654
654
654
654
654
First sub-sample: 1963M07 – 1981M08
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Mean
Std. Dev.
Skewness
Kurtosis
Observations
Mean
Std. Dev.
Skewness
Kurtosis
Observations
0.2175688
0.5683945 0.440092
0.04197
0.25912844
0.844678899
4.4186913
3.2630351 2.605672
1.63156
2.02393709
3.699401139
-0.107685
0.2063334 -0.17502
0.07447
0.0278708
-0.4604143
1.1753065
1.1818493 1.840587
0.40274
0.90170428
2.615563479
218
218
218
218
218
218
Second sub-sample: 1981M09 – 1999M10
MKT_RF
SMB
HML
RMW
CMA
0.895367 -0.225367 0.344037 0.35028 0.30311927
MOM
0.871743119
4.3809848
2.5700709 2.513338
2.962427341
-0.928919
0.1667518 0.272486 -0.04459 -0.2338126 -0.560289426
4.4030112
0.9001268 -0.03389
218
218
218
1.43832
1.86450853
0.1336
0.56661466
1.004318236
218
218
218
Third sub-sample: 1999M11 – 2017M12
MKT_RF
SMB
HML
RMW
CMA
0.4799541 0.4093119 0.256514 0.40257 0.3003211
MOM
0.26087156
4.3576571
3.1558597 3.261759
5.500244458
-0.617728
0.5269281 0.131278 -0.43551 0.89363019 -1.433499131
1.0295364
6.3512863 2.602357
218
218
218
3.09321
2.12446369
8.26343
2.88833294
9.295922984
218
218
218
2. Do the statistics suggest to you that returns for those risk factors come from the same
distribution over the entire period? (5p)
The returns don’t come from the same distribution. The reason for this is because of
the kurtosis. Most of the returns come from the normal distribution as they have a
kurtosis of between -3 and 3. RMW and MOM stocks have kurtosis that is above 3.
3. Make a plot showing the growth of $1 in each of the six “risk factors (portfolios)”
over the full sample. (Recall, this is called an “equity curve”). (5p)
4. Which factor portfolio gives the lowest and highest future value (full sample)? (5p)
MOM has both the highest and lowest future values according to the graph plotted.
5. Make a plot showing the growth of $1 in each of the six “risk factors (portfolios)” over
the five-year period (Jan 2013 – Dec 2017). (Recall, this is called an “equity curve”).
(5p)
6. Which factor portfolio gives the lowest and highest future value (over the five-year
period (Jan 2013 – Dec 2017). )? (5p)
From the plot in the Excel sheet, the MOM portfolio has the highest and the lowest
values.
7. Give a brief explanation of what are the real, macroeconomic, aggregate,
nondiversifiable risk that are proxied by the returns of the [RM-RF], SMB, HML,
RMW, CMA and MOM risk portfolios. For example, why are investors so concerned
about holding stocks that do badly at the times that the HML (value less growth) and
SMB (small-cap less large-cap) portfolios do badly, even though the market [RM-RF]
does not fall? (5p)
Investors will choose portfolios that are faring badly in the short term as they will be
rewarded in the long term. Some things like sensitivity to the market, and also the
sensitivity to the value of stocks. Unsystematic and unpriced risks may cause an addition
in the average expected return.
Part 2.
Go to Yahoo! finance site. Please download monthly adj. close prices from 12/1/2012 to
12/1/2017 for S&P 500 index (^SP500TR) and the following funds:
•
•
•
•
•
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Long-term bond fund: Fidelity Corporate Bond (FCBFX)
Real Estate fund: T. Rowe Price Real Estate (TRREX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
1. Describe briefly the goal of each fund (5p)
Each of the funds main goal is to grow its capital in the long run. They invest more
than 80% of their assets in their respective areas of operation.
2. Compute time plots of monthly prices and continuously compounded returns and
comment. (5p)
Chart Title
FIEUX
FLATX
FCBFX
TRREX
9/1/2017
6/1/2017
3/1/2017
12/1/2016
9/1/2016
6/1/2016
3/1/2016
12/1/2015
9/1/2015
6/1/2015
3/1/2015
12/1/2014
9/1/2014
6/1/2014
3/1/2014
12/1/2013
9/1/2013
6/1/2013
3/1/2013
12/1/2012
40
35
30
25
20
15
10
5
0
FSLCX
a. Are there any unusually large or small returns? (5p)
Yes. FIEUX in the last period id unusually high.
b. Can you identify any news events that may explain these unusual values? (5p)
Inflation and political events like the BREXIT caused the unusual values in the
plot.
3. Make a plot showing the growth of $1 in each of the funds over the five-year period.
(5p)
a. Which fund gives the highest future value? (5p)
FLATX.
b. Are you surprised? (5p)
I am surprised as in the present values it does not have the highest values.
4. Compute univariate descriptive statistics (mean, variance, standard deviation,
skewness, kurtosis) for each return series and comment. (5p)
Mean
Standard
Deviation
Sample Variance
Kurtosis
Skewness
FIEUX
FLATX
FCBFX TRREX
FSLCX
28.78332
21.96311165 9.708027
19.38531315 12.04952
2.481076
6.155738
0.161799
0.188283
4.424312854
0.5719
19.57454423 0.32707
-0.743636414 -0.94961
0.201358346 0.169967
2.683698717 1.690768
7.202238801 2.858697
-1.263624458 -0.52178
-0.32895524 -0.03287
a. Which funds have the highest and lowest average return? (5p)
FIUEX has the highest average while FCBFX has the lowest average.
b. Which funds have the highest and lowest standard deviation? (5p)
FLATX has the highest standard deviation while FCBFX has the lowest
standard deviation.
c. Which funds look most and least normally distributed? (5p)
FIEUX is the most normally distributed as its kurtosis is the nearest to 0 while
TRREX is the least normally distributed as it has the largest absolute kurtosis.
5. Using a monthly risk free rate equal to 0.04167% per month (which corresponds to a
continuously compounded annual rate of 0.5%), compute Sharpe’s slope/ratio for each
fund. Arrange these values in a table from highest to lowest. Which asset has the
highest Sharpe ratios? (5p)
FIEUX
FCBFX
TRREX
FSLCX
FLATX
Sharpe’s
Slope
9.92163293 9.688798 5.670649 4.662097 4.022345
FIEUX has the highest.
6. Compute estimated standard errors and form 95% confidence intervals for the
estimates of the mean and standard deviation. Arrange these values in a table. (5p)
Confidence
Levels
FIEUX
FLATX
FCBFX
TRREX
FSLCX
Upper Limit
33.64623341
30.63476484
10.82895157
24.64536263
15.36342991
Lower Limit
23.92041579
13.29145846
8.587102302
14.12526367
8.735619054
Standard
Error
0.320305525
0.571176333
0.073832014
0.346464015
0.21827722
a. Are these means and standard deviations estimated very precisely? (5p)
They are not precisely estimated.
b. Which estimates are more precise: the estimated means or standard deviations?
(5p)
The standard deviations are more precise as the standard error is less.
7. Convert the monthly sample means into annual estimates by multiplying by 12 and
convert the monthly sample SDs into annual estimates by multiplying by the square
root of 12. Comment on the values of these annual numbers. (5p)
Annualized
Figures
Mean
Standard
Deviation
FIEUX
FLATX
FCBFX
TRREX FSLCX
345.3998952 263.5573398 116.4963232 232.6238 144.5943
1.575143144 2.103405062 0.756240916
1.6382 1.300295
a. Using these values, compute annualized Sharpe ratios. (5p)
Sharpes Slope
FIEUX
FLATX
FCBFX
TRREX FSLCX
216.6361174 123.3192524 148.5364265 139.456 107.9965
b. Are the asset rankings the same as with the monthly Sharpe ratios? (5p)
They are not the same.
8. Compute and plot all pair-wise scatterplots between these 5 funds. Briefly comment on
any relationships you see. (5p)
Scatter Plot
40
35
30
25
20
15
10
5
0
4/1/2012
8/14/2013
12/27/2014
FIEUX
FLATX
5/10/2016
FCBFX
TRREX
9/22/2017
2/4/2019
FSLCX
There is a linear relationship between the stocks.
9. Compute the sample covariance matrix of the returns on these 5 funds and comment
on the direction of linear association between the asset returns. (5p)
Covariance
Matrix
FIEUX
FIEUX
FLATX
FCBFX
TRREX
FSLCX
FLATX
6.155737746 1.750038634
1.750038634 19.57454423
0.85170509 1.086832429
3.204982241 7.744527174
3.044578462 4.002795787
FCBFX
TRREX
FSLCX
0.85170509 3.204982241 3.044578462
1.086832429 7.744527174 4.002795787
0.327069979 1.437094406 0.884652615
1.437094406 7.202238801 4.023442014
0.884652615 4.023442014 2.858696687
10. Compute the sample correlation matrix of the returns on these 5 funds. (5p)
Correlation
Matrix
FIEUX
FLATX
1 0.159426955
0.159426955
1
0.600245408 0.429532884
0.481339846 0.652251851
0.725776792 0.535098293
FIEUX
FLATX
FCBFX
TRREX
FSLCX
FCBFX
TRREX
FSLCX
0.600245408 0.481339846 0.725776792
0.429532884 0.652251851 0.535098293
1 0.936334874 0.914888957
0.936334874
1 0.886706616
0.914888957 0.886706616
1
a. Which funds are most highly correlated? (5p)
TRREX and FCBFX
b. Which are least correlated? (5p)
FIEUX and FLATX
c. Based on the estimated correlation values do you think diversification will
reduce risk with these assets? (5p)
When the correlation between the stocks reduce, there will be more
diversification benefit thus a bend towards the left. This reduces the risk
between the stocks.
Part 3. Estimating expected returns
In this section, you need to use information from Part 1 and Part 2.
Use the Fama-French Three-Factor model augmented by “Momentum”(MOM) to estimate the
expected returns of each of the funds from part 2:
o
o
o
o
o
European stock fund: Fidelity Europe (FIEUX)
Latin America Fund: Fidelity Latin America (FLATX)
Long-term bond fund: Fidelity Corporate Bond (FCBFX)
Real Estate fund: T. Rowe Price Real Estate (TRREX)
Small Cap Stock Fund: Fidelity Small Cap Stock (FSLCX)
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖,𝑀 𝑅𝑀𝐾𝑇
+ 𝛽𝑖,𝑆𝑀𝐵 𝑆𝑀𝐵𝑡 + 𝛽𝑖,𝐻𝑀𝐿 𝐻𝑀𝐿𝑡 + 𝛽𝑖,𝑀𝑂𝑀 𝑀𝑂𝑀𝑡 + 𝑒𝑖,𝑡
𝑒
To simplify notation in the regression notice that 𝑅𝑖,𝑡
= 𝑅𝑖,𝑡 − 𝑅𝐹,𝑡 = is stock or portfolio 𝑖 𝑡ℎ excess
𝑒
return and 𝑅𝑀𝐾𝑇
= 𝑅𝑀𝑡 − 𝑅𝐹,𝑡 = is the excess return on a “stock market portfolio”
In order to do this follow 3 simple steps:
1. Step 1. Estimate the risk premia for each factor (5p)
1
𝜆𝑀𝐾𝑇 = 𝑇 ∑𝑇𝑡=1(𝑅𝑀𝑡 − 𝑅𝐹,𝑡 ) 2.89436
1
, 𝜆𝑆𝑀𝐵 = 𝑇 ∑𝑇𝑡=1 𝑆𝑀𝐵𝑡 4.21436
1
, 𝜆𝐻𝑀𝐿 = 𝑇 ∑𝑇𝑡=1 𝐻𝑀𝐿𝑡 , 4.08756
𝑇
1
𝜆𝑀𝑂𝑀 = ∑ 𝑀𝑂𝑀𝑡
𝑇
𝑡=1
-3.97297
2. Step 2. Estimate the sensitivities of the 𝒊𝒕𝒉 stock to each of those factors. (5p)
Sensitivities
Mkt-RF
SMB
HML
MOM
12.0608
14.5346
14.2228
12.3605
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖𝑀 𝑅𝑀𝐾𝑇
+ 𝛽𝑖𝑆𝑀𝐵 𝑆𝑀𝐵𝑡 + 𝛽𝑖𝐻𝑀𝐿 𝐻𝑀𝐿𝑡 + 𝛽𝑖,𝑀𝑂𝑀 𝑀𝑂𝑀𝑡 + 𝑒𝑖𝑡
3. Step 3. The expected returns can be calculated by combining the results of the previous
steps. (5p)
Expected
Returns
Mkt-RF
SMB
HML
MOM
34.90832
61.25397
58.13634
49.10785
𝐸(𝑅𝑖𝑒 ) = 𝛽̂𝑖,𝑀 𝜆𝑀𝐾𝑇 + 𝛽̂𝑖,𝑆𝑀𝐵 𝜆𝑆𝑀𝐵 + 𝛽̂𝑖,𝐻𝑀𝐿 𝜆𝐻𝑀𝐿 + 𝛽̂𝑖,𝑀𝑂𝑀 𝜆𝑀𝑂𝑀
4. Which fund has the highest and lowest expected return? (5p)
SMB has the highest while MKT-RF has the lowest.
5. Compare the factor betas and provide some comparisons between the two funds. (5p)
One has a very high beta while the other has a moderate beta.
