Octave Code for Calculating Factor Loadings and Returns for Fama-French 25 Portfolios<\/strong><\/p>\n

\r\nclear all; % clear data from Octave\r\nclose all; % close all open plot windows\r\n\r\n% Load Fama-French Data\r\nff_data = load('25_Portfolios_5x5_monthly_2.txt');\r\n% Load FF Factor Mimicking Portfolios\r\nff_facts = load('F-F_Factors_monthly.txt');\r\n\r\n% Starting point changed to January 1932 to avoid missing data\r\nff_data = ff_data(67:end,:);   % start after NAs end\r\nff_facts = ff_facts(67:end-1,:); % start after NAs end, factors had one extra sample, so used end-1\r\n\r\n% Remove date column\r\nr = ff_data(:,2:end);\r\n% Remove date and risk free\r\nff3f = ff_facts(:,2:end);\r\n\r\n% Prompt for User Input to get plotting range\r\nstartyear = input('Enter Starting Year between 1932 and 2010: ')\r\nstartmonth = input('Enter Starting Month 1-12: ')\r\nendyear = input('Enter Ending Year between 1932 and 2010: ')\r\nendmonth = input('Enter Ending Month 1-12: ')\r\nplottitle = input('Enter Title for Plot: ','s')\r\n\r\n% Calculate starting and ending row\r\nstart = 12*(startyear - 1932) + startmonth;\r\nendpoint = 12*(endyear-1932) + endmonth;\r\n\r\n% Extract Desired Data\r\nr = r(start:endpoint,:);\r\nff3f = ff3f(start:endpoint,:);\r\nrmrf = ff3f(:,1)\/100;\r\nsmb = ff3f(:,2)\/100;\r\nhml = ff3f(:,3)\/100;\r\nrf = ff3f(:,4)\/100;\r\n\r\n% Run 25 Fama-French Regressions\r\nrx = r.\/100 - repmat(rf,1,25);\r\n\r\n% Run FF regressions on all portfolios\r\nK = 3\r\nT = size(rx,1)\r\nX = [ones(T,1) rmrf hml smb];\r\nb = X\\rx;\r\ne = rx-X*b;\r\nsigma = cov(e);\r\nu = rx-X*b;\r\ns2 = (T-1)\/(T-K-1)*var(u)';   % this is a vector of the variance of the errors\r\n\r\nmx = inv(X'*X);\r\ndmx = diag(mx); % we\u2019re interested in standard errors,\r\n\r\n% the diagonals of the covariance matrix of bs\r\nsiga = (s2*dmx(1)).^0.5;      % std err of alpha, beta\r\nsigb = (s2*dmx(2:end)').^0.5; % s2 is a column vector of 25. dmx\u2019 is a\r\n                              % row vector corresponding to factors.\r\n                              % this produces a matrix the same size as\r\n                              % the b coefficients.\r\nsig_beta = sigb(:,1);\r\nsig_h = sigb(:,2);\r\nsig_s = sigb(:,3);\r\n\r\nR2 = 1-s2.\/(std(rx).^2)';\r\n\r\n% Pull out the regression factors\r\nff_alpha = b(1,:);\r\nff_beta = b(2,:);\r\nh = b(3,:);\r\ns = b(4,:);\r\n\r\n% Calculate Arithmetic Mean for each of 25 portfolios over range\r\narithmeans = mean(r);\r\n\r\n% Calculate Geometric Mean for each of 25 portfolios over selected range\r\ngeoreturns = r.\/100 + 1;\r\ngeomeans = 100*(exp(mean(log(georeturns)))-1);\r\n\r\n% Select if Geometric or Arithmetic mean is used by adjusting comments\r\n%meanreturns = arithmeans;  % uncomment to use arithmetic means\r\nmeanreturns = geomeans;   % uncomment to use geometric means\r\n\r\n% Expand 5x5 data to 10x10 for use in surface plot function\r\nreturns = [meanreturns ; meanreturns];\r\nreturns = reshape(returns,10,5);\r\nreturns = [returns;returns]\r\nreturns = reshape(returns,10,10);\r\n\r\n% beta can be used for surface plot of beta\r\nbeta_ff = [ff_beta;ff_beta]\r\nbeta_ff = reshape(beta_ff,10,5);\r\nbeta_ff = [beta_ff;beta_ff];\r\nbeta_ff = reshape(beta_ff,10,10);\r\n\r\n% s; s_ff can be used for surface plot of size factor\r\ns_ff = [s;s]\r\ns_ff = reshape(s_ff,10,5);\r\ns_ff = [s_ff;s_ff];\r\ns_ff = reshape(s_ff,10,10);\r\n\r\n% h; h_ff