Help using Base SAS procedures

proc rank

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Occasional Contributor
Posts: 11

proc rank

 

Hello
I would like to double sort the firms based on their size (TNA) and their concentration. Therefore, first, I need to sort the firms into terciles based on their size and then sort them based on their concentration. I need then to estimate for each generated portfolio its returns and calculate the difference between tercile 3 and tercile 1

The aim is to obtain the following :

concentration: size :
1-small 2 3-large
1-diversified

2-

3- concentrated

tercile 3- tercile1 x x x

I would like to know if my sas code is correct and to know how can I calculate tercile3-tercile1 and where to insert that in my sas code.Thank youu in advance.

 

 

 

data have;
input id $ report_date concentration alpha;
informat report_date date9.;
cards;

1 31-Mar-05 0.00323156 -0.00010338 31.9
2 31-Mar-05 0.0090895 0.00130514 2.9
3 31-Mar-05 0.00732307 0.0016094 7.5
4 31-Mar-05 0.143448318 -0.0036284 13.7

5 31-Mar-05 0.0128099 0.000668983 1.3
6 31-Mar-05 0.054668 -0.00081655 42.8
7 31-Mar-05 0.086314948 0.00156074 1.8
8 31-Mar-05 0.0355916 -0.0052425 7.3
9 31-Mar-05 0.00449587 0.00487914 106.4
10 31-Mar-05 0.114491666 -0.0032045 1215.2
11 31-Mar-05 0.00145931 0.00146518 38.8
12 31-Mar-05 0.0136698 -0.0027977 13.9
13 31-Mar-05 0.0240932 -0.0058679 9.5
14 31-Mar-05 -0.002676 0.00330896 1344.2
15 31-Mar-05 0.0207875 0.000187603 133.3
16 31-Mar-05 -0.01618 0.000540587 239.5
17 31-Mar-05 0.186049612 -0.0055676 4.5
18 31-Mar-05 0.11430027 -0.00054218 0.4
19 31-Mar-05 0.0420707 0.000860293 0.1
20 31-Mar-05 0.218189357 -0.0018136 0.4
21 31-Mar-05 0.00308538 0.000341367 8.2
22 31-Mar-05 0.0113599 0.00162913 2.3
23 31-Mar-05 0.0605686 -0.001316 1.1
24 31-Mar-05 0.0275298 -0.0030126 6
25 31-Mar-05 -0.00028393 0.00428045 5.7
26 31-Mar-05 0.000975115 0.00511607 2.2
27 31-Mar-05 0.0513553 -0.0017075 7.1
28 31-Mar-05 0.107913797 -0.0019555 49.4
29 31-Mar-05 0.028197 -0.0011384 42.9
30 31-Mar-05 0.021593 -0.0011053 39.8
31 31-Mar-05 0.00224724 0.00241661 5.1
32 31-Mar-05 0.0209171 -0.0024532 23.4
33 31-Mar-05 -0.0036813 0.00408326 26.7
34 31-Mar-05 -0.0052467 0.000882723 178.7
35 31-Mar-05 -0.0043958 -0.0022193 9.6
36 31-Mar-05 0.21214046 -0.0028023 80.4
37 31-Mar-05 0.00667457 0.00370299 2.9
38 31-Mar-05 0.0623278 -0.0021584 1.9
39 31-Mar-05 -0.0062717 0.000341234 1.2
40 31-Mar-05 0.0101843 -0.0034151 1.5
41 31-Mar-05 -0.022794 -0.0074877 44.3
42 31-Mar-05 0.169917216 -0.0030941 34.4
43 31-Mar-05 0.0108594 0.00377686 40
44 31-Mar-05 0.00473353 -0.00067654 78
1 30-Jun-05 0.009307 -0.00030918 1
2 30-Jun-05 0.00774149 -0.00020542 0.5
3 30-Jun-05 0.011545 -0.0020038 20
4 30-Jun-05 0.194446784 -0.002323 30
5 30-Jun-05 0.0149996 -0.0021457 30
6 30-Jun-05 0.080947592 -0.0019171 273.3
7 30-Jun-05 0.110467025 0.00173641 1816.7
8 30-Jun-05 0.116229935 -0.0028516 28.2
9 30-Jun-05 0.053037 -0.0060764 1.8
10 30-Jun-05 0.0205285 0.00401539 0.6
11 30-Jun-05 0.07412847 -0.0036452 12.9