Now, use the Fama-French 5-Factor model below to estimate each of these funds’ expected
returns
𝑒
𝑒
𝑅𝑖,𝑡
= 𝛼𝑖 + 𝛽𝑖,𝑀 𝑅𝑀𝐾𝑇
+ 𝛽𝑖,𝑆𝑀𝐵 𝑆𝑀𝐵𝑡 + 𝛽𝑖,𝐻𝑀𝐿 𝐻𝑀𝐿𝑡 + 𝛽𝑖,𝐻𝑀𝐿 𝑅𝑀𝑊𝑡 + 𝛽𝑖,𝐻𝑀𝐿 𝐶𝑀𝐴𝑡 + 𝑒𝑖,𝑡
𝑒
To simplify notation in the regression notice that 𝑅𝑖,𝑡
= 𝑅𝑖,𝑡 − 𝑅𝐹,𝑡 = is stock or portfolio 𝑖 𝑡ℎ excess
𝑒
return and 𝑅𝑀𝐾𝑇
= 𝑅𝑀𝑡 − 𝑅𝐹,𝑡 = is the excess return on a “stock market portfolio”
In order to do this follow the same procedure as before.
6. Step 1. Estimate the risk premia for each factor (5p)
Risk
Premier
FIEUX
FLATX
FCBFX
TRREX
FSLCX
24.61632
17.79611
5.541027
15.21831
7.882524
7. Step 2. Estimate the sensitivities of the 𝒊𝒕𝒉 stock to each of those factors. (5p)
Sensitivities
FIEUX
FLATX
FCBFX
TRREX
FSLCX
105.0573
78.58071
23.66136
66.09841
34.53725
8. Step 3. The expected returns can be calculated by combining the results of the previous
steps. (5p)
Expected
Returns
FIEUX
FLATX
FCBFX
TRREX
FSLCX
2586.125
1398.431
131.1082
1005.906
272.2407
9. Which fund has the highest and lowest expected return? (5p)
FIEUX has the highest while FCBFX has the lowest.
10. Compare the factor betas and provide some comparisons between the two funds. (5p)
The two stocks are correlated and their betas are not very different.
11. Which model is better to calculate the factor betas? (5p)
The correlation method is the better method of calculating beta.
Part 4. Portfolio
Please download monthly adj. close prices from 12/1/2012 to 12/1/2017 for the following
individuals stocks:
• Alphabet Inc. (GOOG)
• Boeing (BA)
• Costco Wholesale Corporation (COST)
• Wells Fargo & Company (WFC)
• Tesla, Inc. (TSLA)
Information on these stocks is available on the Yahoo! finance site. After typing in the sticker
symbol and retrieving the quote data, choose Profile to get a summary of the stock. Please review
each stock before doing any of the analysis below.
1. Compute monthly returns for each individual stock. (5p)
2. Compute univariate descriptive statistics (mean, standard deviation) for each return
series and comment. Arrange these values in a table. (5p)
Mean
Standard Deviation
GOOG
646.5391825
174.1395821
BA
126.8102858
42.97551229
COST
WFC
TSLA
126.9223988 42.74329352 214.2069992
25.09601704 6.526396575 77.00336745
a. Which stocks have the highest and lowest average return? (5p)
Alphabet, Inc (GOOG) has the highest while Wells Fargo & Company (WFC)
has the lowest
b. Which stocks have the highest and lowest standard deviation? (5p)
Alphabet, Inc (GOOG) has the highest while Wells Fargo & Company (WFC)
has the lowest
3. Compute the sample covariance matrix of the returns on the five stocks and comment
on the direction of linear association between the asset returns. (5p)
Covariance Matrix
GOOG
BA
COST
WFC
TSLA
GOOG
30324.59406
6529.81087
3967.425111
849.6891052
10585.93626
BA
6529.81087
1846.894656
850.446221
208.8390118
2653.418429
COST
3967.425111
850.446221
629.8100711
135.3074048
1442.415361
WFC
849.6891052
208.8390118
135.3074048
42.59385226
435.827175
TSLA
10585.93626
2653.418429
1442.415361
435.827175
5929.518598
4. Compute the sample correlation matrix of the returns on the 5 stocks. (5p)
Correlation Matrix
GOOG
GOOG
BA
COST
WFC
TSLA
1
0.872533363
0.907833991
0.747634083
0.789445328
BA
0.872533363
1
0.788535033
0.744589842
0.801816448
COST
0.907833991
0.788535033
1
0.826120319
0.7464072
WFC
TSLA
0.747634083 0.789445328
0.744589842 0.801816448
0.826120319
0.7464072
1 0.867223681
0.867223681
1
a. Which assets are most highly correlated? (5p)
Alphabet Inc (GOOG) and Costco Wholesale Corporation (COST)
b. Which are least correlated? (5p)
Wells Fargo & Company (WFC) and Boeing (BA)
c. Based on the estimated correlation values do you think diversification will
reduce risk with these assets? (5p)
When the correlation between the stocks reduce, there will be more
diversification benefit thus a bend towards the left. This reduces the risk
between the stocks.
5. Convert the monthly sample means into annual estimates by multiplying by 12.
Monthly variances and covariances can be annualized by multiplying by 12. Standard
deviations are annualized by multiplying monthly standard deviations by the square
root of 12. (5p)
Annualized
Figures
GOOG
BA
COST
WFC
TSLA
Mean
7758.47019
1521.723429
1523.068786 512.9195222 2570.48399
Standard Deviation 603.2372077
148.8715415
86.93515315 22.60810092 266.7474895
Covariance Matrix
GOOG
BA
COST
WFC
TSLA
GOOG
BA
COST
363895.1288
78357.73044
47609.10133
78357.73044
22162.73588
10205.35465
47609.10133
10205.35465
7557.720854
10196.26926
2506.068142
1623.688858
127031.2351
31841.02115
17308.98433
WFC
10196.26926
2506.068142
1623.688858
511.1262271
5229.9261
TSLA
127031.2351
31841.02115
17308.98433
5229.9261
71154.22318
GOOG
WFC
8.971608995
8.935078105
9.913443824
1
10.40668417
TSLA
9.473343933
9.621797377
8.956886405
10.40668417
1
Correlation Matrix
GOOG
BA
COST
WFC
TSLA
1
10.47040036
10.89400789
8.971608995
9.473343933
BA
COST
10.47040036
10.89400789
1
9.462420393
9.462420393
1
8.935078105
9.913443824
9.621797377
8.956886405
6. Using the annualized estimated means, variances and covariances computed earlier.
Suppose, an investor currently owns a portfolio consisting of two stocks, COST and
WFC, with 60% invested in COST. (5p)
a. Calculate the expected value of the portfolio returns. (5p)
93.25075669. This has been calculated using Excel.
b. Calculate the risk (standard deviation) of the portfolio returns. (5p)
21.44460627. This has been calculated using Excel
7. Suppose the previous investor wants to keep her stake in Costco Wholesale Corporation
(COST) and would like other asset to substitute for WFC. Which of the remaining three
stocks will give her the maximum benefit of diversification, assuming she still keeps 60%
invested in COST? (5p) Alphabet Inc (GOOG)
a. Calculate the expected value of returns of this new portfolio. (5p)
334.7691123. Calculated using Excel
b. Calculate the risk (standard deviation) of this new portfolio. (5p)
120.0503549 Calculated using Excel.
This file was created by CMPT_ME_BEME_OP_INV_RETS using the 201912 CRSP database.
The 1-month TBill return is from Ibbotson and Associates Inc.
Date
Mkt-RF
SMB
HML
RMW
CMA
RF
196307
-0.39
-0.47
-0.83
0.66
-1.15
196308
5.07
-0.79
1.67
0.39
-0.4
196309
-1.57
-0.48
0.18
-0.76
0.24
196310
2.53
-1.29
-0.1
2.75
-2.24
196311
-0.85
-0.84
1.71
-0.45
2.22
196312
1.83
-1.89
-0.12
0.08
-0.3
196401
2.24
0.08
1.59
0.22
1.5
196402
1.54
0.32
2.83
0.06
0.85
196403
1.41
1.41
3.32
-2.01
2.93
196404
0.1
-1.52
-0.55
-1.35
-1.08
196405
1.42
-0.68
1.98
-0.26
0.24
196406
1.27
0.09
0.68
-0.42
0.14
196407
1.74
0.53
0.68
0.14
1.84
196408
-1.44
0.3
0.09
0.06
0.36
MOM
0.27
0.25
0.27
0.29
0.27
0.29
0.3
0.26
0.31
0.29
0.26
0.3
0.3
0.28
1
1.03
0.16
3.14
-0.75
1.7
1.06
0.24
0.72
-0.6
2.54
0.45
-0.37
-0.19
196409
196410
196411
196412
196501
196502
196503
196504
196505
196506
196507
196508
196509
196510
196511
196512
2.69
0.59
0
0.03
3.54
0.44
-1.34
3.11
-0.77
-5.51
1.43
2.73
2.86
2.6
-0.03
1.01
-0.31
0.88
-0.27
-0.58
2.49
3.31
2.03
1.18
0.06
-4.3
1.07
2.72
0.62
3.42
5.19
2.64
1.65
1.14
-1.98
-2.55
0.18
0.22
1.09
0.71
-1.63
0.55
2.18
-0.96
-0.1
1.52
0.21
1.95
-0.48
-0.29
0.6
1.13
0.84
0.4
-0.3
0.26
-0.37
0.12
-1.41
2.06
-0.82
1.22
-1.06
-1.28
0.58
0.48
-0.16
-1.63
0.11
-0.69
0.73
-2.3
0.7
0.41
-0.02
-0.76
0.82
-0.76
-0.9
-0.45
0.28
0.29
0.29
0.31
0.28
0.3
0.36
0.31
0.31
0.35
0.31
0.33
0.31
0.31
0.35
0.33
-0.39
0.08
1.08
-0.69
-1.28
0.3
0.1
2.54
0.54
-3.12
4.1
2.58
3.36
3.45
4.42
0.09
196601
196602
196603
196604
196605
196606
196607
0.72
-1.21
-2.51
2.14
-5.66
-1.44
-1.63
4.72
4.86
0.22
3.36
-5.18
1.34
-0.5
3.49
0.27
-2.07
-0.54
-1.55
0.5
0.96
-2.83
-0.15
1.3
0.41
1.66
0.06
-0.45
-0.07
-1.44
-0.01
-0.91
-1.52
0.8
1.77
0.38
0.35
0.38
0.34
0.41
0.38
0.35
5.41
4.58
1.34
6.18
-4.69
3.28
-1.39
196608
196609
196610
196611
196612
196701
196702
196703
196704
-7.91
-1.06
3.86
1.4
0.13
8.15
0.78
3.99
3.89
-3.03
-1.18
-6.53
3.37
1.91
9
3.03
1.9
0.46
0.47
0.53
2.87
-4.46
-1.22
2.22
-2.17
0.31
-2.64
0.05
-1.65
-3.64
4.17
0.68
0.62
1.94
0.9
2.43
0.77
2.5
4.28
-6.53
-0.13
-2.97
-0.94
-1.51
-3.75
0.41
0.4
0.45
0.4
0.4
0.43
0.36
0.39
0.32
-2.11
-1.73
-5.13
5.73
1.07
-6.75
3.56
1.42
0.64
196705
196706