can be used for surface plot of value factor\r\nh_ff = [h;h]\r\nh_ff = reshape(h_ff,10,5);\r\nh_ff = [h_ff;h_ff];\r\nh_ff = reshape(h_ff,10,10);\r\n\r\n% Define x and y values\r\nx = [0 0.999 1 1.999 2 2.999 3 3.999 4 5];\r\ny = [0 0.999 1 1.999 2 2.999 3 3.999 4 5];\r\n\r\n% Create x-y mesh for surface plot\r\n[xx,yy] = meshgrid(x,y);\r\n\r\n% Generate Plot\r\nsurf(xx,yy,returns)\r\nxlabel('Size','fontsize',20)\r\nylabel('Value','fontsize',20)\r\n%zlabel('Arithmetic Average Monthly Return (%)','rotation',90,'fontsize',20)\r\n%zlabel('Geometric Average Monthly Return (%)','rotation',90,'fontsize',20)\r\ntitle(plottitle,'fontsize',36)\r\naxis([0 5 0 5 min(0,min(meanreturns)-.1) max(2,max(meanreturns)+0.01)])\r\n\r\n% Size Lables for corner portfolios\r\nline([4.5 4.5],[0.5 0.5],[meanreturns(21) meanreturns(21)+0.1])\r\ntext(4.5,0.5,meanreturns(21)+0.15,'LG','horizontalalignment','center','fontsize',18)\r\nline([4.5 4.5],[4.5 4.5],[meanreturns(25) meanreturns(25)+0.1])\r\ntext(4.5,4.5,meanreturns(25)+0.15,'LV','horizontalalignment','center','fontsize',18)\r\nline([0.5 0.5],[4.5 4.5],[meanreturns(5) meanreturns(5)+0.1])\r\ntext(0.5,4.5,meanreturns(5)+0.15,'SV','horizontalalignment','center','fontsize',18)\r\nline([0.5 0.5],[0.5 0.5],[meanreturns(1) meanreturns(1)+0.1])\r\ntext(0.5,0.5,meanreturns(1)+0.15,'SG','horizontalalignment','center','fontsize',18)\r\n\r\n% ETFs\r\nline([4.55 4.55],[1.1 1.1],[meanreturns(22) meanreturns(22)+0.15])\r\ntext(4.55,1.1,meanreturns(22)+0.18,'SPY','horizontalalignment','center','fontsize',18)\r\n\r\nline([4.75 4.75],[1.25 1.25],[meanreturns(22) meanreturns(22)+0.1])\r\ntext(4.75,1.25,meanreturns(22)+0.15,'DIA','horizontalalignment','center','fontsize',18)\r\n\r\nline([3.5 3.5],[0.1 0.1],[meanreturns(16) meanreturns(16)+0.1])\r\ntext(3.5,0.1,meanreturns(16)+0.15,'QQQQ','horizontalalignment','center','fontsize',18)\r\n\r\nline([4.5 4.5],[1.5 1.5],[meanreturns(22) meanreturns(22)+0.18])\r\ntext(4.50,1.5,meanreturns(22)+0.2,'IVE*','horizontalalignment','center','fontsize',18)\r\n\r\nline([1.6 1.6],[1.7 1.7],[meanreturns(7) meanreturns(7)+0.25])\r\ntext(1.6,1.7,meanreturns(7)+0.3,'IWM*','horizontalalignment','center','fontsize',18)\r\n\r\nline([1.7 1.7],[3.55 3.55],[meanreturns(9) meanreturns(9)+0.1])\r\ntext(1.7,3.55,meanreturns(9)+0.15,'IWN','horizontalalignment','center','fontsize',18)\r\n\r\nline([1.65 1.65],[1.85 1.85],[meanreturns(7) meanreturns(7)+0.1])\r\ntext(1.65,1.85,meanreturns(7)+0.15,'IJR','horizontalalignment','center','fontsize',18)\r\n\r\nline([1.6 1.6],[2.85 2.85],[meanreturns(8) meanreturns(8)+0.1])\r\ntext(1.6,2.85,meanreturns(8)+0.15,'IJS','horizontalalignment','center','fontsize',18)\r\n\r\nline([2.9 2.9],[4.6 4.6],[meanreturns(15) meanreturns(15)+0.1])\r\ntext(2.9,4.6,meanreturns(15)+0.15,'IYR','horizontalalignment','center','fontsize',18)\r\n\r\nline([3.4 3.4],[1.25 1.25],[meanreturns(19) meanreturns(19)+0.1])\r\ntext(3.4,1.25,meanreturns(19)+0.15,'MDY','horizontalalignment','center','fontsize',18)\r\n\r\n% Color range set from 0 to 1.6 rather than allowing autoscale.\r\n% This is done for easier comparison between plots, but colors will\r\n% max out for values above 1.6 or below 0.\r\n% For arithmetic averages, I think a range of 0 to 2 works better\r\ncaxis([0 1.6]);\r\nview(50, 25);\r\n% top view\r\n%view(270,90);\r\nreplot\r\n\r\n<\/pre>\n
Regression Result Details:<\/strong><\/p>\n
