capm模型检验(电子表格-版-含推导-分析-数据-结论)

"CAPM模型其实质是讨论风险与收益的关系,其基本的验证思路是考察是否只有股票的系统风险(用β系数代表)与其收益有关,而且这两者为线性正相关。它是对股票收益率的事前预测，把其变成类似计量经济学回归的表达式也就是CAPM模型的事后形式，本次通过EVIEWS进行回归分析验证CAPM模型在此股票上是否有效。见下式：

E(Rj)-Rf=(E(Rm)-Rf)βj(1)//这是CAPM的原本模型

Rj-Rf=C+(Rm-Rf)βj+µ(2)//对CAPM模型的变换式子，在本分析中，Rj-Rf用因变量Y表示，Rm-Rf用自变量X表示，C代表截距项,µ代表残差项。

则模型最终变为：Y=C+Xβj+µ

若（2）要接受CAPM,则应在回归方程显著的条件下同时接受如下的两个假设:

(1)接受H0:C=0的假设;

(2)拒绝H1:βj=0的假设.

变量说明：

Rf为无风险收益率，用t时期3个月的银行定期存款利率表示；

Rm为市场组合的期望收益率，用t时刻的上证指数日回报率表示；

Rj为个股回报率，计算公式如下：Rjt=(Pjt-Pjt–1)/Pjit-1；

βj是股票j的收益率对市场组合收益率的回归方程的斜率，常被称为“β系数”。

本文的数据取自上证A股2013年4月1日到2013年5月22日的十支股票。

",,,,,,

股票名称：,股票代码：,Variable,Coefficient,Std.Error,t-Statistic,Prob.

1.三一重工,600031,X,-2.447954,3.243226,-0.75479,0.4553

,,C,-0.194291,0.15955,-1.217745,0.2312

,,,,,,,

,,R-squared,0.015579,Meandependentvar,,-0.075549

,,AdjustedR-squared,-0.011766,S.D.dependentvar,,0.162949

,,S.E.ofregression,0.163904,Akaikeinfocriterion,,-0.727871

,,Sumsquaredresid,0.967127,Schwarzcriterion,,-0.641682

,,Loglikelihood,15.82955,F-statistic,,0.569708

,,Durbin-Watsonstat,1.012855,Prob(F-statistic),,0.455285

,,,,,,,

,,,,,,,

,,Variable,Coefficient,Std.Error,t-Statistic,Prob.

2.航天机电,600152,X,-2.268172,3.287982,-0.689837,0.4947

,,C,-0.176194,0.161752,-1.089285,0.2833

,,,,,,,

,,R-squared,0.013046,Meandependentvar,,-0.066172

,,AdjustedR-squared,-0.014369,S.D.dependentvar,,0.164985

,,S.E.ofregression,0.166166,Akaikeinfocriterion,,-0.70046

,,Sumsquaredresid,0.994004,Schwarzcriterion,,-0.614271

,,Loglikelihood,15.30874,F-statistic,,0.475875

,,Durbin-Watsonstat,1.060726,Prob(F-statistic),,0.49472

,,,,,,,

,,,,,,,

3.四川路桥,600039,Variable,Coefficient,Std.Error,t-Statistic,Prob.

,,X,-2.139265,3.276311,-0.652949,0.5179

,,C,-0.177371,0.161178,-1.100471,0.2784

,,,,,,,

,,R-squared,0.011704,Meandependentvar,,-0.073602

,,AdjustedR-squared,-0.015748,S.D.dependentvar,,0.164288

,,S.E.ofregression,0.165576,Akaikeinfocriterion,,-0.707572

,,Sumsquaredresid,0.98696,Schwarzcriterion,,-0.621383

,,Loglikelihood,15.44387,F-statistic,,0.426343

,,Durbin-Watsonstat,1.044981,Prob(F-statistic),,0.517937

,,,,,,,

4.凤凰光学,600071,Variable,Coefficient,Std.Error,t-Statistic,Prob.

,,X,-2.135465,3.263153,-0.654417,0.517

,,C,-0.179289,0.16053,-1.116856,0.2715

,,,,,,,

,,R-squared,0.011756,Meandependentvar,,-0.075704

,,AdjustedR-squared,-0.015695,S.D.dependentvar,,0.163632

,,S.E.ofregression,0.164911,Akaikeinfocriterion,,-0.71562

,,Sumsquaredresid,0.979048,Schwarzcriterion,,-0.629431

,,Loglikelihood,15.59678,F-statistic,,0.428262

,,Durbin-Watsonstat,1.050367,Prob(F-statistic),,0.517002

,,,,,,,

5.中金黄金,600489,Variable,Coefficient,Std.Error,t-Statistic,Prob.