12 30-Jun-05 0.000165941 0.00115735 9.2
13 30-Jun-05 0.0157843 -0.0045755 1
14 30-Jun-05 0.0198364 -0.0068052 2390.3
15 30-Jun-05 0.000822336 0.00199478 1366.2
16 30-Jun-05 0.0357898 -0.00060878 3729.9
17 30-Jun-05 -0.0036613 0.000226096 103.2
18 30-Jun-05 0.098482726 -0.0055159 5
19 30-Jun-05 0.0253289 0.000002754 48.6
20 30-Jun-05 0.0423226 0.00106245 7.4
21 30-Jun-05 0.168881853 -0.0012286 13.1
22 30-Jun-05 -0.009306 -0.0013763 0.6
23 30-Jun-05 0.0147948 0.000824364 52.9
24 30-Jun-05 0.107032711 -0.0013647 0.3
25 30-Jun-05 0.0269755 -0.0035086 0.3
26 30-Jun-05 0.00559733 0.00263787 0.3
27 30-Jun-05 0.00441441 0.00371973 47.1
28 30-Jun-05 -0.032813 0.000008644 0.6
29 30-Jun-05 0.09168613 -0.002004 56.1
30 30-Jun-05 0.218771292 -0.0020551 3.8
31 30-Jun-05 0.0148921 -0.0011354 1.1
32 30-Jun-05 0.0342261 0.000230794 39.1
33 30-Jun-05 0.023561 0.000594719 0.2
34 30-Jun-05 0.0212158 -0.00059915 293.6
35 30-Jun-05 0.0176603 -0.0043151 43.2
36 30-Jun-05 -0.0044589 0.00173475 23.5
37 30-Jun-05 0.000541246 0.00275968 20
38 30-Jun-05 -0.0040414 0.00162118 25
39 30-Jun-05 -0.0035244 0.000688753 23
40 30-Jun-05 0.136322452 -0.0020837 2.3
41 30-Jun-05 0.0137513 0.00307359 0.3
42 30-Jun-05 0.0579321 0.000323705 11.8
43 30-Jun-05 0.0140361 0.00403422 0.7
44 30-Jun-05 -0.013 0.000571995 2.5
45 30-Jun-05 0.0125506 -0.0053177 5
46 30-Jun-05 0.0102302 -0.0073835 0.6
47 30-Jun-05 0.186963265 -0.0023848 0.6
48 30-Jun-05 0.014541 0.00236808 152.6
49 30-Jun-05 0.00431307 -0.0017364 58.7
50 30-Jun-05 0.0498479 0.00467702 0.2
1 30-Sep-05 -0.0063786 0.000907728 0.2
2 30-Sep-05 0.0101038 0.000871673 3.2
3 30-Sep-05 0.0284981 0.00153884 0.1
4 30-Sep-05 0.121115631 -0.0015127 2.3
5 30-Sep-05 0.0306446 0.0015302 1.4
6 30-Sep-05 0.067656719 -0.00009431 0.3
7 30-Sep-05 0.108191664 -0.0018033 16.2
8 30-Sep-05 0.089732855 -0.0044431 0.1
9 30-Sep-05 0.065786426 -0.0028578 57.9
10 30-Sep-05 0.0166163 0.0128766 41.6
11 30-Sep-05 0.096876763 -0.0014895 169.1
12 30-Sep-05 0.000536793 -0.0005081 415.1
13 30-Sep-05 0.0342551 0.00110223 71.3
14 30-Sep-05 0.0504572 -0.006145 58.1
15 30-Sep-05 0.00601089 0.00436036 1.8
16 30-Sep-05 0.0417421 0.000042022 0.1
17 30-Sep-05 -0.0026028 -0.00061885 0.1
18 30-Sep-05 0.0491393 -0.0041615 30
19 30-Sep-05 -0.011321 0.00121491 1.9
20 30-Sep-05 0.0262681 0.00412299 1.9
21 30-Sep-05 0.195736815 0.00112513 2
22 30-Sep-05 -0.014973 -0.00050243 77.9
23 30-Sep-05 0.0356525 0.00176706 21.1
24 30-Sep-05 0.085042159 -0.000826 14.6
25 30-Sep-05 0.0305232 -0.0016151 3.6
26 30-Sep-05 0.00321965 0.0029778 0.1
27 30-Sep-05 0.00560532 0.00295675 32.3
28 30-Sep-05 -0.031548 0.00185938 12.6
29 30-Sep-05 0.0605934 -0.0010561 0.2
30 30-Sep-05 0.174065395 -0.0017657 6.3
31 30-Sep-05 0.012552 -0.00016551 221.1
32 30-Sep-05 0.0313465 0.00155516 39.2
33 30-Sep-05 0.015045 0.00141682 56.6
34 30-Sep-05 0.075091573 0.00254347 61.9
35 30-Sep-05 0.0438968 -0.00075478 30
36 30-Sep-05 -0.0038023 0.0039141 101.6
37 30-Sep-05 -0.007097 0.00315936 0.3
38 30-Sep-05 -0.0025448 0.00192762 0.6
;
run;