ETF Regressions (10-yr Monthly; November 2000 thru October 2010):<\/strong><\/p>\n
SPY:<\/strong><\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Estimate Std. Error t value Pr(>|t|)\u00a0\u00a0\u00a0<\/p>\n
(Intercept) -0.0008725\u00a0 0.0005321\u00a0 -1.640\u00a0\u00a0\u00a0 0.104\u00a0\u00a0\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.9620172\u00a0 0.0110702\u00a0 86.901\u00a0 < 2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 -0.1292276\u00a0 0.0200597\u00a0 -6.442 2.78e-09 ***<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.0120373\u00a0 0.0157662\u00a0\u00a0 0.763\u00a0\u00a0\u00a0 0.447\u00a0\u00a0\u00a0<\/p>\n
—\u00a0<\/p>\n
Residual standard error: 0.005643 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.9862,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.9858<\/p>\n
F-statistic:\u00a0 2763 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
DIA:<\/strong><\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Estimate Std. Error t value Pr(>|t|)\u00a0\u00a0\u00a0<\/p>\n
(Intercept)\u00a0 0.0009774\u00a0 0.0013106\u00a0\u00a0 0.746\u00a0 0.45730\u00a0\u00a0\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.8925274\u00a0 0.0272691\u00a0 32.730\u00a0 < 2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 -0.2162676\u00a0 0.0494128\u00a0 -4.377 2.65e-05 ***<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.1189867\u00a0 0.0388367\u00a0\u00a0 3.064\u00a0 0.00272 **<\/p>\n
—<\/p>\n
Residual standard error: 0.0139 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.9074,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.905<\/p>\n
F-statistic: 378.7 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
QQQQ:<\/strong><\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Estimate Std. Error t value Pr(>|t|)\u00a0\u00a0\u00a0<\/p>\n
(Intercept)\u00a0 0.0005339\u00a0 0.0021967\u00a0\u00a0 0.243\u00a0 0.80839\u00a0\u00a0\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1.3260511\u00a0 0.0457057\u00a0 29.013\u00a0 < 2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.2930439\u00a0 0.0828208\u00a0\u00a0 3.538\u00a0 0.00058 ***<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 -0.9161538\u00a0 0.0650942 -14.074\u00a0 < 2e-16 ***<\/p>\n
—<\/p>\n
Residual standard error: 0.0233 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.9196,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.9175<\/p>\n
F-statistic: 442.3 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
IVE:<\/strong><\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Estimate Std. Error t value Pr(>|t|)\u00a0\u00a0\u00a0<\/p>\n
(Intercept) -0.0020639\u00a0 0.0009668\u00a0 -2.135\u00a0\u00a0 0.0349 *\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.9947619\u00a0 0.0201148\u00a0 49.454\u00a0 < 2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 -0.0528948\u00a0 0.0364488\u00a0 -1.451\u00a0\u00a0 0.1494\u00a0\u00a0\u00a0<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.2691649\u00a0 0.0286475\u00a0\u00a0 9.396 6.19e-16 ***<\/p>\n
—<\/p>\n
Residual standard error: 0.01025 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.9601,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.9591<\/p>\n
F-statistic: 931.3 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
IWM:<\/strong><\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Estimate Std. Error t value Pr(>|t|)\u00a0\u00a0\u00a0<\/p>\n
