Dynamic Portfolio Management with Machine Learning
Portfolio optimization, studied by Huang and co-authors in 2021 with a recurrent neural network. The abstract mentions an out-of-sample test but not a benchmark, transaction costs, multiple testing or the size of the universe.
“We shrink Wishart volatility towards a Graphical Lasso initial covariance estimator and solve the portfolio optimization using a fast coordinate descent algorithm with regularization determined using a genetic algorithm.”
StrategyPortfolio optimizationlow-volatility anomaly
TechniqueRecurrent neural networkfeedforward network
FrequencyMonthly
Long/shortLong-short
Also stated: out-of-sample test
Missing from abstract: benchmark, costs, multiple testing, universe
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The paper's neighbourhoodPortfolio optimization by year
FamilyPortfolio optimization105Momentum and trend following289Factor investing240Machine learning return prediction15318 more · 897
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