A gradient-based sample generation framework is proposed to identify macroeconomic conditions inducing specific behaviors in portfolio optimization pipelines.
A universal end-to-end approach to portfolio optimization via deep learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
End-to-end AI policies for cross-asset futures timing outperform rules-based benchmarks on pooled portfolios but vary by asset class, with transformers showing better cost-adjusted performance than LSTMs.
A systematic review of physics-informed neural networks and mathematical modeling approaches for portfolio optimization and management in finance.
citing papers explorer
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Generating Input Distributions for Explaining Portfolio Optimization Pipelines
A gradient-based sample generation framework is proposed to identify macroeconomic conditions inducing specific behaviors in portfolio optimization pipelines.
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End-to-End Parametric Portfolio Policies for Cross-Asset Futures Timing: When Do AI Models Beat Simple Rules?
End-to-end AI policies for cross-asset futures timing outperform rules-based benchmarks on pooled portfolios but vary by asset class, with transformers showing better cost-adjusted performance than LSTMs.
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A Systematic Review of Recent Advancements in PINN Augmented Deep Learning and Mathematical Modeling for Efficient Portfolio Management
A systematic review of physics-informed neural networks and mathematical modeling approaches for portfolio optimization and management in finance.