(Intercept) -0.0019862\u00a0 0.0008238\u00a0 -2.411\u00a0\u00a0 0.0175 *\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a00.9759772\u00a0 0.0171401\u00a0 56.941\u00a0 < 2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.8246550\u00a0 0.0310586\u00a0 26.552\u00a0 < 2e-16 ***<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.1899842\u00a0 0.0244109\u00a0\u00a0 7.783 3.25e-12 ***<\/p>\n
—<\/p>\n
Residual standard error: 0.008737 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.9804,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.9799<\/p>\n
F-statistic:\u00a0 1935 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
IWN:<\/strong><\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Estimate Std. Error t value Pr(>|t|)\u00a0\u00a0\u00a0<\/p>\n
(Intercept) -0.001577\u00a0\u00a0 0.001138\u00a0 -1.386\u00a0\u00a0\u00a0 0.169\u00a0\u00a0\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.876021\u00a0\u00a0 0.023686\u00a0 36.984\u00a0\u00a0 <2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.756015\u00a0\u00a0 0.042920\u00a0 17.614\u00a0\u00a0 <2e-16 ***<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.606810\u00a0\u00a0 0.033734\u00a0 17.988\u00a0\u00a0 <2e-16 ***<\/p>\n
—<\/p>\n
Residual standard error: 0.01207 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.9588,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.9577<\/p>\n
F-statistic:\u00a0\u00a0 899 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
IJR:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/p>\n
(Intercept) -0.001384\u00a0\u00a0 0.001156\u00a0 -1.197\u00a0\u00a0\u00a0 0.234\u00a0\u00a0\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.913679\u00a0\u00a0 0.024063\u00a0 37.971\u00a0 < 2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.795130\u00a0\u00a0 0.043603\u00a0 18.236\u00a0 < 2e-16 ***<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.301977\u00a0\u00a0 0.034270\u00a0\u00a0 8.812 1.43e-14 ***<\/p>\n
—<\/p>\n
Residual standard error: 0.01227 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.958,\u00a0\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.957<\/p>\n
F-statistic: 882.8 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
IJS:<\/strong><\/p>\n
(Intercept) -0.001711\u00a0\u00a0 0.001294\u00a0 -1.322\u00a0\u00a0\u00a0 0.189\u00a0\u00a0\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.914892\u00a0\u00a0 0.026932\u00a0 33.971\u00a0\u00a0 <2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.845430\u00a0\u00a0 0.048802\u00a0 17.324\u00a0\u00a0 <2e-16 ***<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.483571\u00a0\u00a0 0.038356\u00a0 12.607\u00a0\u00a0 <2e-16 ***<\/p>\n
—<\/p>\n
Residual standard error: 0.01373 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.9511,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.9499<\/p>\n
F-statistic: 752.7 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
IYR:<\/strong><\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Estimate Std. Error t value Pr(>|t|)\u00a0\u00a0\u00a0<\/p>\n
(Intercept) 0.0007802\u00a0 0.0039314\u00a0\u00a0 0.198\u00a0\u00a0 0.8430\u00a0\u00a0\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.9250019\u00a0 0.0817982\u00a0 11.308\u00a0 < 2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.4062537\u00a0 0.1482219\u00a0\u00a0 2.741\u00a0\u00a0 0.0071 **<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.8909395\u00a0 0.1164971\u00a0\u00a0 7.648 6.54e-12 ***<\/p>\n
—<\/p>\n