 

 

 

proc rank data=doublesort groups=3 out=ranked;
by date;
   var TNA;
   ranks GROUP_1;
run;
proc rank data=ranked groups=3 out=rankedfinal;
by date
   var Concentration;
   ranks GROUP_2;
run;

proc sort data=rankedfinal; by  date GROUP_2 GROUP_1 ;run;

PROC UNIVARIATE DATA= rankedfinal noprint;
var return;
by date GROUP_2 GROUP_1 ;
output out= ga3 mean=MOYENNE;
run;

proc sort data=ga3; by GROUP_2 GROUP_1;run;
proc means data = ga3 noprint ;
  by GROUP_2 GROUP_1 ;
    var moyenne;
    output out = rankedfinal1 mean= ewret;
run;
Valued Guide
Posts: 797

Re: proc rank

You apparently want 9 portfolios.  But there is no guarentee that you will have that many.  In fact, you could have as few as three.   Consider if your two attributes (TNA and CONCENTRATION) were perfectly correlated, such that GROUP2=1 when GROUP1=1, GROUP2=2 when GROUP1=2, etc.

 

If you want nine equal-size portfolios then you should be doing the ranks for group2 WITHIN EACH LEVEL of GROUP1 , not over all records as you propose.  To do so, sort the results of the first proc rank output by date and group, then run proc rank by date AND the first grouping:

 

proc rank data=have out=need;
  by date;
  var tna;
  ranks tna_group;
run;

proc sort data=need;
  by date tna_group;
run;

proc rank data=need out=rankedfinal;
  by date tna_group;
  var concentration;
  ranks concentration_group;
run;

 

Also I think you can run one PROC SUMMARY (with a properly specified CLASS statement) to get all the data you appear to want from your proc univariate and proc means.  proc summary (just another name for proc means) will provide a mean return for all 27 three-way combinations of DATE/TNA_GROUP/CONCENTRATION_GROUP, all two-way combinations (another 27 results), all 1-way combinations (9), and the global mean (1).

 


proc summary data=rankedfinal  out=want noprint; 
  class date tna_group concentration_group;
  var return;
output out=want mean(return)=mean_return ; run;
Occasional Contributor
Posts: 11

Re: proc rank

[ Edited ]

Thank you for your answer.  I have doble sorted my sample and then I have used the macro posted on this link : https://sites.google.com/site/jiejaycao/home/tools

 

 

This macro  aims to 

1-generate the average raw return matrix for double sorted group   and the high-minus-low difference
2.  report risk-adjusted returns:
CAPM alpha, Fama-French-3-factor alpha,Carhart-4-factor alpha
3. report Newey-West adjusted t-stat in bracket for return value
4. indicate the signicance level of high-minus-low difference

 

I will post the sas code, however I would like to ask you , I am getting a warning message:

WARNING: Output 'OutputStatistics' was not created. Make sure that the output object name, label, or path is spelled
correctly. Also, verify that the appropriate procedure options are used to produce the requested output
object. For example, verify that the NOPRINT option is not used.
WARNING: Output 'fitstatistics' was not created. Make sure that the output object name, label, or path is spelled
correctly. Also, verify that the appropriate procedure options are used to produce the requested output
object. For example, verify that the NOPRINT option is not used.