196707
196708
196709
196710
196711
196712
196801
196802
196803
196804
196805
196806
196807
196808
196809
196810
196811
196812
196901
196902
196903
196904
196905
196906
196907
196908
196909
196910
196911
196912
197001
197002
197003
197004
-4.33
2.41
4.58
-0.89
3.11
-3.09
0.37
3.05
-4.06
-3.75
0.2
9.05
2.28
0.69
-2.72
1.34
4.03
0.42
5.43
-3.94
-1.25
-5.84
2.64
1.46
-0.1
-7.18
-7
4.68
-2.98
5.06
-3.79
-2.63
-8.1
5.13
-1.06
-11
2.37
6.46
3.48
0.76
2.46
0.47
-0.06
5.75
4.49
-2.91
-1.52
6.25
7.04
-0.26
-1.33
2.29
2.77
-0.43
2.41
3.6
-0.46
-4.12
-0.41
-0.83
-0.18
-5.49
-3.37
0.71
1.29
3.89
-2.41
-3.7
3.09
-2.58
-2.4
-6.3
0.8
0.96
2.65
1.46
-2.47
-3.39
-1.71
-0.39
4.75
1.17
-0.59
-1.03
0.84
0.67
5.48
1
0.24
2.89
-0.9
0.02
1.69
0.9
-0.46
0.03
0.73
-1.09
1.42
-3.87
-3.19
-3.19
-1.12
-3.02
3.04
4.04
4.25
6.39
-1.75
-0.64
0.51
0.42
0.22
0.81
1.29
-0.65
-4.58
-0.14
1.17
2.79
0.39
-1.33
-3
-0.6
-2.03
-1.3
0.46
-1.78
-1.54
2.17
-1.34
0.38
-0.97
4.26
1.4
1.16
3.42
0.01
1.44
2.47
-1.77
-2.28
-0.87
-0.64
1.61
-2.39
2.72
1.41
-0.95
-2.55
-2.29
0.03
6.45
2.49
-1.15
-3.66
-1.82
2.68
3.61
0.4
0.92
2.81
-2.52
1.65
1.41
0.8
-0.42
0.06
1.49
-1.55
1.97
-4.08
-0.83
-2.03
0.32
-2.04
3.92
3.06
4.43
5.88
0.33
0.27
0.31
0.31
0.32
0.39
0.36
0.33
0.4
0.39
0.38
0.43
0.45
0.43
0.48
0.42
0.43
0.44
0.42
0.43
0.53
0.46
0.46
0.53
0.48
0.51
0.53
0.5
0.62
0.6
0.52
0.64
0.6
0.62
0.57
0.5
0.67
6.03
-1.07
-1.41
2.55
3.66
1.29
3.23
-4.64
-3.41
3.18
5.15
3.73
-1.92
-0.84
1.88
-0.67
-1.52
1.73
0.11
-0.15
-2.46
3.95
1.17
1.68
-2.22
1.64
2.13
2.53
-4.32
3.6
5.05
0.63
0.12
-0.29
-0.73
197005
197006
197007
197008
197009
197010
197011
197012
197101
197102
197103
197104
197105
197106
197107
197108
197109
197110
197111
197112
197201
197202
197203
197204
197205
197206
197207
197208
197209
197210
197211
197212
197301
197302
197303
197304
197305
197306
197307
197308
197309
197310
197311
197312
197401
197402
197403
-6.92
-5.79
6.93
4.49
4.18
-2.28
4.59
5.72
4.84
1.41
4.13
3.15
-3.98
-0.1
-4.5
3.79
-0.85
-4.42
-0.46
8.71
2.49
2.87
0.63
0.29
1.25
-2.43
-0.8
3.26
-1.14
0.52
4.6
0.62
-3.29
-4.85
-1.3
-5.68
-2.94
-1.56
5.05
-3.82
4.75
-0.83
-12.75
0.61
-0.17
-0.47
-2.81
-4.42
-2.17
-0.69
1.54
8.49
-4.5
-4
3.02
7.54
2.05
2.15
-0.37
-1.17
-1.45
-1.41
-0.18
0.19
-1.65
-2.9
3.27
6.33
0.96
-0.49
0.15
-3.15
-0.35
-2.74
-3.53
-2.25
-2.53
-0.53
-1.82
-2.79
-4.01
-2.31
-3
-5.98
-2.54
7.25
-1.75
3.54
-0.26
-7.25
-4.6
10.41
0.16
2.64
3.6
0.87
0.96
1.03
-5.58
0.27
1.63
1.01
1.38
-1.29
-4.04
0.72
-1.38
-2.06
0.17
2.67
-2.96
-0.43
-1.7
-0.35
1.99
-2.76
-1.73
0.23
-2.69
-2.51
0.78
4.69
0.47
1.37
4.76
-2.27
2.68
1.7
2.78
5.69
0.21
1.44
-5.31
1.14
2.18
1.74
4.04
4.09
5.99
2.54
-0.11
-1.3
0.19
-0.07
0.57
0.24
1.83
1.75
0.14
-2.19
0.65
1.97
-1.56
1.46
1.52
0.48
-0.34
2.59
1.48
2.45
-0.43
-1.48
1.65
1.58
-0.59
2.33
1.75
1.08
-2.02
1.67
-0.12
-1.98
2.6
0.4
-0.34
-1.07
-1.63
2.02
-0.27
-0.1
-1.36
-2.3
-1.94
-2.71
-2.8
-3.01
-1.93
2.82
3.62
2.9
1.9
-0.15
-5.93
2.38
1.59
0.28
-0.14
-0.72
-2.69
0.72
0.3
-1.74
1.62
2.6
-1.56
-1.24
-0.36
-1.83
0.29
-0.52
-0.19
-0.89
-1.85
-0.36
-0.59
2.95
-1.98
-0.05
3.35
-2.2
0.92
-0.01
0.73
2.71
-1.79
0.22
-3.45
1.2
1.82
2.59
1.49
2.3
4.47
2.67
0.36
0.53
0.58
0.52
0.53
0.54
0.46
0.46
0.42
0.38
0.33
0.3
0.28
0.29
0.37
0.4
0.47
0.37
0.37
0.37
0.37
0.29
0.25
0.27
0.29
0.3
0.29
0.31
0.29
0.34
0.4
0.37
0.37
0.44
0.41
0.46
0.52
0.51
0.51
0.64
0.7
0.68
0.65
0.56
0.64
0.63
0.58
0.56
-2.73
5.78
-2.94
-6.5
-8.92
9.61
2.89
-2.19
-6.71
0.81
-1.21
1.32
0.89
2.72
-2.37
3.51
2.09
0.46
1.48
-0.68
0.24
2.7
2.91
2.9
3.19
1.93
2.75
-5.34
1.75
0.75
-5.14
4.98
3.71
2.12
3.62
6.4
7.09
4.31
-11.69
3.42
-7.03
6.75
8.63
10.39
-8.82
0.14
-0.99
197404
197405
197406
197407
197408
197409
197410
197411
197412
197501
197502
197503
197504
197505
197506
197507
197508
197509
197510
197511
197512
197601
197602
197603
197604
197605
197606
197607
197608
197609
197610
197611
197612
197701
197702
197703
197704
197705
197706
197707
197708
197709
197710
197711
197712
197801
197802
-5.29
-4.68
-2.83
-8.05
-9.35
-11.77
16.1
-4.51
-3.45
13.66
5.56
2.66
4.23
5.19
4.83
-6.59
-2.85
-4.26
5.31
2.64
-1.6
12.16
0.32
2.32
-1.49
-1.34
4.05
-1.07
-0.56
2.07
-2.42
0.36
5.65
-4.05
-1.94
-1.37
0.15
-1.45
4.71
-1.69
-1.75
-0.27
-4.38
4
0.27
-6.01
-1.38
-0.63
-3.17
-0.02
1.99
0.3
1.46
-6.85
-1.44
-4.36
12.79
-0.65
3.98
-0.59
2.89
1.32
3.42
-2.79
0.02
-4.23
-1.07
-0.03
6.35
7.97
-1.34
0.14
-1.12
-1.13
0.62
-2.03
0.12
0.16
2.65
3.61
5.9
1.11
1.29
0.59
1.28
2.07
1.82
0.89
1.56
1.5
3.62
1.6
2.69
3.67
1.06
-2.04
0.79
5.14
2.5
5.49
-9.99
-0.16
0
8.44
-4.56
2.49
-1.11
-4.02
1.32
1.7
-0.9
0.34
0.3
1.99
1.76
8.58
5.78
-0.04
-0.06
-1.32
0.68
1.74
0.79
-0.29
-0.13
1.51
2.22
4.26
0.5
1.02
3.45
0.84
-0.64
-0.59
-2.79
-0.49
1.72
0.31
-0.37
3.31
0.76
2.86
5.08
0.62
-3.26
-0.26
-4.28
-0.03
-3.39
-0.65
-0.89
1.15
1.23
1.37
-1.05
-2.62
0.43
1.03
0.53
-0.46
-0.62
-0.12
-1.9
-2.58
-0.38
0.39
2.47
-0.59
-1.09
-0.46
0.99
-0.22
-1.39
-0.62
-0.53
-0.13
-0.32
-2.03
0.33
0.88
0.83
0.96
1.23
-0.3
-0.05
0.94
-1.67
0.36
1.85
0
3.06
4.58
2.55
5.91
-2.94
2.95
3.23
-0.86
-2.14
-1.25
-1.35
-0.61
1.06
1.19
-0.89
0.5
2.27
1.71
0.6
2.36
3.84
1.09
-1.1
-1.42
0.93
0.29
-0.59
-1.14
-0.31
0.08
2.23
1.93
-0.19
-0.09
1.16
0.11
-1.22
0.01
-0.62
-0.84
-0.46
0.65
-0.65
1.53
1.04
0.75
0.75
0.6
0.7
0.6
0.81
0.51
0.54
0.7
0.58
0.43
0.41
0.44
0.44
0.41
0.48
0.48
0.53
0.56
0.41
0.48
0.47
0.34
0.4
0.42
0.37
0.43
0.47
0.42
0.44
0.41
0.4
0.4
0.36
0.35
0.38
0.38
0.37
0.4
0.42
0.44
0.43
0.49
0.5
0.49
0.49
0.46
2
-0.26
2.37
3.01
3
4.32
-0.5
2.17
2.95
-13.89
-0.55
-1.99
1.34
-0.47
0.06
0.42
-0.13
0.32
-0.14
-0.45
-0.12
4.46
0.36
0.19
0.5
-1.1
-0.42
-0.12
-0.85
0.24
-0.48
2.9
0.77
3.98
0.35
0.49
4.22
2.01
1.77
0.31
-1.78
2.06
-0.13
2.19
1.56
-0.72
1.9
197803
197804
197805
197806
197807
197808
197809
197810
197811
197812
197901
197902
197903
197904
197905
197906
197907
197908
197909
197910
197911
197912
198001
198002
198003
198004
198005
198006
198007
198008
198009
198010
198011
198012
198101
198102
198103
198104
198105
198106
198107
198108
198109
198110
198111
198112
198201
2.85
7.88
1.76
-1.69
5.11
3.75
-1.43
-11.91
2.71
0.88
4.23
-3.56
5.68
-0.06
-2.21
3.85
0.82
5.53
-0.82
-8.1
5.21
1.79
5.51
-1.22
-12.9
3.97
5.26
3.06
6.49
1.8
2.19
1.06
9.59
-4.52
-5.04
0.57
3.56
-2.11
0.11
-2.36
-1.54
-7.04
-7.17
4.92
3.36
-3.65
-3.24
3.72
-0.31
4.56
1.59
0.15
4.93
-0.28
-10.05
2.85
1.07
3.84
0.55
3.16
2.4
0.5
0.99
1.24
1.97
-0.27
-3.46
2.44
4.34
1.9
-1.49
-6.97
1
2.1
1.47
3.93
4.26
0.58
2.33
-3.4
-0.31
3.37
-0.49
3.08
4.6
2.45
-0.97
-1.97
-1.81
-2.49
2.24
-1.38
1.19
-1.18
1.2
-3.54
-0.62
0.59
-1.11
-0.46
1.87
1.36
-2.22
-2.19
2.27
1.2
-0.67
1.05
1.92
1.48
1.7
-1.55
-0.87
-1.86
-3.25
-1.98
1.8
0.62
-1.06
1.06
0.39
-0.89
-6.3
-2.64
-4.79
-2.74
-8.35
2.68
6.84
0.97
0.67
2.26
-0.43
5.13
-0.66
4.84
5.2
-4.21
1.9
0.74
3.17
-0.6
2.82
0.27
-1.44
1.58
1.39
-0.68
0.17
1.09
1.96
-2.5
-1.14
0.62
1.05
-0.88
-1.61
-0.2
1.58
1.02
1.04
0.11
-0.67
-1.67
0.11
1.38
-2.09
0.31
-0.09
3.94
2.1
2.03
1.67
4.53
-1.25
-3.54
0.19
-2.34
0.82
0.33
-1.35
1.08
-0.25
-0.02
3.29
0.1
0.19
-1.52
1.88
-1.35
0.36
-0.02
-0.78
0.42
1.8
1.03
-1.21
-1.38
1.57
1.04
0.28
0.21
-0.84
-0.45
0.59
-1.45
0.54
0.24
-2
-1.01
2.01
3.04
-1.34
0.54
-0.47
-0.95
-2.46
-0.8
-2.81
-1.22
-5.74
1.18
4.35
2.25
-0.53
1.2
-1.6
2.68
-3.04
1.46
2.79
-2.76
0.63
2.41
2.08
0.53
0.54
0.51
0.54
0.56
0.56
0.62
0.68
0.7
0.78
0.77
0.73
0.81
0.8
0.82
0.81
0.77
0.77
0.83
0.87
0.99
0.95
0.8
0.89
1.21
1.26
0.81
0.61
0.53
0.64
0.75
0.95
0.96
1.31
1.04
1.07
1.21
1.08
1.15
1.35
1.24
1.28
1.24
1.21
1.07
0.87
0.8
1.37
0.85
2.85
2.78
4.22
2.82
-3.11
-8.39
5.52
3.05
-1.24
-1.06
2.88
0.81
-0.2
0.82
-1.09
-0.23
5.34
2.12
7.96
4.78
7.51
7.88
-9.59
-0.42
-1.11
1.59
0.37
3.19
5.44
7.32
15.24
-6.65
-7.94
-1.39
0.74
-0.96
3.75
-5.9
-2.52
-1.12
1.98
4.07
-0.27
1.32
1.73
198202
198203
198204
198205
198206
198207
198208
198209
198210
198211
198212
198301
198302
198303
198304
198305
198306
198307
198308
198309
198310
198311
198312
198401
198402
198403
198404
198405
198406
198407
198408
198409
198410
198411
198412
198501
198502
198503
198504
198505
198506
198507
198508
198509
198510
198511
198512
-5.86
-1.87
3.27
-3.99
-3.09
-3.19
11.14
1.29
11.3
4.67
0.55
3.6
2.59
2.82
6.67
