capm模型检验(Excel 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β系数表)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。见下式：

E(Rj)-Rf=(E(Rm)-Rf)βj (1)//这是CAPM的原本模型

股票名称：股票代码：Variable Coefficient Std. Error

1.三一重工600031X-

2.447954

3.243226

C-0.1942910.15955

R-squared0.015579 Mean dependent var

Adjusted R-squared-0.011766 S.D. dependent var

S.E. of regression0.163904 Akaike inf

Sum squared resid0.967127 Schwarz criterion

Log likelihood15.82955 F-statistic

Durbin-Watson stat 1.012855 Prob(F-statistic)

Variable Coefficient Std. Error

2.航天机电600152X-2.268172

3.287982

C-0.1761940.161752

R-squared0.013046 Mean dependent var

Adjusted R-squared-0.014369 S.D. dependent var

S.E. of regression0.166166 Akaike inf

Sum squared resid0.994004 Schwarz criterion

Log likelihood15.30874 F-statistic

Durbin-Watson stat 1.060726 Prob(F-statistic)

3.四川路桥600039Variable Coefficient Std. Error

X-2.139265 3.276311

C-0.1773710.161178

R-squared0.011704 Mean dependent var

Adjusted R-squared-0.015748 S.D. dependent var

S.E. of regression0.165576 Akaike inf

Sum squared resid0.98696 Schwarz criterion

Log likelihood15.44387 F-statistic

Durbin-Watson stat 1.044981 Prob(F-statistic)

4.凤凰光学600071Variable Coefficient Std. Error

X-2.135465 3.263153

C-0.1792890.16053

R-squared0.011756 Mean dependent var

S.E. of regression0.164911 Akaike inf

Sum squared resid0.979048 Schwarz criterion

Log likelihood15.59678 F-statistic

Durbin-Watson stat 1.050367 Prob(F-statistic) 5.中金黄金600489Variable Coefficient Std. Error

X-2.892332 3.228677

C-0.2173140.158834

R-squared0.021806 Mean dependent var

Adjusted R-squared-0.005366 S.D. dependent var

S.E. of regression0.163169 Akaike inf

Sum squared resid0.95847 Schwarz criterion

Log likelihood16.0004 F-statistic

Durbin-Watson stat 1.029506 Prob(F-statistic) 6.方兴科技600552Variable Coefficient Std. Error

X-2.436679 3.288756

C-0.191880.16179

R-squared0.01502 Mean dependent var

Adjusted R-squared-0.012341 S.D. dependent var

S.E. of regression0.166205 Akaike inf

Sum squared resid0.994472 Schwarz criterion

Log likelihood15.2998 F-statistic

Durbin-Watson stat 1.115256 Prob(F-statistic) 7.江苏舜天600827Variable Coefficient Std. Error

X-2.552558 3.254945

C-0.1947030.160127

R-squared0.016796 Mean dependent var

Adjusted R-squared-0.010515 S.D. dependent var

S.E. of regression0.164497 Akaike inf

Sum squared resid0.974129 Schwarz criterion

Log likelihood15.69249 F-statistic

Durbin-Watson stat 1.018081 Prob(F-statistic) 8.凯乐科技600260Variable Coefficient Std. Error

X-3.110213 3.242644

C-0.2230970.159521

R-squared0.024918 Mean dependent var

S.E. of regression0.163875 Akaike inf

Sum squared resid0.966781 Schwarz criterion

Log likelihood15.83636 F-statistic

Durbin-Watson stat 1.045083 Prob(F-statistic) 9.古越龙山600059Variable Coefficient Std. Error

X-2.535157 3.252968

C-0.1968760.160029

R-squared0.016591 Mean dependent var

Adjusted R-squared-0.010726 S.D. dependent var

S.E. of regression0.164397 Akaike inf

Sum squared resid0.972946 Schwarz criterion

Log likelihood15.71558 F-statistic

Durbin-Watson stat 1.012261 Prob(F-statistic) 10.鄂尔多斯600295Variable Coefficient Std. Error

X-2.574677 3.241956

C-0.1976850.159488

R-squared0.017218 Mean dependent var

Adjusted R-squared-0.010081 S.D. dependent var

S.E. of regression0.16384 Akaike inf

Sum squared resid0.96637 Schwarz criterion

Log likelihood15.84443 F-statistic

Durbin-Watson stat 1.035606 Prob(F-statistic)

t-Statistic Prob.

-0.754790.4553

-1.2177450.2312 Mean dependent var-0.075549 S.D. dependent var0.162949

e info criterion-0.727871 Schwarz criterion-0.641682 F-statistic0.569708 Prob(F-statistic)0.455285

t-Statistic Prob.

-0.6898370.4947

-1.0892850.2833 Mean dependent var-0.066172 S.D. dependent var0.164985

e info criterion-0.70046 Schwarz criterion-0.614271 F-statistic0.475875 Prob(F-statistic)0.49472

t-Statistic Prob.

-0.6529490.5179

-1.1004710.2784 Mean dependent var-0.073602 S.D. dependent var0.164288

e info criterion-0.707572 Schwarz criterion-0.621383 F-statistic0.426343 Prob(F-statistic)0.517937

t-Statistic Prob.

-0.6544170.517

-1.1168560.2715 Mean dependent var-0.075704

有股票的系统风险(用β系数代事前预测，把其变成类似计量经济学回分析验证CAPM模型在此股票上是否有效

e info criterion-0.71562 Schwarz criterion-0.629431 F-statistic0.428262 Prob(F-statistic)0.517002

t-Statistic Prob.

-0.8958260.3763

-1.3681830.1797 Mean dependent var-0.077016 S.D. dependent var0.162733

e info criterion-0.736863 Schwarz criterion-0.650674 F-statistic0.802504 Prob(F-statistic)0.376297

t-Statistic Prob.

-0.7409120.4636

-1.1859820.2434 Mean dependent var-0.073684 S.D. dependent var0.165189

e info criterion-0.699989 Schwarz criterion-0.613801 F-statistic0.548951 Prob(F-statistic)0.463552

t-Statistic Prob.

-0.7842090.438

-1.2159330.2319 Mean dependent var-0.070886 S.D. dependent var0.163639

e info criterion-0.720657 Schwarz criterion-0.634469 F-statistic0.614984 Prob(F-statistic)0.438047

t-Statistic Prob.

-0.959160.3439

-1.3985380.1705 Mean dependent var-0.07223

e info criterion-0.72823 Schwarz criterion-0.642041 F-statistic0.919987 Prob(F-statistic)0.343876

t-Statistic Prob.

-0.7793360.4409

-1.2302490.2266 Mean dependent var-0.073903 S.D. dependent var0.163522

e info criterion-0.721873 Schwarz criterion-0.635684 F-statistic0.607365 Prob(F-statistic)0.440875

t-Statistic Prob.

-0.7941740.4323

-1.2394980.2232 Mean dependent var-0.072795 S.D. dependent var0.163021

e info criterion-0.728654 Schwarz criterion-0.642465 F-statistic0.630712 Prob(F-statistic)0.432299