 

Do you know what is the problem? I have tried ods trace on to see  the output, I didn't find  the fitstatistics and the outputstatistics. Do you know how can I choose the appropriate lag


/********************************************************************** Purpose: 0. report formulized portfolio double sorting results 1. generate the average raw return matrix for double sorted groups and the high-minus-low difference 2. also report risk-adjusted returns: CAPM alpha, Fama-French-3-factor alpha,Carhart-4-factor alpha 3. report Newey-West adjusted t-stat in bracket for return value 4. indicate the signicance level of high-minus-low difference by '*', '**' and '***' for 10%, 5% and 1% Author : Jie Cao, jiecao@mail.utexas.edu Date : 12/01/2006 Notes : the two groups must be sorted ex-ante and each group1-group2 combination has a time-series of average raw return history Input data : the panel data, it must contains the equal or value weighted raw portfolio return (in %) for each group1-group2 each period factors : the corresponding Fama-French daily, weekly or monthly factors in time-series (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) byvar : usually left blank, in use if there is an extra layer like byvar-group1-group2-period for each row in the panel data rankvar1 : the group1 number rankvar2 : the group2 number timevar : the time-variable, daily, weekly, or monthly lag : the lag used for Newey-West test var : the raw return Output out1 : report double sorting results out2 : reprot both sorting and single sorting results ***********************************************************************/ %macro doublesort_adj(data=, factors=, byvar=, rankvar1=, rankvar2=, timevar=, lag=, var=, out1=, out2=); * rank1 is row and rank2 is column; proc sort data=&data; by &byvar &rankvar1 &rankvar2 &timevar;run; proc transpose data=&data out=data; by &byvar &rankvar1 &rankvar2 &timevar; var &var;run; proc sort data=data; by _name_ &byvar &rankvar1 &rankvar2 &timevar;run; *Find H-L difference for rank variable 2; proc sort data=&data out=sum; by &byvar &rankvar1 &timevar &rankvar2;run; data sum_diff; set sum(where=(&rankvar2>-1)); by &byvar &rankvar1 &timevar &rankvar2; if first.&timevar or last.&timevar; if first.&timevar then &rankvar2=1; if last.&timevar then &rankvar2=2; run; proc transpose data=sum_diff out=sum_diff2; by &byvar &rankvar1 &timevar; var &var; id &rankvar2; run; data sum_diff2; set sum_diff2; &rankvar2=99; col1 = _2 - _1; drop _2 _1; run; data sum_diff2; set data sum_diff2;run; *Find H-L difference for rank variable 1; proc sort data=sum_diff2; by _name_ &byvar &rankvar2 &timevar &rankvar1;run; data sum_diff3; set sum_diff2(where=(&rankvar1>-1)); by _name_ &byvar &rankvar2 &timevar &rankvar1; if first.&timevar or last.&timevar;if first.&timevar then &rankvar1=1; if last.&timevar then &rankvar1=2; run; proc transpose data=sum_diff3 out=sum_diff3; by _name_ &byvar &rankvar2 &timevar; var col1; id &rankvar1; run; data sum_diff3; set sum_diff3; &rankvar1=100; col1 = _2 - _1; drop _2 _1; run; data sum_diff3; set sum_diff3 sum_diff2;run; proc sort data=sum_diff3; by _name_ &byvar &rankvar1 &rankvar2 &timevar;run; **link four factors to get alpha; data ff; set &factors;run; proc sql; create table sum_diff3 as select a.