,,X,-2.892332,3.228677,-0.895826,0.3763

,,C,-0.217314,0.158834,-1.368183,0.1797

,,,,,,,

,,R-squared,0.021806,Meandependentvar,,-0.077016

,,AdjustedR-squared,-0.005366,S.D.dependentvar,,0.162733

,,S.E.ofregression,0.163169,Akaikeinfocriterion,,-0.736863

,,Sumsquaredresid,0.95847,Schwarzcriterion,,-0.650674

,,Loglikelihood,16.0004,F-statistic,,0.802504

,,Durbin-Watsonstat,1.029506,Prob(F-statistic),,0.376297

,,,,,,,

,,,,,,,

6.方兴科技,600552,Variable,Coefficient,Std.Error,t-Statistic,Prob.

,,X,-2.436679,3.288756,-0.740912,0.4636

,,C,-0.19188,0.16179,-1.185982,0.2434

,,,,,,,

,,R-squared,0.01502,Meandependentvar,,-0.073684

,,AdjustedR-squared,-0.012341,S.D.dependentvar,,0.165189

,,S.E.ofregression,0.166205,Akaikeinfocriterion,,-0.699989

,,Sumsquaredresid,0.994472,Schwarzcriterion,,-0.613801

,,Loglikelihood,15.2998,F-statistic,,0.548951

,,Durbin-Watsonstat,1.115256,Prob(F-statistic),,0.463552

,,,,,,,

,,,,,,,

7.江苏舜天,600827,Variable,Coefficient,Std.Error,t-Statistic,Prob.

,,X,-2.552558,3.254945,-0.784209,0.438

,,C,-0.194703,0.160127,-1.215933,0.2319

,,,,,,,

,,R-squared,0.016796,Meandependentvar,,-0.070886

,,AdjustedR-squared,-0.010515,S.D.dependentvar,,0.163639

,,S.E.ofregression,0.164497,Akaikeinfocriterion,,-0.720657

,,Sumsquaredresid,0.974129,Schwarzcriterion,,-0.634469

,,Loglikelihood,15.69249,F-statistic,,0.614984

,,Durbin-Watsonstat,1.018081,Prob(F-statistic),,0.438047

,,,,,,,

,,,,,,,

8.凯乐科技,600260,Variable,Coefficient,Std.Error,t-Statistic,Prob.

,,X,-3.110213,3.242644,-0.95916,0.3439

,,C,-0.223097,0.159521,-1.398538,0.1705

,,,,,,,

,,R-squared,0.024918,Meandependentvar,,-0.07223

,,AdjustedR-squared,-0.002167,S.D.dependentvar,,0.163698

,,S.E.ofregression,0.163875,Akaikeinfocriterion,,-0.72823

,,Sumsquaredresid,0.966781,Schwarzcriterion,,-0.642041

,,Loglikelihood,15.83636,F-statistic,,0.919987

,,Durbin-Watsonstat,1.045083,Prob(F-statistic),,0.343876

,,,,,,,

,,,,,,,

9.古越龙山,600059,Variable,Coefficient,Std.Error,t-Statistic,Prob.

,,X,-2.535157,3.252968,-0.779336,0.4409

,,C,-0.196876,0.160029,-1.230249,0.2266

,,,,,,,

,,R-squared,0.016591,Meandependentvar,,-0.073903

,,AdjustedR-squared,-0.010726,S.D.dependentvar,,0.163522

,,S.E.ofregression,0.164397,Akaikeinfocriterion,,-0.721873

,,Sumsquaredresid,0.972946,Schwarzcriterion,,-0.635684

,,Loglikelihood,15.71558,F-statistic,,0.607365

,,Durbin-Watsonstat,1.012261,Prob(F-statistic),,0.440875

,,,,,,,

,,,,,,,

10.鄂尔多斯,600295,Variable,Coefficient,Std.Error,t-Statistic,Prob.

,,X,-2.574677,3.241956,-0.794174,0.4323

,,C,-0.197685,0.159488,-1.239498,0.2232

,,,,,,,

,,R-squared,0.017218,Meandependentvar,,-0.072795

,,AdjustedR-square