0.52
3.07
-4.07
-0.5
0.91
-3.44
2.16
-1.78
-1.92
-4.82
0.63
-0.51
-5.97
1.82
-2.74
10.28
-0.8
-0.84
-1.76
1.84
7.99
1.22
-0.84
-0.96
5.09
1.27
-0.74
-1.02
-4.54
4.02
6.48
3.88
0.37
0
1.15
0.56
-0.51
0.95
-4.29
2.57
1.92
4.53
-0.01
3.28
2.9
1.42
0.49
6.22
1.15
0.98
-4.34
0.25
-3.82
1.88
-0.47
-0.07
-1.62
-0.28
-1.01
0.08
0.07
-2.22
-0.3
0
-1.43
-0.98
-0.69
3.49
1
-1.48
-0.1
-2.33
0.51
2.89
-0.32
-1.81
-1.55
0
-0.39
6.1
3.78
-2.78
1.81
1.54
0.15
1.16
0.34
-3.68
-1.96
0.01
-0.86
0.67
2.07
0.6
-1.4
-3.87
5.62
5.55
1.09
5.06
-0.62
1.67
7.63
3.36
0.48
1.29
0.26
-2.6
0.49
-1.85
5.32
0.51
4.08
-0.17
-5.43
-0.12
4.1
3.73
-0.86
0.76
-1.63
2.28
1.32
0.78
-2.87
-1.53
-3.38
-1.44
1.51
0.91
0.03
1.03
-1.96
2.12
0.3
-1.05
-0.07
-1.5
-0.57
-0.07
-0.16
-1.85
2.52
-0.03
0.45
1.21
-0.81
-0.48
1.59
-0.93
0.82
-0.93
3.27
2.32
3.08
3.69
-0.89
1.4
1.17
0.81
1.21
-0.83
1.26
1.18
1.59
1.3
1.44
-0.45
-0.04
1.16
0.92
0.29
0.97
4.57
2.37
-0.15
-0.09
2.72
1.54
0.18
-0.04
-0.37
0.32
1.03
-0.46
1.02
2.81
1.6
-1.53
-0.89
2.84
1.95
0.59
2.97
0.64
1.21
3
1.57
1.09
0.76
-0.5
-1.51
-2.17
-0.81
2.53
-1.16
2.08
-1.26
-3.36
1.03
2.94
0.71
-1.39
-0.59
-0.31
1.88
1.61
-1.11
-2.33
-1.8
0.92
0.98
1.13
1.06
0.96
1.05
0.76
0.51
0.59
0.63
0.67
0.69
0.62
0.63
0.71
0.69
0.67
0.74
0.76
0.76
0.76
0.7
0.73
0.76
0.71
0.73
0.81
0.78
0.75
0.82
0.83
0.86
1
0.73
0.64
0.65
0.58
0.62
0.72
0.66
0.55
0.62
0.55
0.6
0.65
0.61
0.65
5
2.93
-0.41
2.55
5
4.47
-3.52
4.17
0.03
5.89
0.02
-1.7
3.83
0.93
1.8
-1.59
1.78
-3.13
-6.25
-0.1
-4.56
-0.09
0.74
-2.49
0.19
1.09
2.05
1.52
-0.7
2.93
-5.69
3.67
3.21
1.7
1.54
-6.9
1.82
1.68
3.06
4
3.63
-3.94
1.82
1.48
4.88
-0.4
-0.13
198601
198602
198603
198604
198605
198606
198607
198608
198609
198610
198611
198612
198701
198702
198703
198704
198705
198706
198707
198708
198709
198710
198711
198712
198801
198802
198803
198804
198805
198806
198807
198808
198809
198810
198811
198812
198901
198902
198903
198904
198905
198906
198907
198908
198909
198910
198911
0.65
7.13
4.88
-1.31
4.62
1.03
-6.45
6.07
-8.6
4.66
1.17
-3.27
12.47
4.39
1.64
-2.11
0.11
3.94
3.85
3.52
-2.59
-23.24
-7.77
6.81
4.21
4.75
-2.27
0.56
-0.29
4.79
-1.25
-3.31
3.3
1.15
-2.29
1.49
6.1
-2.25
1.57
4.33
3.35
-1.35
7.2
1.44
-0.76
-3.67
1.03
1.01
-0.76
-0.57
2.92
-1.26
-0.87
-3.54
-4.36
1.97
-2.33
-1.88
0.07
-1.51
3.41
0.21
-1.55
-0.59
-2.29
-1.11
-0.9
0.37
-8.09
2.83
0.09
-0.54
3.32
6.26
1.12
-2.59
2.19
-0.17
-0.02
-1.31
-2.95
-1.66
2.01
-2.23
2.67
0.76
-0.7
-0.01
-1.08
-4.04
0.43
0.46
-3.33
-1.3
0.53
-0.94
-0.44
-2.85
-0.11
1.4
4.78
3.52
3.19
-1.32
-0.06
0.37
-3.18
-5.99
1.66
-0.33
0.13
1.07
0.66
-0.9
0.28
4.23
3.14
-4.49
5.08
-1.65
0.74
1.69
2.3
-1.07
2.27
2.03
-0.68
1.71
1.24
-1.55
0.51
0.87
0.46
-1.45
-0.82
2.19
-2.84
0.72
-1.34
-1.03
-1.12
-2.02
1.15
1.12
2.91
2.08
1.83
-0.57
-1.74
-0.05
0.13
1.12
0.83
0.2
-0.9
1.36
-0.46
0.52
1.73
-0.5
2.03
-0.9
2.01
-1.92
3.01
-1.12
1.56
-0.16
-0.23
-0.7
1.42
-0.59
-0.76
1.72
1.4
-0.31
0.64
-1.03
-0.82
0.01
0.76
0.49
0.24
1.99
0.41
1.4
0
-0.91
-2.05
-1.39
0.99
0.02
1.11
0.87
0.61
3.24
3.62
0.89
0.61
-0.03
-1.09
-2.76
4.03
1.22
1.32
0.83
1.56
-1.59
1.89
2.39
0.71
-2.36
2.09
-0.11
1.91
1.9
0.38
-3.25
1.45
1.74
-0.48
1.04
1.6
-0.37
0.16
1.89
0.79
-0.54
-0.04
1.52
-0.62
-0.5
0.59
0.02
1.47
0.56
0.53
0.6
0.52
0.49
0.52
0.52
0.46
0.45
0.46
0.39
0.49
0.42
0.43
0.47
0.44
0.38
0.48
0.46
0.47
0.45
0.6
0.35
0.39
0.29
0.46
0.44
0.46
0.51
0.49
0.51
0.59
0.62
0.61
0.57
0.63
0.55
0.61
0.67
0.67
0.79
0.71
0.7
0.74
0.65
0.68
0.69
2.96
2.78
2.45
-0.5
2.02
5.16
1.8
-5.01
-5.86
2.69
-0.32
0.4
2.1
-2.17
1.6
0.26
-0.68
-0.2
2.67
-0.87
0.71
-7.86
-1.15
5.83
-7.58
-1.54
0.63
2.25
0.64
-2.91
0.63
0.33
0.24
1.31
0.42
0.42
-0.14
0.94
3.55
1.69
1.56
0.65
5.44
-0.14
3.4
1.37
2.58
198912
199001
199002
199003
199004
199005
199006
199007
199008
199009
199010
199011
199012
199101
199102
199103
199104
199105
199106
199107
199108
199109
199110
199111
199112
199201
199202
199203
199204
199205
199206
199207
199208
199209
199210
199211
199212
199301
199302
199303
199304
199305
199306
199307
199308
199309
199310
1.16
-7.85
1.11
1.83
-3.36
8.42
-1.09
-1.9
-10.15
-6.12
-1.92
6.35
2.46
4.69
7.19
2.65
-0.28
3.65
-4.94
4.24
2.32
-1.59
1.29
-4.19
10.84
-0.59
1.09
-2.66
1.07
0.3
-2.34
3.77
-2.38
1.19
1.02
4.13
1.53
0.93
0.12
2.3
-3.05
2.89
0.31
-0.34
3.71
-0.12
1.41
-2.3
-1.33
1.18
1.64
-0.4
-2.4
1.38
-3.27
-3.85
-3.78
-5.07
-0.06
0.61
3.86
3.96
3.82
0.33
0.17
0.17
-0.96
1.44
1.55
0.95
-0.87
-2.42
9.26
1.35
-0.9
-5.73
0.21
-2.71
-0.61
-0.42
0.48
2.02
3.91
1.64
2.09
-3.42
0.05
-0.86
1.88
0.11
0.9
0.25
3.06
1.61
0.28
0.87
0.61
-2.9
-2.55
-3.74
-1.94
-0.01
1.58
0.73
0.22
-3.16
-1.54
-1.84
-0.54
-1.23
1.42
-0.57
1.21
-1.25
-0.78
-1
-0.43
-1.93
-4.03
4.51
6.37
3.65
4.31
1.28
3.4
-0.53
-1.03
-0.21
-2.1
-1.48
2.52
5.87
6.42
1.22
2.61
-3.41
2.62
3.25
-0.45
-0.44
-1.54
-0.1
-1.13
-0.19
2.15
1.72
1.65
-1
-0.27
-0.43
-0.16
2.87
0.82
2.86
1.55
-0.27
-0.39
0.53
2.11
1.75
1.61
0.88
-1.81
-1.73
1.08
3.58
-1.33
0.09
-0.08
1.71
-0.96
-0.03
1.57
3.79
1.53
1.31
-0.84
-0.44
-1.77
-0.5
-0.29
-3.69
-0.01
-1.06
-2.18
-1.15
0.77
-0.1
1.45
1.32
-0.64
-1.02
-0.98
-1.57
-0.34
3.1
2.99
3.71
-0.49
-4.75
-2
-4.08
-0.16
-0.99
0.71
-2.45
0.63
-1.41
-0.55
0.16
-0.29
0.12
-3.12
3.15
2.04
1.93
2.22
0.43
1.07
-0.72
-1.62
-0.5
-0.8
-1.91
0.75
2.78
4.05
0.92
1.52
-1.18
1.09
2.03
-0.04
-0.08
0.6
0.61
0.57
0.57
0.64
0.69
0.68
0.63
0.68
0.66
0.6
0.68
0.57
0.6
0.52
0.48
0.44
0.53
0.47
0.42
0.49
0.46
0.46
0.42
0.39
0.38
0.34
0.28
0.34
0.32
0.28
0.32
0.31
0.26
0.26
0.23
0.23
0.28
0.23
0.22
0.25
0.24
0.22
0.25
0.24
0.25
0.26
0.22
2.81
-3.28
-0.53
1.65
2.4
3.05
2.43
5.93
1.78
5.53
6.73
-5.62
0.16
-6.53
-4.73
2.83
-2.4
-0.1
0.46
4.31
1.63
1.73
3.23
1.26
8.3
-2.51
-0.58
-0.36
-2.6
0.13
-0.61
1.44
-0.51
1.43
2.71
-0.34
4.44
4.83
3.1
3.74
0.33
0.3
4.58
3.25
2.58
3.41
-2.67
199311
199312
199401
199402
199403
199404
199405
199406
199407
199408
199409
199410
199411
199412
199501
199502
199503
199504
199505
199506
199507
199508
199509
199510
199511
199512
199601
199602
199603
199604
199605
199606
199607
199608
199609
199610
199611
199612
199701
199702
199703
199704
199705
199706
199707
199708
199709
-1.89
1.65
2.87
-2.55
-4.78
0.68
0.58
-3.03
2.82
4.01
-2.31
1.34
-4.04
0.86
1.8
3.63
2.19
2.11
2.9
2.72
3.72
0.55
3.35
-1.52
3.96
1.03
2.26
1.33
0.73
2.06
2.36
-1.14
-5.97
2.77
5.01
0.86
6.25
-1.7
4.98
-0.49
-5.02
4.04
6.74
4.1
7.33
-4.15
5.35
-1.43
1.36
-0.14
2.65
-1.02
-1.05
-2.46
-0.52
-1.83
1.29
2.67
-2.3
0.11
-0.02
-2.76
-0.55
-0.72
-0.34
-2.06
2.95
2.1
1.83
-1.88
-4.05
-1.17
0.67
-2.59
1.82
1.54
4.64
3.17
-3.47
-3.68
2.55
-1.33
-3.88
-3.79
3.21
-1.82
-2.53
-0.48
-5.73
4.65
1.26
-2.84
7.66
2.56
-0.27
0.57
2.09
-1.45
1.29
1.68
0.2
1.69
0.62
-2.81
-1.91
-1.74
-0.94
0.54
0.81
1.09
-1.1
2.27
1.72
-2.28
-1.67
2.71
-0.75
-0.74
0.95
0.87
0.3
-1.42
1.01
-3.91
-1.2
1.56
4.45
-0.46
-3.15
5.12
1.16
0.89
-1.65
5.2
3.81
-0.07
-3.87
1.23
0.82
1.38
0.01
1.55
0.7
-2.29
2.45
0.77
1.01
0.9
1.16
-0.89
0.87
0.6
0.58
0.81
0.42
0.61
0.58
-0.33
0.05
0.46
-0.48
0.45
-1.36
1.33
2.14
-0.83
-1.33
-0.64
0.4
1.3
0.18
0.42
3.46
2.83
-0.29
1.51
1.32
1.99
0.56
1.3
0.69
0.36
3.26
-0.97
1.01
-0.24
-0.93
-1.39
-0.95
-0.32
1.44
-1.03
1.27
1.12
0.7
1.53
0.11
-1.45
0.93
-0.62
-0.44
0.31
-0.83
-0.33
0.16
1
0.09
-2.44
-1.7
1.64
0.36
-0.03
1.19
2.99
2.28
-1.8
-0.97
-2.2
-0.22
1.09
2.6
-2.42
-2.18
2.98
-0.73
1.51
-0.28
3.41
1.67
-0.89
-2.91
0.54
-2.56
-0.02
-0.92
0.25
0.23
0.25
0.21
0.27
0.27
0.31
0.31
0.28
0.37
0.37
0.38
0.37
0.44
0.42
0.4
0.46
0.44
0.54
0.47
0.45
0.47
0.43
0.47
0.42
0.49
0.43
0.39
0.39
0.46
0.42
0.4
0.45
0.41
0.44
0.42
0.41
0.46
0.45
0.39
0.43
0.43
0.49
0.37
0.43
0.41
0.44
-4.71
2.28
0.02
-0.29
-1.33
0.41
-2.15
-0.82
0.19
1.56
1.32
1.43
-0.2
3.5
-1.82
-0.36
0.39
1.8
-0.44
2.9
2.53
0.09
2.71
4.11
-0.64
2.54
0.56
0.57
-1.87
-0.91
1.55
1.01
-0.11
-0.05
2.7
3.91
-2.23
0.61
1.95
-2.05
0.96
4.92
-5.16
2.62
3.81
-2.55
1.45
199710
199711
199712
199801
199802
199803
199804
199805
199806
199807
199808
199809
199810
199811
199812
199901
199902
199903
199904
199905
199906
199907
199908
199909
199910
199911
199912
200001
200002
200003
200004
200005
200006
200007
200008
200009
200010
200011
200012
200101
200102
200103
200104
200105
200106
200107
200108
-3.8
2.98
1.32
0.15
