Multi-time Window Ensemble and Maximization of Expected Return for Stock Movement Prediction

This paper proposes a novel end-to-end model that predicts stock movements, MERTE: Maximization of Expected Returns in multi-Time window Ensemble for stock movement prediction. MERTE is based on three main ideas: 1) an ensemble framework to capture multiple time-based momentums; 2) consolidating the expected return of trading to a loss function; and 3) learning correlations between the stocks without pre-defined knowledge. MERTE consists of several base learners with the same neural network structure, but each receives an input of a different time-sequential length. The base learner specializes in learning the time momentum inherent in its given time window, and it also learns trading performance throughout our proposed loss function. The base learner consists of two attention mechanisms to learn correlations and dynamics of the stock movements without any domain knowledge. Experimental results report that MERTE outperforms baseline models, yielding superior trading gains on almost all six real-world datasets.