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The paper proposes a single-level optimization formulation that incorporates KKT conditions into decision-focused learning for mean-variance portfolio optimization, improving performance in experiments on real-world ETF data.
A Stanford professor showed that a simple daily rebalancing strategy with two ordinary stocks can turn $100,000 into $7 million by leveraging volatility and compounding, without needing price predictions.
A PyTorch-enabled, GPU-accelerated clustering pipeline optimizes financial portfolio grouping at scale, delivering hard labels, soft factor loadings, and structural-break signals efficiently for instruments up to a million.
Titans-QFWP is a hybrid reinforcement learning architecture integrating a Quantum Fast Weight Programmer with Titans-style memory for adaptive portfolio optimization, achieving strong performance on S&P 500 stocks.
Introduces a full-stack hands-on Python quantitative trading video tutorial, covering data scraping, machine learning prediction, portfolio optimization, and strategy backtesting, and mentions the open-source quantitative trading execution system Kungfu.
Proposes using neural predicates to generate structured probabilistic views for the Black-Litterman model, improving reproducibility and interpretability in portfolio construction.
Introduces RL-NSGA-II-GRC, a method integrating reinforcement learning with the NSGA-II genetic algorithm enhanced by gray relational coefficients for multi-objective optimization, applied to NASDAQ portfolio optimization. Achieves improved convergence and diversified Pareto fronts.
Proposes learned predictive ambiguity sets (LPAS) for distributionally robust optimization, where a deep contextual model outputs a nominal scenario distribution, state-dependent Wasserstein radius, and ground metric, trained with decision loss and calibration. Applied to portfolio optimization on S&P 500 data, the method achieves higher returns and Sharpe ratio with reduced conservatism compared to fixed-radius baselines.
This paper proposes a deep reinforcement learning framework (MORP-DRL) for multi-objective reliability-based portfolio optimization, jointly optimizing expected return and downside risk using CVaR and EVaR under practical constraints, and demonstrates performance on global equity indices across different market regimes.
This paper proposes an end-to-end decision-focused learning framework for sparse tangent portfolio optimization that replaces discrete asset selection with a smooth top-k operator, enabling gradient flow through prediction and optimization to directly maximize Sharpe ratio.
FlowBank introduces a three-stage framework for optimizing agentic workflows in LLM multi-agent systems by precomputing a diverse set of reusable workflows and adaptively selecting the best one per query, achieving higher scores while maintaining cost competitiveness.
This paper presents a decision support framework for optimizing validator selection in proof-of-stake blockchains, balancing portfolio quality and diversification through multi-objective optimization and interactive preference learning.
This paper introduces Semantic State Abstraction Interfaces (SSAI) to separate representation hypotheses from optimization variance in LLM-augmented portfolio decisions. It concludes that SSAI's apparent advantage is largely a basket-selection effect, with dense encodings and principal components performing better empirically.