Abstract:The potential of machine learning has sustained the interest of both academia and industry in stock market prediction over the past decade. This paper aims to integrate modern techniques such as Gradient Boosting Machines (GBMs) into a novel ensemble called CalixBoost which is a resource-efficient and accurate stock index predictor. Data comprising macro-economic metrics and technical financial indicators, as well as sentiment analysis of social media using a simple and fast but effective rule-based model are … Show more
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