Exploiting Supply Chain Interdependencies for Stock Return Prediction: A Full-State Graph Convolutional LSTM
“While not achieving the lowest traditional prediction errors, FS-GCLSTM consistently delivers superior portfolio performance, attaining the highest annualized returns, Sharpe ratios, and Sortino ratios across both markets.”
AssetEquitiesindices
StrategyMachine learning
TechniqueRecurrent neural networkfeedforward network
GeographyUS
Also stated: Sharpe ratio, Information ratio, Excess return, Profit, Long-short, Fundamentals, benchmark
Missing from abstract: out-of-sample test, costs, multiple testing, universe
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