SMMF: Square-Matricized Momentum Factorization for Memory-Efficient Optimization
A 2024 paper by Park and Lee on momentum and trend following, using a transformer: the abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“In our experiment, SMMF takes up to 96% less memory compared to state-of-the-art memory efficient optimizers, e.g., Adafactor, CAME, and SM3, while achieving comparable model performance on various CNN and Transformer tasks.”
StrategyMomentum
TechniqueTransformerconvolutional network
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodMomentum and trend following by year
FamilyMomentum and trend following289Factor investing240Machine learning return prediction153News sentiment and text signals14818 more · 854
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