7.04
4.76
0.73
-3.07
3.18
-2.46
-16.08
6.15
7.13
6.1
6.16
3.5
-4.08
3.45
4.33
-2.46
4.77
-3.49
-1.38
-2.79
6.12
3.37
7.72
-4.74
2.45
5.2
-6.4
-4.42
4.64
-2.51
7.03
-5.45
-2.76
-10.72
1.19
3.13
-10.05
-7.26
7.94
0.72
-1.94
-2.13
-6.46
-0.51
-5.03
-2.01
-1.41
-0.02
-0.65
-0.02
-3.11
-3.68
-5.32
-5.02
-0.84
-3.51
0.64
-1.51
-0.75
-5.15
-4.29
4.66
3.73
2.25
2.53
-1.93
2.56
-7.09
5.9
5.42
4.15
18.32
-14.91
-5.55
-3.68
10.39
-0.95
-1.1
0.23
-2.83
-0.46
3.17
5.8
2.67
2.32
-0.63
3.58
6.61
-2.91
2.73
1.95
0.79
3.45
-1.45
-0.13
1.05
0.74
4.16
-2.33
-0.96
3.4
-3.31
-2.21
-3.15
-4.46
-4.03
1.4
-2.65
2.53
2.4
-3.6
-0.76
-1.31
-3.4
-2.88
-6.51
-8.74
-0.29
-9.93
7.38
8.61
2.56
-9.86
8.06
-0.66
6.13
5.64
11.26
7.37
-4.86
12.87
6.46
-4.72
3.18
-1.03
5.57
2.51
1.09
3.07
1.19
0.46
-0.86
-1.02
-2.13
0.96
-0.52
1.82
3.47
-1.51
0.59
-1.24
-0.75
-2.69
-1.71
-4.2
-2.42
1.09
1.32
0.48
-0.29
-0.74
-1.71
-3.85
-7.91
-6.05
-18.33
11.68
7.55
4.63
-6.83
6
-3.07
3.08
9.66
13.33
1.85
-4.44
9
3.41
-2.72
0.18
2.03
7.16
3.95
1.87
1.66
1.92
-0.85
-2.4
-0.47
-0.23
2.54
-2.93
0.45
5.85
-2.91
0.28
-1.15
-3.33
-6.86
4.09
-1.39
0.92
3.36
-3.37
3.11
0.31
-1.06
-1.25
-1.58
-5.29
4.73
-0.51
-1.05
5.27
0.74
-3.07
2.99
0.61
6.43
4.68
8.51
5.67
-6.55
9.56
3.93
-3.95
2.18
-1.8
3.01
6.5
0.42
0.39
0.48
0.43
0.39
0.39
0.43
0.4
0.41
0.4
0.43
0.46
0.32
0.31
0.38
0.35
0.35
0.43
0.37
0.34
0.4
0.38
0.39
0.39
0.39
0.36
0.44
0.41
0.43
0.47
0.46
0.5
0.4
0.48
0.5
0.51
0.56
0.51
0.5
0.54
0.38
0.42
0.39
0.32
0.28
0.3
0.31
-0.38
0.29
3.95
0.11
-1.15
2.15
0.78
1.88
7.26
3.68
1.88
-0.71
-5.36
1.16
8.99
3.04
-0.16
-1.29
-9.06
-5.28
4.88
1.51
2.92
6.46
5.47
5.62
13.19
1.86
18.36
-6.39
-8.58
-9.08
16.59
-0.11
5.7
2.2
-4.63
-2.4
6.7
-25.05
12.51
8.35
-7.97
2.12
0.34
5.47
5.54
200109
200110
200111
200112
200201
200202
200203
200204
200205
200206
200207
200208
200209
200210
200211
200212
200301
200302
200303
200304
200305
200306
200307
200308
200309
200310
200311
200312
200401
200402
200403
200404
200405
200406
200407
200408
200409
200410
200411
200412
200501
200502
200503
200504
200505
200506
200507
-9.25
2.46
7.54
1.61
-1.44
-2.29
4.24
-5.2
-1.38
-7.21
-8.18
0.5
-10.35
7.84
5.96
-5.76
-2.57
-1.88
1.09
8.22
6.05
1.42
2.35
2.34
-1.24
6.08
1.35
4.29
2.15
1.4
-1.32
-1.83
1.17
1.86
-4.06
0.08
1.6
1.43
4.54
3.43
-2.76
1.89
-1.97
-2.61
3.65
0.57
3.92
-5.69
5.42
-0.3
5.14
1.19
-0.46
4.28
6.66
-3.07
3.78
-6.24
-1.27
3.07
-4.12
2.9
0.5
0.81
-0.89
0.55
1.06
4.83
1.68
4.73
2.54
0.52
2.64
2.21
-2.69
2.54
-0.91
2.11
-2.19
-0.4
2.57
-3.02
-1.27
3.29
0.29
4.13
0.01
-1.11
-0.3
-1.4
-4.02
2.72
3.26
2.83
1.6
-8.1
2.01
1.1
3.33
2.5
1.1
3.92
1.68
0.12
-3.44
2.52
1.32
-5.45
-1.12
2.23
-0.93
-1.45
-2.08
1.03
-0.29
0.68
-1.13
2.03
0.01
1.77
1.85
2.41
1.97
0.5
0.22
-2.62
-0.39
1.39
4.12
1.02
-0.25
-0.62
1.8
-0.07
1.96
1.64
1.59
-0.35
-0.81
2.63
-0.51
4.96
-2.66
-3.76
0.3
4.35
7.62
-1.36
4.57
2.27
3.62
4.19
1.15
3.24
-3.28
-9.13
6.04
-0.63
1.04
1.8
-4.57
-6.92
0.55
-4.17
-2.46
1.34
-1.59
0.08
0
-3.53
2.28
1.5
3.31
-0.97
1.12
5.08
1.3
-1.34
-0.07
-1.06
-1.26
3.05
1.2
0.47
0.96
-1.31
0.98
-1.19
3.27
-4.64
-1.66
-0.29
2.84
5.12
0.61
5.41
2.42
2.51
-0.72
-1.61
-2.28
0.92
5.12
-1.59
0.81
-0.55
-0.69
1.14
3.22
-0.33
1.87
2.2
0.31
1.55
1.67
0.97
3.4
-1.34
-1.04
-2.8
-0.08
-0.41
-1.62
-1.43
-1.89
0.4
-0.22
0.51
-1.37
-0.06
1.11
-0.9
0.28
-0.53
-0.98
0.28
0.22
0.17
0.15
0.14
0.13
0.13
0.15
0.14
0.13
0.15
0.14
0.14
0.14
0.12
0.11
0.1
0.09
0.1
0.1
0.09
0.1
0.07
0.07
0.08
0.07
0.07
0.08
0.07
0.06
0.09
0.08
0.06
0.08
0.1
0.11
0.11
0.11
0.15
0.16
0.16
0.16
0.21
0.21
0.24
0.23
0.24
11.54
-8.41
-8.58
-0.02
3.68
6.76
-1.66
7.95
2.97
6.15
3.34
1.76
9.09
-5.3
-16.17
9.64
1.61
1.25
1.61
-9.46
-10.77
-1.06
-0.31
-0.56
-0.19
3.73
1.62
-5.71
2.6
-1.1
0.21
-5.39
1.6
2.1
-2.29
-1.55
5.25
-1.52
3.23
-2.84
3.2
3.16
0.54
-0.87
0.44
2.05
-0.01
200508
200509
200510
200511
200512
200601
200602
200603
200604
200605
200606
200607
200608
200609
200610
200611
200612
200701
200702
200703
200704
200705
200706
200707
200708
200709
200710
200711
200712
200801
200802
200803
200804
200805
200806
200807
200808
200809
200810
200811
200812
200901
200902
200903
200904
200905
200906
-1.22
0.49
-2.02
3.61
-0.25
3.04
-0.3
1.46
0.73
-3.57
-0.35
-0.78
2.03
1.84
3.23
1.71
0.87
1.4
-1.96
0.68
3.49
3.24
-1.96
-3.73
0.92
3.22
1.8
-4.83
-0.87
-6.36
-3.09
-0.93
4.6
1.86
-8.44
-0.77
1.53
-9.24
-17.23
-7.86
1.74
-8.12
-10.1
8.95
10.19
5.21
0.43
-0.86
-0.31
-1.41
0.8
-0.27
5.77
-0.45
3.45
-0.83
-2.99
-0.23
-3.69
0.44
-1.43
1.93
0.81
-0.8
0.08
1.4
0.07
-2.02
0.28
0.76
-2.87
-0.34
-2.41
0.03
-2.81
0.18
-0.52
-0.53
0.75
-1.15
3.22
1.15
3.66
3.42
0.35
-3.2
-3.88
3.37
-2.01
-1.17
0.67
6.71
-2.33
2.32
1.27
0.76
0.23
-1.19
0.44
1.12
-0.25
0.6
2.6
2.55
0.88
2.94
-1.71
0.05
-0.04
0.07
3.15
-0.11
-0.09
-0.22
-1.15
-0.04
-1.12
-3.33
-2.24
-1.86
-2.6
-1.18
-0.51
3.65
-0.94
-0.14
-0.95
-1.38
-2.42
5.88
1.52
6.33
-2.89
-6.02
-0.25
-11.18
-7.27
3.56
5.53
-0.23
-2.72
-2.23
0.49
-0.58
-0.6
0.12
-0.94
-0.7
-0.04
1.15
0.96
1.38
1.54
-1.74
0.88
-0.22
0.09
-0.8
0.22
-0.47
0.25
1.02
1.13
0.53
0.57
-0.91
-0.58
-0.31
1.88
0.7
1.98
0.76
1.09
1.28
0.81
4.3
-0.98
1.85
2.95
3.6
4.83
-0.08
0.23
1.71
-2.33
0.28
-0.98
-1.06
0.45
-0.56
-1.26
-1.19
0.24
-0.54
2
-0.5
-0.25
1.34
-0.11
0.93
2.15
0.53
0.26
-0.88
2.05
0.24
-0.74
-0.59
1.01
-1.29
0.07
-1.04
-0.56
-3.17
-0.1
-0.34
-1.09
2.2
-1.06
0.47
-2.54
0.04
-0.42
1.06
0.81
1.77
1.91
2.72
-1.4
-1.15
-1.12
-2.22
0.14
-2.16
-0.17
0.3
0.29
0.27
0.31
0.32
0.35
0.34
0.37
0.36
0.43
0.4
0.4
0.42
0.41
0.41
0.42
0.4
0.44
0.38
0.43
0.44
0.41
0.4
0.4
0.42
0.32
0.32
0.34
0.27
0.21
0.13
0.17
0.18
0.18
0.17
0.15
0.13
0.15
0.08
0.03
0
0
0.01
0.02
0.01
0
0.01
2.27
3.46
-1.29
0.25
0.77
2.68
-1.84
1.26
0.61
-3.65
1.48
-2.22
-3.46
-0.94
-0.23
-1.01
0.84
0.21
-1.35
2.49
-0.16
-0.27
0.37
2.8
0.11
4.59
4.96
0.93
6.52
-7.89
6.11
4.1
-0.2
3.21
12.53
-5.14
-4.04
0.35
7.79
7.1
-5.08
-1.84
4.16
-11.38
-34.39
-12.44
5.29
200907
200908
200909
200910
200911
200912
201001
201002
201003
201004
201005
201006
201007
201008
201009
201010
201011
201012
201101
201102
201103
201104
201105
201106
201107
201108
201109
201110
201111
201112
201201
201202
201203
201204
201205
201206
201207
201208
201209
201210
201211
201212
201301
201302
201303
201304
201305
7.72
3.33
4.08
-2.59
5.56
2.75
-3.36
3.4
6.31
2
-7.89
-5.56
6.93
-4.77
9.54
3.88
0.6
6.82
1.99
3.49
0.45
2.9
-1.27
-1.75
-2.36
-5.99
-7.59
11.35
-0.28
0.74
5.05
4.42
3.11
-0.85
-6.19
3.89
0.79
2.55
2.73
-1.76
0.78
1.18
5.57
1.29
4.03
1.55
2.8
2.44
-0.05
2.74
-4.78
-2.8
6.22
0.29
1.46
1.79
5.04
0.06
-2.49
0.09
-3.1
3.72
0.78
3.6
0.95
-2.42
1.64
2.67
-0.51
-0.69
0.11
-1.34
-3.2
-3.78
3.57
-0.26
-0.44
2.16
-1.63
-0.45
-0.59
-0.09
0.95
-2.68
0.44
0.66
-0.81
0.36
1.87
0.56
-0.38
0.83
-2.3
2.06
5.28
7.75
0.93
-4.18
-0.17
0
0.3
3.16
2.11
2.81
-2.38
-4.5
-0.26
-1.95
-3.13
-2.6
-0.9
3.82
0.82
1.1
-1.58
-2.52
-2.08
-0.32
-1.21
-2.48
-1.41
-0.18
-0.34
1.77
-1.13
0.08
0.92
-0.48
-0.59
0.44
-0.27
1.31
1.53
3.79
-0.97
3.58
0.95
0.03
-0.3
0.62
2.6
-0.66
-3.02
1.36
4.18
0.81
0.48
-1.39
-0.33
-0.56
1.1
1.37
-0.21
0.24
0.72
-0.21
1.3
0.24
-3.65
-0.89
-1.93
1.64
1.09
2.02
2.38
2.43
3.2
1.85
-1.87
1.79
0.8
-1.81
-0.19
-0.33
1.12
2.28
-1.24
1.11
-1.26
-1.38
-1.39
0.68
-1.98
-1.64
-0.69
0.17
0.07
-1.66
3.2
3.18
0.38
-1.66
0.13
-0.07
0.46
1.4
1.7
1.69
-0.22
-1.51
2.02
-1.69
0.47
-0.23
1.64
3.22
0.69
0.95
-0.03
-0.84
-1.5
-1.45
-1.76
-0.24
0.28
-0.88
1.53
2.4
-1.4
-0.1
0.82
0.67
2.39
0.34
0.13
-0.73
1.59
2.28
0.91
0.86
1.47
0.48
1.31
0.47
-0.78
0.01
0.01
0.01
0
0
0.01
0
0
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0
0
0
0
0.01
0
0
0
0
0
0
0
0
0.01
0
0
0.01
0.01
0.01
0.01
0.01
0
0
0
0
0
-5.36
-8.84
-4.93
2.66
0.29
2.91
-5.38
3.6
3.66
3.22
-0.13
-3
1.88
-0.19
1.35
1.64
2.54
-3.19
-0.32
2.03
3.51
0.06
-0.58
1.77
0.02
-0.33
-2.57
-1.42
3.98
1.95
-7.96
-0.28
1.49
3.84
6.59
-0.72
3.16
-2.48
-1.04
0.1
0.34
-2.97
-1.86
1.32
1.97
0.27
-1.78
201306
201307
201308
201309
201310
201311
201312
201401
201402
201403
201404
201405
201406
201407
201408
201409
201410
201411
201412
201501
201502
201503
201504
201505
201506
201507
201508
201509
201510
201511
201512
201601
201602
201603
201604
201605
201606
201607
201608
201609
201610
201611
201612
201701
201702
