Comparative Evaluation of Modern Deep Learning Methodologies for Portfolio Optimization
Portfolio optimization in equities over 2015–2023, studied by Ozechi and co-authors in 2026 with a transformer. The abstract mentions a Sharpe ratio, profits, drawdowns and a benchmark but not an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Results show that hybrid models provide superior stability and risk control, with Transformer+GNN achieving the lowest volatility and drawdown.”
AssetEquitiesETFs, bonds
StrategyPortfolio optimizationmachine learning
TechniqueTransformerfeedforward network
Sample window2015–2023
Also stated: Sharpe ratio, Profit, Drawdown, benchmark
Missing from abstract: out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodPortfolio optimization by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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