How to balance financial returns with metalearning for trend prediction
DOI:
https://doi.org/10.5753/jidm.2024.3371Keywords:
market movement, metalearning, stock market, machine learningAbstract
The prediction of market price movement is an essential tool for decision-making in trading scenarios. However, there are several candidate methods for this task. Metalearning can be an important ally for the automatic selection of methods, which can be machine learning algorithms for classification tasks, named here classification algorithms. In this work, we present the use of metalearning for classification in market movement prediction and elaborate new analyses of its statistical implications. Different setups and metrics were evaluated for the meta-target selection. Cumulative return was the metric that achieved the best meta and base-level results. According to the experimental results, metalearning was a competitive selection strategy for predicting market price movement. This work is an extension of Bandeira et. al[2022].
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