*, (a.col1-b.rf*100) as exret, b.mktrf*100 as mktrf, b.smb*100 as smb,b.hml*100 as hml, b.umd*100 as umd from sum_diff3 as a left join ff as b on a.&timevar = b.date;/* the &timevar must be consistent with the time identifier (i.e. date) from FF time-series factors*/ quit; proc sort data=sum_diff3; by _name_ &byvar &rankvar1 &rankvar2 &timevar;run; data sum_diff3; set sum_diff3; if &rankvar1=100 or &rankvar2=99 then exret=col1; run; ***average return; options nonotes; proc model data=sum_diff3; by &byvar _name_ &rankvar1 &rankvar2; parms a; exogenous col1 ; instruments / intonly; col1=a; fit col1 / gmm kernel=(bart, %eval(&lag+1), 0);*lag=6; ods output parameterestimates=param0 fitstatistics=fitresult OutputStatistics=residual; quit; data param0; set param0; type='Average ret';run; **CAPM alpha; options nonotes; proc model data=sum_diff3; by &byvar _name_ &rankvar1 &rankvar2; parms a b1; instruments mktrf; exret =a+b1* mktrf; fit exret / gmm kernel=(bart, %eval(&lag+1), 0); ods output parameterestimates=param1 fitstatistics=fitresult OutputStatistics=residual; quit; data param1; set param1; type='CAPM Alpha'; if parameter='a';run; **Three Factor alpha; options nonotes; proc model data=sum_diff3; by &byvar _name_ &rankvar1 &rankvar2; parms a b1 b2 b3; instruments mktrf smb hml; exret =a+b1* mktrf+b2* smb+b3*hml; fit exret / gmm kernel=(bart, %eval(&lag+1), 0); ods output parameterestimates=param2 fitstatistics=fitresult OutputStatistics=residual; quit; data param2; set param2; type='FF3 Alpha'; if parameter='a';run; *Four factor alpha*; options nonotes; proc model data=sum_diff3; by &byvar _name_ &rankvar1 &rankvar2; parms a b1 b2 b3 b4; instruments mktrf smb hml umd; exret =a+b1* mktrf+b2* smb+b3*hml+b4*umd; fit exret / gmm kernel=(bart, %eval(&lag+1), 0); ods output parameterestimates=param3 fitstatistics=fitresult OutputStatistics=residual; quit; data param3; set param3; type='Carhart4 Alpha'; if parameter='a'; run; data param; set param0 param1 param2 param3;run; data param; set param; if probt<0.1 then p='* '; if probt<0.05 then p='** '; if probt<0.01 then p='***';tvalue2=put(tvalue,7.2); est=put(estimate, 12.2); param=est; if &rankvar1=100 or &rankvar2=99 then PARAM=compress(est||p); T=compress('('||tvalue2||')'); keep &byvar &rankvar1 &rankvar2 type _name_ param T; rename _name_=name;run; proc sort data=param; by name &byvar type &rankvar1 &rankvar2;run; proc transpose data=param out=out; by name &byvar type &rankvar1; var param T; id &rankvar2; run; data &out1; set out; if &rankvar1>-1;drop N1; if _name_='T' then &rankvar1=.; run; data &out2;set out; if _name_='T' then &rankvar1=.; rename _99=H_L N1=all;run; option notes; %mend doublesort_adj; %doublesort_adj(data=rankedfd1, factors=marketm, byvar=, rankvar1=Group_1, rankvar2=Group_2, timevar=report_date, lag=5, var=MOYENNE, out1=d1, out2=d2);

