Most branch predictors use the PC information of the branch instruction and its dynamic Global Branch History (GBH). In this letter, we suggest a Branch Direction History (BDH) as the third component of the branch prediction and analyze its impact upon the prediction accuracy. Additionally, we propose a new branch predictor, direction-gshare predictor, which utilizes the BDH combined with the GBH. At first, we model a neural network with (PC, GBH, and BDH) and analyze their actual impact upon the branch prediction accuracy, and then we simulate our new predictor, the direction-gshare predictor. The simulation results show that the aliasing in Pattern History Table (PHT) is significantly reduced by the additional use of BDH information. The direction-gshare predictor outperforms bimodal predictor, two-level adaptive predictor and gshare predictor up to 15.32%, 5.41% and 5.74% respectively, without additional hardware costs.