Residual standard error: 0.04169 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.6629,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.6542<\/p>\n
F-statistic: 76.05 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
MDY:<\/strong><\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Estimate Std. Error t value Pr(>|t|)\u00a0\u00a0\u00a0<\/p>\n
(Intercept) 0.0009243\u00a0 0.0011596\u00a0\u00a0 0.797\u00a0\u00a0\u00a0 0.427\u00a0\u00a0\u00a0<\/p>\n
rmrf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.9659343\u00a0 0.0241262\u00a0 40.037\u00a0 < 2e-16 ***<\/p>\n
smb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.3705806\u00a0 0.0437178\u00a0\u00a0 8.477 8.49e-14 ***<\/p>\n
hml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.1572083\u00a0 0.0343607\u00a0\u00a0 4.575 1.20e-05 ***<\/p>\n
—<\/p>\n
Residual standard error: 0.0123 on 116 degrees of freedom<\/p>\n
Multiple R-squared: 0.9501,\u00a0\u00a0\u00a0\u00a0 Adjusted R-squared: 0.9488<\/p>\n
F-statistic: 736.8 on 3 and 116 DF,\u00a0 p-value: < 2.2e-16<\/p>\n
Fama-French 25 Portfolios – Regression Results (Nov. 2000 – October 2010)<\/strong><\/p>\n
\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
ALPHA<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
-0.68%<\/td>
-0.05%<\/td>
0.01%<\/td>
0.02%<\/td>
0.16%<\/td>\n\t<\/tr>\n\t
2<\/td>
-0.22%<\/td>
0.03%<\/td>
0.18%<\/td>
-0.09%<\/td>
-0.30%<\/td>\n\t<\/tr>\n\t
3<\/td>
-0.08%<\/td>
0.11%<\/td>
0.30%<\/td>
0.17%<\/td>
0.37%<\/td>\n\t<\/tr>\n\t
4<\/td>
0.17%<\/td>
0.20%<\/td>
0.01%<\/td>
0.15%<\/td>
-0.09%<\/td>\n\t<\/tr>\n\t
Large<\/td>
0.04%<\/td>
0.12%<\/td>
-0.11%<\/td>
-0.27%<\/td>
-0.18%<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
ALPHA (t-stat)<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
-2.41<\/td>
-0.28<\/td>
0.07<\/td>
0.16<\/td>
0.96<\/td>\n\t<\/tr>\n\t
2<\/td>
-1.42<\/td>
0.17<\/td>
1.36<\/td>
-0.67<\/td>
-2.06<\/td>\n\t<\/tr>\n\t
3<\/td>
-0.58<\/td>
0.71<\/td>
1.83<\/td>
0.88<\/td>
1.69<\/td>\n\t<\/tr>\n\t
4<\/td>
1.40<\/td>
1.28<\/td>
0.03<\/td>
0.75<\/td>
-0.43<\/td>\n\t<\/tr>\n\t
Large<\/td>
0.41<\/td>
0.86<\/td>
-0.69<\/td>
-2.06<\/td>
-0.68<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
BETA<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
1.18<\/td>
0.98<\/td>
0.83<\/td>
0.77<\/td>
0.98<\/td>\n\t<\/tr>\n\t
2<\/td>
1.09<\/td>
0.90<\/td>
0.84<\/td>
0.88<\/td>
1.04<\/td>\n\t<\/tr>\n\t
3<\/td>
1.09<\/td>
0.96<\/td>
0.89<\/td>
0.93<\/td>
0.97<\/td>\n\t<\/tr>\n\t
4<\/td>
1.06<\/td>
0.96<\/td>
1.02<\/td>
0.98<\/td>
1.11<\/td>\n\t<\/tr>\n\t
Large<\/td>
0.95<\/td>
0.86<\/td>
0.90<\/td>
0.90<\/td>
1.07<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
BETA (t-stat)<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
20.003<\/td>
28.263<\/td>
27.927<\/td>
24.682<\/td>
28.526<\/td>\n\t<\/tr>\n\t
2<\/td>
33.474<\/td>
26.726<\/td>
30.813<\/td>
31.303<\/td>
33.793<\/td>\n\t<\/tr>\n\t
3<\/td>
40.148<\/td>
29.188<\/td>
25.975<\/td>
22.508<\/td>
21.314<\/td>\n\t<\/tr>\n\t
4<\/td>
41.506<\/td>
29.263<\/td>
24.257<\/td>
23.706<\/td>
26.411<\/td>\n\t<\/tr>\n\t
Large<\/td>
52.533<\/td>
30.54<\/td>
28.014<\/td>
33.053<\/td>
19.277<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
h<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
-0.38<\/td>
0.03<\/td>
0.34<\/td>
0.52<\/td>
0.72<\/td>\n\t<\/tr>\n\t
2<\/td>
-0.37<\/td>
0.16<\/td>
0.43<\/td>
0.58<\/td>
0.90<\/td>\n\t<\/tr>\n\t
3<\/td>
-0.45<\/td>