.Thank youu again 

 

 

 

data have2;
input report_date Group_1 Group_2 moyenne;
informat report_date date9.;
cards;
REPORT_DATE GROUP_1 GROUP_2 MOYENNE
3/31/2005 1 1 -2.322171824
6/30/2005 1 1 0.626790319
9/30/2005 1 1 1.982357543
3/31/2006 1 1 2.198054512
6/30/2006 1 1 0.0145957
12/31/2007 1 1 -1.240469711
3/31/2008 1 1 -0.474454175
6/30/2008 1 1 -6.384736204
9/30/2008 1 1 -11.20326428
12/31/2008 1 1 4.266663084
3/31/2009 1 1 7.231308572
6/30/2009 1 1 -0.447744893
9/30/2009 1 1 5.929067393
12/31/2009 1 1 2.57751674
3/31/2010 1 1 4.036217119
6/30/2010 1 1 -1.20620396
9/30/2010 1 1 9.328738063
12/31/2010 1 1 6.353543839
3/31/2011 1 1 1.324201634
6/30/2011 1 1 -1.601201557
9/30/2011 1 1 -10.50014413
12/31/2012 1 1 2.295058131
9/30/2013 1 1 4.89632191
12/31/2013 1 1 2.32541224
3/31/2014 1 1 0.091297204
6/30/2014 1 1 2.235187498
9/30/2014 1 1 -3.484066949
12/31/2014 1 1 -1.823150093
3/31/2015 1 1 -1.527052398
6/30/2015 1 1 -2.296832908
9/30/2015 1 1 -3.226596051
12/31/2015 1 1 -2.350325922
3/31/2016 1 1 6.452720906
3/31/2005 1 2 -2.435033333
6/30/2005 1 2 1.589133564
9/30/2005 1 2 1.938033691
3/31/2006 1 2 2.606787331
6/30/2006 1 2 -1.06971269
12/31/2007 1 2 -0.846350605
3/31/2008 1 2 -0.316333072
6/30/2008 1 2 -6.576942096
9/30/2008 1 2 -13.27043089
12/31/2008 1 2 4.884534433
3/31/2009 1 2 7.43684456
6/30/2009 1 2 -0.579475586
9/30/2009 1 2 5.876049733
12/31/2009 1 2 2.463108998
3/31/2010 1 2 6.527865041
6/30/2010 1 2 -2.075713905
9/30/2010 1 2 10.38296691
12/31/2010 1 2 6.399916889
3/31/2011 1 2 0.502057898
6/30/2011 1 2 -1.243589455
9/30/2011 1 2 -10.32849698
12/31/2012 1 2 1.473758817
9/30/2013 1 2 5.054047282
12/31/2013 1 2 1.484317969
3/31/2014 1 2 0.674285761
6/30/2014 1 2 1.96611123
9/30/2014 1 2 -3.077085786
12/31/2014 1 2 -1.622661646
3/31/2015 1 2 -0.876391176
6/30/2015 1 2 -1.887314414
9/30/2015 1 2 -3.211623903
12/31/2015 1 2 -1.982155451
3/31/2016 1 2 6.444438622
3/31/2005 1 3 -3.063433079
6/30/2005 1 3 2.406043805
9/30/2005 1 3 2.587182805
3/31/2006 1 3 3.527857546
6/30/2006 1 3 -1.538172321
12/31/2007 1 3 -1.239657725
3/31/2008 1 3 -1.095768426
6/30/2008 1 3 -7.72586063
9/30/2008 1 3 -12.66765454
12/31/2008 1 3 3.346787823
3/31/2009 1 3 8.759043383
6/30/2009 1 3 -1.110819519
9/30/2009 1 3 4.954552331
12/31/2009 1 3 2.761224237
3/31/2010 1 3 5.7669492
6/30/2010 1 3 -4.294199302
9/30/2010 1 3 9.547207912
12/31/2010 1 3 6.907712084
3/31/2011 1 3 0.239119669
6/30/2011 1 3 -2.381578283
9/30/2011 1 3 -9.900471195
12/31/2012 1 3 1.982756966
9/30/2013 1 3 5.107008482
12/31/2013 1 3 2.31786484
3/31/2014 1 3 -0.608583105
6/30/2014 1 3 2.387027063
9/30/2014 1 3 -3.036925061
12/31/2014 1 3 -1.238230944
3/31/2015 1 3 -0.705223331
6/30/2015 1 3 -1.56115226
