Fake news detection has recently gained much attention from the wider NLP community due to its importance for preventing the spread of misinformation and its negative impact through the social media. The goal of this task is to classify the veracity labels of a statement expressed by a politician into fine-grained classes (degrees of truth). Previous deep learning approaches have significantly improved the performance of Political Fake Statement Detection by modeling statement with the speaker's credit history. However, the credit history may not be available in reality and most approaches did not consider about the evidence that supporting or denying claims when detecting fake news. In addition, state-of-the-art models may struggle to detect fine-grained labels because the statement of the speaker expresses factual and incorrect instances at the same time. In this paper, we approach the Political Fake Statement Detection problem by proposing two multi-stage feature-assisted neural models that consider claims and justifications as an input in a stance detection manner. We explore five-stage and three-stage classification strategies to better discern between the fine-grained labels of fake news. The proposed model in each stage is built on the powerful combination between dual GRU layers and lexical features which we further optimise by using Gaussian Noise. An extensive experimental work on a real-world benchmark LIARPLUS (an extended version of LIAR) dataset shows that three-stage model achieves state-of-the-art Accuracy (46.13%) and F1score (45.13%) without using metadata and the credit history of the speaker. We also experimentally show that modeling the credit history in conjunction with statement and justification gives more than 6% improvement (e.g. 52.23% and 52.26% respectively).