A generalization of multi-source fusion-based framework to stock selection

Václav Snásel, Juan D. Velásquez, Millie Pant, Dimitrios Georgiou, Lingping Kong

Abstract: Highlights•We generalize a fusion-based model which performs Do-It-Yourself stock selection.•This model integrates data and decision-level fusion to extend a single model’s strength.•We propose incorporating dynamic time warping to observe the prediction loss, increasing accuracy.•We present the platform in a real-life problem, justify its effectiveness and practicality.