9/30/2015 1 3 -3.760019327
12/31/2015 1 3 -2.729687349
3/31/2016 1 3 7.060595051
3/31/2005 2 1 -2.140270345
6/30/2005 2 1 1.828399489
9/30/2005 2 1 3.228528125
3/31/2006 2 1 3.046076601
6/30/2006 2 1 -0.561187613
12/31/2007 2 1 -1.372377344
3/31/2008 2 1 -1.653002186
6/30/2008 2 1 -7.369311069
9/30/2008 2 1 -10.79778152
12/31/2008 2 1 4.542882575
3/31/2009 2 1 6.332025054
6/30/2009 2 1 0.665235567
9/30/2009 2 1 3.856023888
12/31/2009 2 1 2.439971204
3/31/2010 2 1 5.30331915
6/30/2010 2 1 -1.315842659
9/30/2010 2 1 9.55397894
12/31/2010 2 1 6.546704368
3/31/2011 2 1 0.09037446
6/30/2011 2 1 -1.519320206
9/30/2011 2 1 -9.284942436
12/31/2012 2 1 2.360583264
9/30/2013 2 1 5.037464272
12/31/2013 2 1 1.681270083
3/31/2014 2 1 0.272434664
6/30/2014 2 1 2.047536758
9/30/2014 2 1 -3.958858788
12/31/2014 2 1 -1.826459901
3/31/2015 2 1 -1.05916749
6/30/2015 2 1 -2.547343232
9/30/2015 2 1 -2.826212581
12/31/2015 2 1 -1.465009398
3/31/2016 2 1 6.133429858
3/31/2005 2 2 -1.916876579
6/30/2005 2 2 1.131196343
9/30/2005 2 2 2.806330757
3/31/2006 2 2 1.932782118
6/30/2006 2 2 0.122114921
12/31/2007 2 2 -1.658254159
3/31/2008 2 2 -0.904590876
6/30/2008 2 2 -8.173949476
9/30/2008 2 2 -9.721551737
12/31/2008 2 2 4.600794163
3/31/2009 2 2 6.518280792
6/30/2009 2 2 0.238502059
9/30/2009 2 2 4.450664754
12/31/2009 2 2 1.979304808
3/31/2010 2 2 5.935359522
6/30/2010 2 2 -3.022620613
9/30/2010 2 2 9.213448493
12/31/2010 2 2 6.487539172
3/31/2011 2 2 -0.12057478
6/30/2011 2 2 -1.598320202
9/30/2011 2 2 -8.293524448
12/31/2012 2 2 1.75759085
9/30/2013 2 2 5.299883246
12/31/2013 2 2 1.842179385
3/31/2014 2 2 0.215677192
6/30/2014 2 2 1.598921949
9/30/2014 2 2 -2.762990976
12/31/2014 2 2 -1.510258758
3/31/2015 2 2 -1.419294217
6/30/2015 2 2 -2.372750378
9/30/2015 2 2 -3.904015356
12/31/2015 2 2 -1.787518889
3/31/2016 2 2 6.888912545
3/31/2005 2 3 -2.669968261
6/30/2005 2 3 0.965619644
9/30/2005 2 3 1.578924128
3/31/2006 2 3 1.817990688
6/30/2006 2 3 -1.316281088
12/31/2007 2 3 -1.655901047
3/31/2008 2 3 -2.193572999
6/30/2008 2 3 -10.49299246
9/30/2008 2 3 -11.0921548
12/31/2008 2 3 4.599329464
3/31/2009 2 3 8.921736464
6/30/2009 2 3 1.091586637
9/30/2009 2 3 4.334811365
12/31/2009 2 3 2.87522004
3/31/2010 2 3 6.254339258
6/30/2010 2 3 -4.549056471
9/30/2010 2 3 10.41858545
12/31/2010 2 3 6.178918441
3/31/2011 2 3 0.383679077
6/30/2011 2 3 -1.606762104
9/30/2011 2 3 -9.302983225
12/31/2012 2 3 2.618063019
9/30/2013 2 3 5.07674057
12/31/2013 2 3 2.643362741
3/31/2014 2 3 -0.319062829
6/30/2014 2 3 2.048142593
9/30/2014 2 3 -3.489614908
12/31/2014 2 3 -1.508985386
3/31/2015 2 3 -0.62553499
6/30/2015 2 3 -1.764952512
9/30/2015 2 3 -3.916254491
12/31/2015 2 3 -2.280877653