201703
201704
-1.2
5.65
-2.71
3.77
4.18
3.12
2.81
-3.32
4.65
0.43
-0.19
2.06
2.61
-2.04
4.24
-1.97
2.52
2.55
-0.06
-3.11
6.13
-1.12
0.59
1.36
-1.53
1.54
-6.04
-3.08
7.75
0.56
-2.17
-5.77
-0.07
6.96
0.92
1.78
-0.05
3.95
0.5
0.25
-2.02
4.86
1.82
1.94
3.57
0.17
1.09
1.35
1.8
-0.01
2.67
-1.53
1.33
-0.53
0.57
0.15
-1.17
-4.15
-1.89
3.1
-4.25
0.29
-3.79
3.78
-2.31
2.88
-0.87
0.21
3.04
-3.03
0.77
2.86
-4.55
0.4
-2.78
-2.16
3.35
-2.99
-3.42
0.95
1.09
1.17
-0.71
0.49
2.64
1.72
1.74
-4.01
6.81
0.41
-1.28
-2.13
0.78
0.49
-0.17
0.56
-2.78
-1.19
1.14
0.24
-0.31
-2.09
-0.4
5.09
1.14
-0.27
-0.74
0.01
-0.59
-1.23
-1.7
-3
2.06
-3.48
-1.81
-0.46
1.85
-1.37
-0.79
-4.12
2.66
0.53
-0.07
-0.51
-2.59
2.08
-0.5
1.16
3.26
-1.81
-1.47
-1.11
3.34
-1.49
4.16
8.29
3.58
-2.78
-1.79
-3.17
-1.87
-0.44
-1.46
0.62
-0.73
2.7
0.14
-0.52
-3.99
-0.26
2.16
3.48
0.1
-1.97
0.94
-0.63
1.13
-0.47
1.4
-1.16
1.67
-1.07
0.1
-0.15
-1.72
0.5
0.04
0.76
1.81
0.82
-2.57
0.31
2.6
3.28
0.81
-2.88
-1.01
1.19
1.33
-1.33
-2.29
1.19
-0.1
0.99
-0.01
0.78
0.68
2
-0.01
0.56
-2.16
-1.32
0.87
0.05
0.07
-1.4
-0.41
1.9
1.07
-1.08
-1.91
0.47
-0.7
-0.51
-0.17
0.18
0.88
-1.67
-1.75
-0.51
-0.49
-0.74
-1.47
-2.56
1.25
-0.51
0.43
-1.1
0.13
3.02
2.05
-0.02
1.97
-2.55
1.92
-1.24
-0.39
-0.08
0.24
3.7
-0.3
-0.95
-1.72
-1
-1.55
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.01
0.01
0.02
0.02
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.01
0.03
0.04
0.04
0.03
0.05
0.59
1.75
0.05
2.96
0.15
0.4
0.04
1.7
2.07
-3.34
-3.86
1.15
0.67
-0.25
0.8
0.52
0
1.06
1.09
3.84
-2.83
3.02
-7.39
6.01
3.03
10.33
-2.12
5.33
-4.04
2.32
3.45
1.33
-4.11
-5.13
-6.27
1.92
4.22
-2.99
-3.12
-0.58
0.65
-4.15
-0.42
-0.94
-1.65
-0.92
0.55
201705
201706
201707
201708
201709
201710
201711
201712
1.06
0.78
1.87
0.16
2.51
2.25
3.12
1.06
-3.05
2.48
-1.59
-1.85
4.84
-1.97
-0.43
-1.04
-3.78
1.35
-0.29
-2.24
3.03
-0.05
-0.04
0.14
1.21
-2.01
-0.71
0.28
-1.18
1.04
3.12
0.65
-1.88
-0.07
-0.15
-2.42
1.63
-3.33
0
1.65
0.06
0.06
0.07
0.09
0.09
0.09
0.08
0.09
1.45
-0.26
1.63
3.52
-1.17
4.3
-0.73
-1.65
With growth of $1
Date
Mkt-RF
196307
196308
196309
196310
196311
196312
196401
196402
196403
196404
196405
196406
196407
196408
SMB
HML
RMW
0.61
3.7027
-0.3477
2.1533
0.0915
1.7263
1.9764
1.5494
1.4701
0.671
1.4762
1.3847
1.6714
-0.2684
0.53
0.1113
0.2756
-0.1537
0.0848
-0.4717
0.5724
0.6996
1.2773
-0.2756
0.1696
0.5777
0.8109
0.689
0.17
0.4539
0.2006
0.153
0.4607
0.1496
0.4403
0.6511
0.7344
0.0765
0.5066
0.2856
0.2856
0.1853
1.66
2.3074
0.3984
6.225
0.913
1.7928
2.0252
1.7596
-1.6766
-0.581
1.2284
0.9628
1.8924
1.7596
196409
196410
196411
196412
196501
196502
196503
196504
196505
196506
196507
196508
196509
196510
196511
196512
2.2509
0.9699
0.61
0.6283
2.7694
0.8784
-0.2074
2.5071
0.1403
-2.7511
1.4823
2.2753
2.3546
2.196
0.5917
1.2261
0.3657
0.9964
0.3869
0.2226
1.8497
2.2843
1.6059
1.1554
0.5618
-1.749
1.0971
1.9716
0.8586
2.3426
3.2807
1.9292
0.4505
0.3638
-0.1666
-0.2635
0.2006
0.2074
0.3553
0.2907
-0.1071
0.2635
0.5406
0.0068
0.153
0.4284
0.2057
0.5015
0.8632
1.1786
2.656
3.5358
3.0544
2.324
1.162
2.0916
1.0458
1.8592
-0.6806
5.0796
0.2988
3.6852
-0.0996
-0.4648
196601
196602
196603
196604
196605
196606
196607
1.0492
-0.1281
-0.9211
1.9154
-2.8426
-0.2684
-0.3843
3.0316
3.1058
0.6466
2.3108
-2.2154
1.2402
0.265
0.7633
0.2159
-0.1819
0.0782
-0.0935
0.255
0.3332
-3.0378
1.411
3.818
2.3406
4.4156
1.7596
0.913
196608
196609
196610
196611
196612
196701
196702
196703
196704
-4.2151
-0.0366
2.9646
1.464
0.6893
5.5815
1.0858
3.0439
2.9829
-1.0759
-0.0954
-2.9309
2.3161
1.5423
5.3
2.1359
1.537
0.7738
0.2499
0.2601
0.6579
-0.5882
-0.0374
0.5474
-0.1989
0.2227
-0.2788
1.743
-1.079
-4.3824
8.5822
2.7888
2.6892
4.8804
3.154
5.6938
196705
196706
196707
196708
196709
196710
196711
196712
196801
196802
196803
196804
196805
196806
196807
196808
196809
196810
196811
196812
196901
196902
196903
196904
196905
196906
196907
196908
196909
196910
196911
196912
197001
197002
197003
197004
-2.0313
2.0801
3.4038
0.0671
2.5071
-1.2749
0.8357
2.4705
-1.8666
-1.6775
0.732
6.1305
2.0008
1.0309
-1.0492
1.4274
3.0683
0.8662
3.9223
-1.7934
-0.1525
-2.9524
2.2204
1.5006
0.549
-3.7698
-3.66
3.4648
-1.2078
3.6966
-1.7019
-0.9943
-4.331
3.7393
-0.0366
-6.1
1.7861
3.9538
2.3744
0.9328
1.8338
0.7791
0.4982
3.5775
2.9097
-1.0123
-0.2756
3.8425
4.2612
0.3922
-0.1749
1.7437
1.9981
0.3021
1.8073
2.438
0.2862
-1.6536
0.3127
0.0901
0.4346
-2.3797
-1.2561
0.9063
1.2137
2.5917
-0.7473
-1.431
2.1677
-0.8374
-0.742
-2.809
0.306
0.3332
0.6205
0.4182
-0.2499
-0.4063
-0.1207
0.1037
0.9775
0.3689
0.0697
-0.0051
0.3128
0.2839
1.1016
0.34
0.2108
0.6613
0.017
0.1734
0.4573
0.323
0.0918
0.1751
0.2941
-0.0153
0.4114
-0.4879
-0.3723
-0.3723
-0.0204
-0.3434
0.6868
0.8568
0.8925
1.2563
-1.245
0.5976
2.5066
2.3572
2.0252
3.0046
3.8014
0.581
-5.9428
1.4276
3.6022
6.2914
2.3074
-0.5478
-3.32
0.664
-1.7098
-0.498
2.4236
-1.2948
-0.8964
5.2622
-0.5644
2.2908
0.0498
8.7316
3.984
3.5856
7.3372
1.6766
4.0504
5.7602
-1.2782
-2.1248
0.2158
0.5976
197005
197006
197007
197008
197009
197010
197011
197012
197101
197102
197103
197104
197105
197106
197107
197108
197109
197110
197111
197112
197201
197202
197203
197204
197205
197206
197207
197208
197209
197210
197211
197212
197301
197302
197303
197304
197305
197306
197307
197308
197309
197310
197311
197312
197401
197402
197403
-3.6112
-2.9219
4.8373
3.3489
3.1598
-0.7808
3.4099
4.0992
3.5624
1.4701
3.1293
2.5315
-1.8178
0.549
-2.135
2.9219
0.0915
-2.0862
0.3294
5.9231
2.1289
2.3607
0.9943
0.7869
1.3725
-0.8723
0.122
2.5986
-0.0854
0.9272
3.416
0.9882
-1.3969
-2.3485
-0.183
-2.8548
-1.1834
-0.3416
3.6905
-1.7202
3.5075
0.1037
-7.1675
0.9821
0.5063
0.3233
-1.1041
-1.8126
-0.6201
0.1643
1.3462
5.0297
-1.855
-1.59
2.1306
4.5262
1.6165
1.6695
0.3339
-0.0901
-0.2385
-0.2173
0.4346
0.6307
-0.3445
-1.007
2.2631
3.8849
1.0388
0.2703
0.6095
-1.1395
0.3445
-0.9222
-1.3409
-0.6625
-0.8109
0.2491
-0.4346
-0.9487
-1.5953
-0.6943
-1.06
-2.6394
-0.8162
4.3725
-0.3975
2.4062
0.3922
-3.3125
-1.908
6.0473
0.6148
1.9292
0.782
0.3179
0.3332
0.3451
-0.7786
0.2159
0.4471
0.3417
0.4046
-0.0493
-0.5168
0.2924
-0.0646
-0.1802
0.1989
0.6239
-0.3332
0.0969
-0.119
0.1105
0.5083
-0.2992
-0.1241
0.2091
-0.2873
-0.2567
0.3026
0.9673
0.2499
0.4029
0.9792
-0.2159
0.6256
0.459
0.6426
1.1373
0.2057
0.4148
-0.7327
0.3638
0.5406
0.4658
0.8568
0.8653
1.1883
0.6018
0.1513
-0.498
1.9754
1.5438
2.6062
2.0584
4.6978
4.565
1.8924
-1.9754
2.739
4.9302
-0.9296
4.0836
4.1832
2.4568
1.0956
5.9594
4.1168
5.727
0.9462
-0.7968
4.399
4.2828
0.6806
5.5278
4.565
3.4528
-1.6932
4.4322
1.4608
-1.6268
5.976
2.324
1.0956
-0.1162
-1.0458
5.0132
1.2118
1.494
-0.5976
-2.158
-1.5604
-2.8386
-2.988
-3.3366
-1.5438
6.3412
197404
197405
197406
197407
197408
197409
197410
197411
197412
197501
197502
197503
197504
197505
197506
197507
197508
197509
197510
197511
197512
197601
197602
197603
197604
197605
197606
197607
197608
197609
197610
197611
197612
197701
197702
197703
197704
197705
197706
197707
197708
197709
197710
197711
197712
197801
197802
-2.6169
-2.2448
-1.1163
-4.3005
-5.0935
-6.5697
10.431
-2.1411
-1.4945
8.9426
4.0016
2.2326
3.1903
3.7759
3.5563
-3.4099
-1.1285
-1.9886
3.8491
2.2204
-0.366
8.0276
0.8052
2.0252
-0.2989
-0.2074
3.0805
-0.0427
0.2684
1.8727
-0.8662
0.8296
4.0565
-1.8605
-0.5734
-0.2257
0.7015
-0.2745
3.4831
-0.4209
-0.4575
0.4453
-2.0618
3.05
0.7747
-3.0561
-0.2318
0.1961
-1.1501
0.5194
1.5847
0.689
1.3038
-3.1005
-0.2332
-1.7808
7.3087
0.1855
2.6394
0.2173
2.0617
1.2296
2.3426
-0.9487
0.5406
-1.7119
-0.0371
0.5141
3.8955
4.7541
-0.1802
0.6042
-0.0636
-0.0689
0.8586
-0.5459
0.5936
0.6148
1.9345
2.4433
3.657
1.1183
1.2137
0.8427
1.2084
1.6271
1.4946
1.0017
1.3568
1.325
2.4486
1.378
1.9557
2.4751
0.3502
-0.1768
0.3043
1.0438
0.595
1.1033
-1.5283
0.1428
0.17
1.6048
-0.6052
0.5933
-0.0187
-0.5134
0.3944
0.459
0.017
0.2278
0.221
0.5083
0.4692
1.6286
1.1526
0.1632
0.1598
-0.0544
0.2856
0.4658
0.3043
0.1207
0.1479
0.4267
0.5474
0.8942
0.255
0.3434
0.7565
0.3128
0.0612
0.0697
-0.3043
0.0867
0.4624
0.2227
0.1071
0.7327
0.2992
6.4076
10.0928
2.6892
-3.7516
1.2284
-5.4448
1.6102
-3.9674
0.581
0.1826
3.569
3.7018
3.9342
-0.083
-2.6892
2.3738
3.3698
2.5398
0.8964
0.6308
1.4608
