# Factor Model Regression Market Factor Statistically Insignificant After Adding Extra Factors

I am running a simple 4 factor model which includes the factors: Benchmark (Market - FortyConsumerSixtyHealthcare), SMB, HML and MOM.

When I simply apply the regression to the portfolio returns and the benchmark I get a statistically significant result and a fairly strong Beta of 0.4262:

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coef    std err          t      P>|t|      [0.025      0.975]
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Intercept                        0.0192      0.006      2.973      0.004       0.006       0.032
FortyConsumerSixtyHealthcare(BM) 0.4262      0.178      2.398      0.018       0.073       0.779


However when I run the model with the other factors included the significance is lost and the loading is also reduced significantly to 0.1809:

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coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------------------
Intercept                        0.0233      0.006      3.902      0.000       0.011       0.035
FortyConsumerSixtyHealthcare(BM) 0.1809      0.171      1.060      0.292      -0.158       0.520
SMB                              0.0085      0.003      3.374      0.001       0.003       0.013
HML                             -0.0017      0.002     -0.706      0.482      -0.007       0.003
MOM                             -0.0055      0.002     -2.589      0.011      -0.010      -0.001


The only statistically significant factors now seem to be size and momentum both of which have tiny loadings.

I am not sure why the loading on the benchmark has changed so significantly by adding other factors and why it is now not statistically significant, surely this would lead someone to think that the portfolio is market neutral when in fact it has a beta 0.4262?