This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github.