Question on lm(): When does R-squared come out as NA?

I have a situation with a large dataset (3000+ observations), where
I'm doing lags as regressors, where I get:

Call:
lm(formula = rj ~ rM + rM.1 + rM.2 + rM.3 + rM.4)

Residuals:
1990-06-04 1994-11-14 1998-08-21 2002-03-13 2005-09-15
 -5.64672   -0.59596   -0.04143    0.55412    8.18229

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.003297   0.017603  -0.187    0.851
rM           0.845169   0.010522  80.322   <2e-16 ***
rM.1         0.116330   0.010692  10.880   <2e-16 ***
rM.2         0.002044   0.010686   0.191    0.848
rM.3         0.013181   0.010692   1.233    0.218
rM.4         0.009587   0.010525   0.911    0.362
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.044 on 3532 degrees of freedom
Multiple R-Squared:    NA,	Adjusted R-squared:    NA
F-statistic:    NA on 5 and 3532 DF,  p-value: NA


rM.1, rM.2, etc. are lagged values of rM. The OLS seems fine in every
respect, except that there is an NA as the multiple R-squared. I will
be happy to give sample data to someone curious about what is going
on. I wondered if this was a well-known pathology. The way I know it,
if the data allows computation of (X'X)^{-1}, one can compute the R2.

-- 
Ajay Shah                                                   Consultant
ajayshah at mayin.org                      Department of Economic Affairs
http://www.mayin.org/ajayshah           Ministry of Finance, New Delhi

I've not seen a reply to this, nor ever seen it.
Please make a reproducible example available (do see the posting guide).

lm(formula = rj ~ rM + rM.1 + rM.2 + rM.3 + rM.4)

Residuals:
1990-06-04 1994-11-14 1998-08-21 2002-03-13 2005-09-15
-5.64672   -0.59596   -0.04143    0.55412    8.18229

Coefficients:
           Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.003297   0.017603  -0.187    0.851
rM           0.845169   0.010522  80.322   <2e-16 ***
rM.1         0.116330   0.010692  10.880   <2e-16 ***
rM.2         0.002044   0.010686   0.191    0.848
rM.3         0.013181   0.010692   1.233    0.218
rM.4         0.009587   0.010525   0.911    0.362
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.044 on 3532 degrees of freedom
Multiple R-Squared:    NA,	Adjusted R-squared:    NA
F-statistic:    NA on 5 and 3532 DF,  p-value: NA


rM.1, rM.2, etc. are lagged values of rM. The OLS seems fine in every
respect, except that there is an NA as the multiple R-squared. I will
be happy to give sample data to someone curious about what is going
on. I wondered if this was a well-known pathology. The way I know it,
if the data allows computation of (X'X)^{-1}, one can compute the R2.