-1.494
-2.6228
1.0292
2.3074
5.7602
0.6806
-0.1494
0.8964
3.3034
1.2948
-0.6474
0.6308
0.7802
1.4442
1.1288
-1.7098
2.2078
3.1208
3.0378
3.2536
3.7018
1.162
1.577
3.2204
-1.1122
2.2576
197803
197804
197805
197806
197807
197808
197809
197810
197811
197812
197901
197902
197903
197904
197905
197906
197907
197908
197909
197910
197911
197912
198001
198002
198003
198004
198005
198006
198007
198008
198009
198010
198011
198012
198101
198102
198103
198104
198105
198106
198107
198108
198109
198110
198111
198112
198201
2.3485
5.4168
1.6836
-0.4209
3.7271
2.8975
-0.2623
-6.6551
2.2631
1.1468
3.1903
-1.5616
4.0748
0.5734
-0.7381
2.9585
1.1102
3.9833
0.1098
-4.331
3.7881
1.7019
3.9711
-0.1342
-7.259
3.0317
3.8186
2.4766
4.5689
1.708
1.9459
1.2566
6.4599
-2.1472
-2.4644
0.9577
2.7816
-0.6771
0.6771
-0.8296
-0.3294
-3.6844
-3.7637
3.6112
2.6596
-1.6165
-1.3664
2.5016
0.3657
2.9468
1.3727
0.6095
3.1429
0.3816
-4.7965
2.0405
1.0971
2.5652
0.8215
2.2048
1.802
0.795
1.0547
1.1872
1.5741
0.3869
-1.3038
1.8232
2.8302
1.537
-0.2597
-3.1641
1.06
1.643
1.3091
2.6129
2.7878
0.8374
1.7649
-1.272
0.3657
2.3161
0.2703
2.1624
2.968
1.8285
0.0159
-0.5141
-0.4293
-0.7897
1.7172
-0.2014
1.1607
-0.0954
0.374
-0.4318
0.0646
0.2703
-0.0187
0.0918
0.4879
0.4012
-0.2074
-0.2023
0.5559
0.374
0.0561
0.3485
0.4964
0.4216
0.459
-0.0935
0.0221
-0.1462
-0.3825
-0.1666
0.476
0.2754
-0.0102
0.3502
0.2363
0.0187
-0.901
-0.2788
-0.6443
-0.2958
-1.2495
0.6256
1.3328
0.3349
0.2839
0.5542
0.0969
1.0421
0.0578
0.9928
1.054
-0.5457
0.493
0.2958
0.7089
0.664
6.3412
2.1082
-0.7304
4.2828
3.9674
0.5312
1.9422
3.4694
4.9136
-2.49
-0.2324
2.6892
3.403
0.1992
-1.0126
1.328
4.2828
3.3532
3.3864
1.8426
0.5478
-1.1122
1.8426
3.9508
-1.8094
2.1746
1.5106
8.2004
5.146
5.0298
4.4322
9.1798
-0.415
-4.2164
1.9754
-2.2244
3.0212
2.2078
-0.581
3.4528
1.245
1.6268
7.1214
1.826
1.9754
-0.8632
198202
198203
198204
198205
198206
198207
198208
198209
198210
198211
198212
198301
198302
198303
198304
198305
198306
198307
198308
198309
198310
198311
198312
198401
198402
198403
198404
198405
198406
198407
198408
198409
198410
198411
198412
198501
198502
198503
198504
198505
198506
198507
198508
198509
198510
198511
198512
-2.9646
-0.5307
2.6047
-1.8239
-1.2749
-1.3359
7.4054
1.3969
7.503
3.4587
0.9455
2.806
2.1899
2.3302
4.6787
0.9272
2.4827
-1.8727
0.305
1.1651
-1.4884
1.9276
-0.4758
-0.5612
-2.3302
0.9943
0.2989
-3.0317
1.7202
-1.0614
6.8808
0.122
0.0976
-0.4636
1.7324
5.4839
1.3542
0.0976
0.0244
3.7149
1.3847
0.1586
-0.0122
-2.1594
3.0622
4.5628
2.9768
0.7261
0.53
1.1395
0.8268
0.2597
1.0335
-1.7437
1.8921
1.5476
2.9309
0.5247
2.2684
2.067
1.2826
0.7897
3.8266
1.1395
1.0494
-1.7702
0.6625
-1.4946
1.5264
0.2809
0.4929
-0.3286
0.3816
-0.0053
0.5724
0.5671
-0.6466
0.371
0.53
-0.2279
0.0106
0.1643
2.3797
1.06
-0.2544
0.477
-0.7049
0.8003
2.0617
0.3604
-0.4293
-0.2915
0.53
0.3233
1.207
0.8126
-0.3026
0.4777
0.4318
0.1955
0.3672
0.2278
-0.4556
-0.1632
0.1717
0.0238
0.2839
0.5219
0.272
-0.068
-0.4879
1.1254
1.1135
0.3553
1.0302
0.0646
0.4539
1.4671
0.7412
0.2516
0.3893
0.2142
-0.272
0.2533
-0.1445
1.0744
0.2567
0.8636
0.1411
-0.7531
0.1496
0.867
0.8041
0.0238
0.2992
-0.1071
0.5576
0.3944
0.3026
-0.3179
-0.0901
-3.9508
-0.7304
4.1666
3.1706
1.7098
3.3698
-1.5936
5.1792
2.158
-0.083
1.5438
-0.83
0.7138
1.5438
1.3944
-1.411
5.8432
1.6102
2.407
3.6686
0.3154
0.8632
4.2994
0.1162
3.0212
0.1162
7.0882
5.5112
6.7728
7.7854
0.1826
3.984
3.6022
3.0046
3.6686
0.2822
3.7516
3.6188
4.2994
3.818
4.0504
0.913
1.5936
3.5856
3.1872
2.1414
3.2702
198601
198602
198603
198604
198605
198606
198607
198608
198609
198610
198611
198612
198701
198702
198703
198704
198705
198706
198707
198708
198709
198710
198711
198712
198801
198802
198803
198804
198805
198806
198807
198808
198809
198810
198811
198812
198901
198902
198903
198904
198905
198906
198907
198908
198909
198910
198911
1.0065
4.9593
3.5868
-0.1891
3.4282
1.2383
-3.3245
4.3127
-4.636
3.4526
1.3237
-1.3847
8.2167
3.2879
1.6104
-0.6771
0.6771
3.0134
2.9585
2.7572
-0.9699
-13.5664
-4.1297
4.7641
3.1781
3.5075
-0.7747
0.9516
0.4331
3.5319
-0.1525
-1.4091
2.623
1.3115
-0.7869
1.5189
4.331
-0.7625
1.5677
3.2513
2.6535
-0.2135
5.002
1.4884
0.1464
-1.6287
1.2383
1.0653
0.1272
0.2279
2.0776
-0.1378
0.0689
-1.3462
-1.7808
1.5741
-0.7049
-0.4664
0.5671
-0.2703
2.3373
0.6413
-0.2915
0.2173
-0.6837
-0.0583
0.053
0.7261
-3.7577
2.0299
0.5777
0.2438
2.2896
3.8478
1.1236
-0.8427
1.6907
0.4399
0.5194
-0.1643
-1.0335
-0.3498
1.5953
-0.6519
1.9451
0.9328
0.159
0.5247
-0.0424
-1.6112
0.7579
0.7738
-1.2349
-0.159
0.2601
0.0102
0.0952
-0.3145
0.1513
0.408
0.9826
0.7684
0.7123
-0.0544
0.1598
0.2329
-0.3706
-0.8483
0.4522
0.1139
0.1921
0.3519
0.2822
0.017
0.2176
0.8891
0.7038
-0.5933
1.0336
-0.1105
0.2958
0.4573
0.561
-0.0119
0.5559
0.5151
0.0544
0.4607
0.3808
-0.0935
0.2567
0.3179
0.2482
-0.0765
0.0306
0.5423
-0.3128
0.2924
-0.0578
-0.0051
-0.0204
-1.6932
3.569
3.5192
6.4906
5.1128
4.6978
0.7138
-1.2284
1.577
1.8758
3.5192
3.0378
1.992
0.166
3.9176
0.8964
2.5232
4.5318
0.83
5.0298
0.166
4.9966
-1.5272
6.6566
-0.1992
4.2496
1.3944
1.2782
0.498
4.0172
0.6806
0.3984
4.5152
3.984
1.1454
2.7224
-0.0498
0.2988
1.6766
2.9216
2.4734
2.0584
4.9634
2.3406
3.984
1.66
0.1494
198912
199001
199002
199003
199004
199005
199006
199007
199008
199009
199010
199011
199012
199101
199102
199103
199104
199105
199106
199107
199108
199109
199110
199111
199112
199201
199202
199203
199204
199205
199206
199207
199208
199209
199210
199211
199212
199301
199302
199303
199304
199305
199306
199307
199308
199309
199310
1.3176
-4.1785
1.2871
1.7263
-1.4396
5.7462
-0.0549
-0.549
-5.5815
-3.1232
-0.5612
4.4835
2.1106
3.4709
4.9959
2.2265
0.4392
2.8365
-2.4034
3.1964
2.0252
-0.3599
1.3969
-1.9459
7.2224
0.2501
1.2749
-1.0126
1.2627
0.793
-0.8174
2.9097
-0.8418
1.3359
1.2322
3.1293
1.5433
1.1773
0.6832
2.013
-1.2505
2.3729
0.7991
0.4026
2.8731
0.5368
1.4701
-0.689
-0.1749
1.1554
1.3992
0.318
-0.742
1.2614
-1.2031
-1.5105
-1.4734
-2.1571
0.4982
0.8533
2.5758
2.6288
2.5546
0.7049
0.6201
0.6201
0.0212
1.2932
1.3515
1.0335
0.0689
-0.7526
5.4378
1.2455
0.053
-2.5069
0.6413
-0.9063
0.2067
0.3074
0.7844
1.6006
2.6023
1.3992
1.6377
-1.2826
0.5565
0.0742
1.5264
0.5883
1.007
0.6625
2.1518
1.3833
0.2176
0.3179
0.2737
-0.323
-0.2635
-0.4658
-0.1598
0.1683
0.4386
0.2941
0.2074
-0.3672
-0.0918
-0.1428
0.0782
-0.0391
0.4114
0.0731
0.3757
-0.0425
0.0374
0
0.0969
-0.1581
-0.5151
0.9367
1.2529
0.7905
0.9027
0.3876
0.748
0.0799
-0.0051
0.1343
-0.187
-0.0816
0.5984
1.1679
1.2614
0.3774
0.6137
-0.4097
0.6154
0.7225
0.0935
0.0952
-0.0918
1.494
-0.2158
1.3446
5.229
4.5152
4.399
0
1.2118
0.9462
1.3944
6.4242
3.0212
6.4076
4.233
1.2118
1.0126
2.5398
5.1626
4.565
4.3326
3.1208
-1.3446
-1.2118
3.4528
7.6028
-0.5478
1.8094
1.5272
4.4986
0.0664
1.6102
4.2662
7.9514
4.1998
3.8346
0.2656
0.9296
-1.2782
0.83
1.1786
-4.4654
1.6434
-0.0996
-1.9588
-0.249
2.9382
1.494
199311
199312
199401
199402
199403
199404
199405
199406
199407
199408
199409
199410
199411
199412
199501
199502
199503
199504
199505
199506
199507
199508
199509
199510
199511
199512
199601
199602
199603
199604
199605
199606
199607
199608
199609
199610
199611
199612
199701
199702
199703
199704
199705
199706
199707
199708
199709
-0.5429
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2.8054
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0.1162
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199710
199711
199712
199801
199802
199803
199804
199805
199806
199807
199808
199809
199810
199811
199812
199901
199902
199903
199904
199905
199906
199907
199908
199909
199910
199911
199912
200001
200002
200003
200004
200005
200006
200007
200008
200009
200010
200011
200012
200101
200102
200103
200104
200105
200106
200107
200108
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2.0842
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1.2682
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3.4694
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2.4236
0.2324
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3.2536
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21.0488
14.193
9.3458
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11.62