0.16<\/td>
0.41<\/td>
0.58<\/td>
0.68<\/td>\n\t<\/tr>\n\t
4<\/td>
-0.38<\/td>
0.27<\/td>
0.45<\/td>
0.54<\/td>
0.83<\/td>\n\t<\/tr>\n\t
Large<\/td>
-0.28<\/td>
0.20<\/td>
0.30<\/td>
0.56<\/td>
0.60<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
h (t-stat)<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
-4.52<\/td>
0.61<\/td>
8.08<\/td>
11.65<\/td>
14.77<\/td>\n\t<\/tr>\n\t
2<\/td>
-7.91<\/td>
3.25<\/td>
10.96<\/td>
14.29<\/td>
20.67<\/td>\n\t<\/tr>\n\t
3<\/td>
-11.55<\/td>
3.47<\/td>
8.28<\/td>
9.83<\/td>
10.51<\/td>\n\t<\/tr>\n\t
4<\/td>
-10.31<\/td>
5.78<\/td>
7.53<\/td>
9.17<\/td>
13.87<\/td>\n\t<\/tr>\n\t
Large<\/td>
-10.96<\/td>
5.04<\/td>
6.59<\/td>
14.36<\/td>
7.59<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
s<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
1.18<\/td>
1.07<\/td>
0.94<\/td>
0.98<\/td>
1.06<\/td>\n\t<\/tr>\n\t
2<\/td>
1.01<\/td>
0.98<\/td>
0.92<\/td>
0.89<\/td>
1.12<\/td>\n\t<\/tr>\n\t
3<\/td>
0.74<\/td>
0.57<\/td>
0.47<\/td>
0.43<\/td>
0.66<\/td>\n\t<\/tr>\n\t
4<\/td>
0.45<\/td>
0.32<\/td>
0.28<\/td>
0.28<\/td>
0.19<\/td>\n\t<\/tr>\n\t
Large<\/td>
-0.19<\/td>
-0.12<\/td>
-0.02<\/td>
-0.10<\/td>
-0.23<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
s (t-stat)<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
11.11<\/td>
17.09<\/td>
17.53<\/td>
17.47<\/td>
17.08<\/td>\n\t<\/tr>\n\t
2<\/td>
17.15<\/td>
16.12<\/td>
18.43<\/td>
17.47<\/td>
20.16<\/td>\n\t<\/tr>\n\t
3<\/td>
14.96<\/td>
9.57<\/td>
7.61<\/td>
5.76<\/td>
7.98<\/td>\n\t<\/tr>\n\t
4<\/td>
9.63<\/td>
5.37<\/td>
3.66<\/td>
3.80<\/td>
2.49<\/td>\n\t<\/tr>\n\t
Large<\/td>
-5.95<\/td>
-2.40<\/td>
-0.32<\/td>
-2.08<\/td>
-2.29<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\n\n\n\t\n\t\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
R2<\/th>
Growth<\/th>
2<\/th>
3<\/th>
4<\/th>
Value<\/th>\n\t<\/tr>\n<\/thead>\n
Small<\/td>
0.87<\/td>
0.95<\/td>
0.96<\/td>
0.96<\/td>
0.96<\/td>\n\t<\/tr>\n\t
2<\/td>
0.93<\/td>
0.93<\/td>
0.92<\/td>
0.91<\/td>
0.90<\/td>\n\t<\/tr>\n\t
3<\/td>
0.93<\/td>
0.94<\/td>
0.90<\/td>
0.87<\/td>
0.88<\/td>\n\t<\/tr>\n\t
4<\/td>
0.93<\/td>
0.95<\/td>
0.87<\/td>
0.87<\/td>
0.92<\/td>\n\t<\/tr>\n\t
Large<\/td>
0.94<\/td>
0.96<\/td>
0.87<\/td>
0.89<\/td>
0.79<\/td>\n\t<\/tr>\n<\/tbody>\n<\/table>\n<\/pre>\n","protected":false},"excerpt":{"rendered":"
Note: This page contains the data source links and source code used in my “Fama-French Factor Loadings for Popular ETFs” post and my “Fundamental Indexing: Up and Running for 5 Years” post.\u00a0 If you are looking for a detailed tutorial on how to run the Fama-French regressions using R, then check out my screencast here. […]<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":145,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":[],"_links":{"self":[{"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/pages\/892"}],"collection":[{"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/comments?post=892"}],"version-history":[{"count":31,"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/pages\/892\/revisions"}],"predecessor-version":[{"id":916,"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/pages\/892\/revisions\/916"}],"up":[{"embeddable":true,"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/pages\/145"}],"wp:attachment":[{"href":"http:\/\/www.calculatinginvestor.com\/wp-json\/wp\/v2\/media?parent=892"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}