ARIMA forecast insensitive to inclusion of exogenous variables I'm conducting an economic forecast based on an ARIMA time series model with multiple independent variables. I'm using a daily time series data that contains about 2 years of daily data inputs for a sum of 8 different regressors.  
My ARIMA fit is so good so far but I'm wondering why the external regressors are not of a great effect to the predicted values. In my model the predicted values and $R^2$ are only slightly affected regardless of whether I include the external regressors or not. 
In both cases the number of observations is equal and the adjusted R2=0.97 (Predicted value is not of a great change).
Could anybody help me with the significance of this phenomenon?
Update:
The case is now solved as shown in the following graphs:
1: The out of sample prediction without regressors
 
2: The out of sample prediction with signficative regressors
 
Thanks Mr. Richard Hardy for your help. It is highly appreciated
 A: Here is a summary of how the issue was presented and how it was addressed.


*

*Comparing two models with and without certain exogenous variables showed the following:
(A) inclusion of exogenous variables lowers information criteria (AIC, BIC) considerably...
(B) ...but barely affects the $R^2_{adj.}$ (that is very high anyway at about 0.97).

*Points of concern were:
(A) dependent variable was integrated; hence, $R^2_{adj.}$ was little relevant;
(B) exogenous variables were integrated, potentially leading to unbalanced regression;
(C) out-of-sample forecasts were not assessed.

*Points of concern were addressed as follows:
(A) $R^2_{adj.}$ not given consideration;
(B) exogenous variables were differenced;
(C) out of sample forecasts were assessed and found to differ significantly between the models with and without the exogenous variables.

*The finding in 3. (C) is now in line with the observation in 2. (A).  Puzzle solved. 
As of now, the forecast without regressors is going down in a weird way. Perhaps both pictures depict the forecasts from the same model that includes the exogenous variables -- but in the first picture the future values of the exogenous variables are set to zero? The idea was to have two different models, not the same model with two different sets of future values of the exogenous variables.