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6.7728
17.6956
23.7878
4.731
-5.7104
16.6
7.3206
-2.8552
1.9588
5.0298
13.5456
8.217
200109
200110
200111
200112
200201
200202
200203
200204
200205
200206
200207
200208
200209
200210
200211
200212
200301
200302
200303
200304
200305
200306
200307
200308
200309
200310
200311
200312
200401
200402
200403
200404
200405
200406
200407
200408
200409
200410
200411
200412
200501
200502
200503
200504
200505
200506
200507
-5.0325
2.1106
5.2094
1.5921
-0.2684
-0.7869
3.1964
-2.562
-0.2318
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0.915
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5.3924
4.2456
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1.2749
5.6242
4.3005
1.4762
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2.0374
-0.1464
4.3188
1.4335
3.2269
1.9215
1.464
-0.1952
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1.3237
1.7446
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0.6588
1.586
1.4823
3.3794
2.7023
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1.7629
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0.9577
3.0012
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0.371
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4.0598
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2.5334
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2.067
0.795
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0.0583
0.8215
1.0918
3.0899
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3.0369
1.8762
0.8056
1.9292
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1.8921
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0.5353
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0.371
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1.9716
2.2578
2.0299
0.442
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0.5117
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0.357
0.8364
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0.3944
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0.4845
0.5797
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0.255
0.2074
-0.2754
0.1037
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0.1275
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0.476
0.1581
0.5032
0.4488
0.4403
0.1105
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0.6171
0.0833
9.8936
-2.7556
-4.5816
2.158
8.881
14.3092
-0.5976
9.2462
5.4282
7.6692
8.6154
3.569
7.0384
-3.7848
-13.4958
11.6864
0.6142
3.3864
4.648
-5.9262
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2.573
-5.2622
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3.8844
-0.9794
1.7928
1.66
-4.1998
5.4448
4.15
7.1546
0.0498
3.5192
10.0928
3.818
-0.5644
1.5438
-0.0996
-0.4316
6.723
3.652
2.4402
3.2536
-0.5146
3.2868
-0.3154
200508
200509
200510
200511
200512
200601
200602
200603
200604
200605
200606
200607
200608
200609
200610
200611
200612
200701
200702
200703
200704
200705
200706
200707
200708
200709
200710
200711
200712
200801
200802
200803
200804
200805
200806
200807
200808
200809
200810
200811
200812
200901
200902
200903
200904
200905
200906
-0.1342
0.9089
-0.6222
2.8121
0.4575
2.4644
0.427
1.5006
1.0553
-1.5677
0.3965
0.1342
1.8483
1.7324
2.5803
1.6531
1.1407
1.464
-0.5856
1.0248
2.7389
2.5864
-0.5856
-1.6653
1.1712
2.5742
1.708
-2.3363
0.0793
-3.2696
-1.2749
0.0427
3.416
1.7446
-4.5384
0.1403
1.5433
-5.0264
-9.9003
-4.1846
1.6714
-4.3432
-5.551
6.0695
6.8259
3.7881
0.8723
0.0742
0.3657
-0.2173
0.954
0.3869
3.5881
0.2915
2.3585
0.0901
-1.0547
0.4081
-1.4257
0.7632
-0.2279
1.5529
0.9593
0.106
0.5724
1.272
0.5671
-0.5406
0.6784
0.9328
-0.9911
0.3498
-0.7473
0.5459
-0.9593
0.6254
0.2544
0.2491
0.9275
-0.0795
2.2366
1.1395
2.4698
2.3426
0.7155
-1.166
-1.5264
2.3161
-0.5353
-0.0901
0.8851
4.0863
-0.7049
1.7596
0.3859
0.2992
0.2091
-0.0323
0.2448
0.3604
0.1275
0.272
0.612
0.6035
0.3196
0.6698
-0.1207
0.1785
0.1632
0.1819
0.7055
0.1513
0.1547
0.1326
-0.0255
0.1632
-0.0204
-0.3961
-0.2108
-0.1462
-0.272
-0.0306
0.0833
0.7905
0.0102
0.1462
0.0085
-0.0646
-0.2414
1.1696
0.4284
1.2461
-0.3213
-0.8534
0.1275
-1.7306
-1.0659
0.7752
1.1101
0.1309
-0.2924
-2.0418
2.4734
0.6972
0.664
1.8592
0.0996
0.498
1.5936
3.569
3.2536
3.9508
4.2164
-1.2284
3.1208
1.2948
1.8094
0.332
2.0252
0.8798
2.075
3.3532
3.5358
2.5398
2.6062
0.1494
0.6972
1.1454
4.7808
2.822
4.9468
2.9216
3.4694
3.7848
3.0046
8.798
0.0332
4.731
6.557
7.636
9.6778
1.5272
2.0418
4.4986
-2.2078
2.1248
0.0332
-0.0996
200907
200908
200909
200910
200911
200912
201001
201002
201003
201004
201005
201006
201007
201008
201009
201010
201011
201012
201101
201102
201103
201104
201105
201106
201107
201108
201109
201110
201111
201112
201201
201202
201203
201204
201205
201206
201207
201208
201209
201210
201211
201212
201301
201302
201303
201304
201305
5.3192
2.6413
3.0988
-0.9699
4.0016
2.2875
-1.4396
2.684
4.4591
1.83
-4.2029
-2.7816
4.8373
-2.2997
6.4294
2.9768
0.976
4.7702
1.8239
2.7389
0.8845
2.379
-0.1647
-0.4575
-0.8296
-3.0439
-4.0199
7.5335
0.4392
1.0614
3.6905
3.3062
2.5071
0.0915
-3.1659
2.9829
1.0919
2.1655
2.2753
-0.4636
1.0858
1.3298
4.0077
1.3969
3.0683
1.5555
2.318
1.8232
0.5035
1.9822
-2.0034
-0.954
3.8266
0.6837
1.3038
1.4787
3.2012
0.5618
-0.7897
0.5777
-1.113
2.5016
0.9434
2.438
1.0335
-0.7526
1.3992
1.9451
0.2597
0.1643
0.5883
-0.1802
-1.166
-1.4734
2.4221
0.3922
0.2968
1.6748
-0.3339
0.2915
0.2173
0.4823
1.0335
-0.8904
0.7632
0.8798
0.1007
0.7208
1.5211
0.8268
0.3286
0.9699
-0.689
1.6218
1.0676
1.4875
0.3281
-0.5406
0.1411
0.17
0.221
0.7072
0.5287
0.6477
-0.2346
-0.595
0.1258
-0.1615
-0.3621
-0.272
0.017
0.8194
0.3094
0.357
-0.0986
-0.2584
-0.1836
0.1156
-0.0357
-0.2516
-0.0697
0.1394
0.1122
0.4709
-0.0221
0.1836
0.3264
0.0884
0.0697
0.2448
0.1241
0.3927
0.4301
0.8143
0.0051
0.7786
0.3315
0.1751
0.119
0.2754
0.612
0.5644
-3.3532
3.9176
8.5988
3.0046
2.4568
-0.6474
1.1122
0.7304
3.486
3.9342
1.3114
2.0584
2.8552
1.3114
3.818
2.0584
-4.399
0.1826
-1.5438
4.3824
3.4694
5.0132
5.6108
5.6938
6.972
4.731
-1.4442
4.6314
2.988
-1.3446
1.3446
1.1122
3.5192
5.4448
-0.3984
3.5026
-0.4316
-0.6308
-0.6474
2.7888
-1.6268
-1.0624
0.5146
1.9422
1.7762
-1.0956
201306
201307
201308
201309
201310
201311
201312
201401
201402
201403
201404
201405
201406
201407
201408
201409
201410
201411
201412
201501
201502
201503
201504
201505
201506
201507
201508
201509
201510
201511
201512
201601
201602
201603
201604
201605
201606
201607
201608
201609
201610
201611
201612
201701
201702
201703
201704
-0.122
4.0565
-1.0431
2.9097
3.1598
2.5132
2.3241
-1.4152
3.4465
0.8723
0.4941
1.8666
2.2021
-0.6344
3.1964
-0.5917
2.1472
2.1655
0.5734
-1.2871
4.3493
-0.0732
0.9699
1.4396
-0.3233
1.5494
-3.0744
-1.2688
5.3375
0.9516
-0.7137
-2.9097
0.5673
4.8556
1.1712
1.6958
0.5795
3.0195
0.915
0.7625
-0.6222
3.5746
1.7202
1.7934
2.7877
0.7137
1.2749
1.2455
1.484
0.5247
1.9451
-0.2809
1.2349
0.2491
0.8321
0.6095
-0.0901
-1.6695
-0.4717
2.173
-1.7225
0.6837
-1.4787
2.5334
-0.6943
2.0564
0.0689
0.6413
2.1412
-1.0759
0.9381
2.0458
-1.8815
0.742
-0.9434
-0.6148
2.3055
-1.0547
-1.2826
1.0335
1.1077
1.1501
0.1537
0.7897
1.9292
1.4416
1.4522
-1.5953
4.1393
0.7473
-0.1484
-0.5989
0.9434
0.7897
0.1411
0.2652
-0.3026
-0.0323
0.3638
0.2108
0.1173
-0.1853
0.102
1.0353
0.3638
0.1241
0.0442
0.1717
0.0697
-0.0391
-0.119
-0.34
0.5202
-0.4216
-0.1377
0.0918
0.4845
-0.0629
0.0357
-0.5304
0.6222
0.2601
0.1581
0.0833
-0.2703
0.5236
0.085
0.3672
0.7242
-0.1377
-0.0799
-0.0187
0.7378
-0.0833
0.8772
1.5793
0.7786
-0.3026
-0.1343
-0.3689
-0.1479
0.9296
-0.7636
2.6892
0.4482
6.142
1.8924
0.7968
-4.9634
1.2284
5.2456
7.4368
1.826
-1.6102
3.2204
0.6142
3.5358
0.8798
3.984
-0.2656
4.4322
-0.1162
1.826
1.411
-1.1952
2.49
1.7264
2.9216
4.6646
3.0212
-2.6062
2.1746
5.976
7.1048
3.0046
-3.1208
-0.0166
3.6354
3.8678
-0.5478
-2.1414
3.6354
1.494
3.3034
1.6434
2.9548
2.7888
4.98
201705
201706
201707
201708
201709
201710
201711
201712
1.2566
1.0858
1.7507
0.7076
2.1411
1.9825
2.5132
1.2566
-1.0865
1.8444
-0.3127
-0.4505
3.0952
-0.5141
0.3021
-0.0212
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0.3995
0.1207
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0.6851
0.1615
0.1632
0.1938
3.6686
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