3/31/2016 2 3 6.549847454
3/31/2005 3 1 -1.575118227
6/30/2005 3 1 1.12384807
9/30/2005 3 1 2.302000372
3/31/2006 3 1 2.263326848
6/30/2006 3 1 -0.519515099
12/31/2007 3 1 0.638903511
3/31/2008 3 1 -1.072609428
6/30/2008 3 1 -6.960439156
9/30/2008 3 1 -9.433949204
12/31/2008 3 1 2.423745032
3/31/2009 3 1 6.564420902
6/30/2009 3 1 -0.317507012
9/30/2009 3 1 5.407487336
12/31/2009 3 1 1.995858332
3/31/2010 3 1 5.610697649
6/30/2010 3 1 -1.860893117
9/30/2010 3 1 9.168087721
12/31/2010 3 1 6.711588318
3/31/2011 3 1 0.265628154
6/30/2011 3 1 -2.28810599
9/30/2011 3 1 -9.503749891
12/31/2012 3 1 1.84201768
9/30/2013 3 1 5.015118099
12/31/2013 3 1 1.763221182
3/31/2014 3 1 0.572648961
6/30/2014 3 1 2.0801556
9/30/2014 3 1 -3.639577744
12/31/2014 3 1 -1.998462654
3/31/2015 3 1 -1.68262813
6/30/2015 3 1 -2.550869277
9/30/2015 3 1 -2.876979374
12/31/2015 3 1 -2.386181485
3/31/2016 3 1 7.022733055
3/31/2005 3 2 -2.143950435
6/30/2005 3 2 0.545862866
9/30/2005 3 2 2.129712148
3/31/2006 3 2 1.764491015
6/30/2006 3 2 -0.783620219
12/31/2007 3 2 -0.97583881
3/31/2008 3 2 -0.597903926
6/30/2008 3 2 -7.678382307
9/30/2008 3 2 -11.66207884
12/31/2008 3 2 3.905737041
3/31/2009 3 2 6.525406537
6/30/2009 3 2 -1.086361905
9/30/2009 3 2 5.553301477
12/31/2009 3 2 2.949795251
3/31/2010 3 2 5.670726827
6/30/2010 3 2 -2.884013201
9/30/2010 3 2 8.764470242
12/31/2010 3 2 6.577653317
3/31/2011 3 2 0.068791001
6/30/2011 3 2 -1.521590878
9/30/2011 3 2 -9.353286348
12/31/2012 3 2 2.665415372
9/30/2013 3 2 5.28913576
12/31/2013 3 2 2.006834018
3/31/2014 3 2 -0.61642788
6/30/2014 3 2 1.769921622
9/30/2014 3 2 -2.526256388
12/31/2014 3 2 -1.523431539
3/31/2015 3 2 -0.813288728
6/30/2015 3 2 -1.941764766
9/30/2015 3 2 -3.438947813
12/31/2015 3 2 -1.956527965
3/31/2016 3 2 6.701681526
3/31/2005 3 3 -1.823792866
6/30/2005 3 3 1.726441505
9/30/2005 3 3 2.069270297
3/31/2006 3 3 3.247117139
6/30/2006 3 3 -1.423422931
12/31/2007 3 3 -1.479774712
3/31/2008 3 3 -1.756124615
6/30/2008 3 3 -8.468743543
9/30/2008 3 3 -14.43354213
12/31/2008 3 3 5.963423941
3/31/2009 3 3 7.903468017
6/30/2009 3 3 0.0470396
9/30/2009 3 3 5.671113129
12/31/2009 3 3 3.529030076
3/31/2010 3 3 6.653064067
6/30/2010 3 3 -4.448708333
9/30/2010 3 3 10.54382053
12/31/2010 3 3 6.610569877
3/31/2011 3 3 0.438423905
6/30/2011 3 3 -2.133997746
9/30/2011 3 3 -9.590102421
12/31/2012 3 3 2.536270488
9/30/2013 3 3 4.539256264
12/31/2013 3 3 2.202420887
3/31/2014 3 3 -0.668028044
6/30/2014 3 3 2.084467915
9/30/2014 3 3 -2.770458643
12/31/2014 3 3 -1.018341357
3/31/2015 3 3 -0.825179375
6/30/2015 3 3 -1.762280682
9/30/2015 3 3 -3.508094255
12/31/2015 3 3 -1.893558697
3/31/2016 3 3 6.203550471
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