Previous studies of an automated detection of Major Depression in adolescents based on acoustic speech analysis identified the glottal and the Teager Energy features as the strongest correlates of depression. This study investigates the effectiveness of these features in an early prediction of Major Depression in adolescents using a fully automated speech analysis and classification system. The prediction was achieved through a binary classification of speech recordings from 15 adolescents who developed Major Depression within two years after these recordings were made and 15 adolescents who did not developed Major Depression within the same time period. The results provided a proof of concept that an acoustic speech analysis can be used in early prediction of depression. The glottal features made the strongest predictors of depression with 69% accuracy, 62% specificity and 76% sensitivity. The TEO feature derived from glottal wave also provided good results, specifically when calculated at the frequency range of 1.3 kHz to 5.5 kHz.