Mean Absolute Error increasing with more correlated factors

I am using Microsoft Azure Machine Learning Studio to predict stock market prices. We have the variables- Index price(target-to be predicted),Low price,High price,dates and days. We use split of 0.7 and run Linear regression. We get Mean absolute error of 109. We then try to add more variables(macroeconomic factors which positively effect the index prices) which are correlated with the target variable and should improve the predictions- we find that the Mean Absolute error increases to 110.I have attached the pics for your reference. Are we interpreting wrong or what’s wrong in we are doing?
PS:We tried Boosted Tree regression as well-but the same problem as described above is observed.
Errors