Papers Portfolio Optimization
“Portfolio Optimization” 태그가 달린 논문 509편 · 필터 해제
Optimal Momentum Methods for Stochastic Multilevel Compositional Optimization
This paper investigates stochastic multi-level optimization where the objective is a nested composition of several smooth non-convex functions. We assume that only stochastic estimates of the gradient and function values…
Portfolio OptimizationLLMDE: A Large Language Model-Driven Differential Evolution Algorithm for Portfolio Optimization
This study proposes a Large Language Model-Driven Differential Evolution (LLMDE) algorithm to reduce the reliance on handcrafted hyperparameter design. The proposed algorithm leverages a prompt engineering strategy, allo…
Portfolio OptimizationPrompt EngineeringLearning Discrete Decisions for MIPs with Constraint-Aware Diffusion
This paper proposes a novel learning-based approach to approximately solve instances of mixed-integer optimization problems. These problems are computationally challenging, as they require jointly determining discrete an…
Portfolio OptimizationNeural Network-Driven Volatility Drag Mitigation under Aggressive Leverage
This paper introduces a compact reformulation of a modular end-to-end neural network for global minimum-variance portfolio optimization that decouples model complexity from both look-back window length and universe size.…
Portfolio OptimizationLarge-Scale Portfolio Optimization Problem Under Cardinality Constraint With Enhanced Multi-Objective Evolutionary Algorithms
Decision-making is posing an increasingly formidable challenge to investors because of the growing number of alternatives available in financial markets. A hot area of research over the past few decades has been portfoli…
Portfolio OptimizationDeep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization
Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints. Existing reliability bas…
Reinforcement LearningPortfolio OptimizationDecision MakingDecision-focused Sparse Tangent Portfolio Optimization
Sparse tangent portfolio optimization aims to learn an interpretable, low-cardinality portfolio in the tangency direction of the mean-variance frontier. However, the associated cardinality-constrained formulation is NP-h…
Portfolio OptimizationDirected Graph Topology Inference via Graph Filter Identification
We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs of a graph convolutional filter, i.e., a …
Portfolio OptimizationGenerating Input Distributions for Explaining Portfolio Optimization Pipelines
We propose a predict-optimize-explain framework that uses gradient-based sample generation to interpret various portfolio models by identifying macroeconomic conditions that induce specified portfolio outcomes. Unlike tr…
Portfolio OptimizationA Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis
The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously. This paper presents a groundbreaking unifie…
Reinforcement LearningPortfolio OptimizationSentiment AnalysisAddressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman
Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data. However, these models fail to account for fat-tailed re…
Reinforcement LearningPortfolio OptimizationScaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models. However, its practical deployment is often hindered by high computa…
Computational EfficiencyPortfolio OptimizationBenchmarking Quantum Algorithmic Resilience for CVaR Portfolio Optimization: The Expressibility-Coherence Trade-off
Quantum combinatorial optimization offers theoretical advantages for complex financial modeling, but physical implementation on Noisy Intermediate Scale Quantum (NISQ) devices is severely constrained by hardware topology…
Portfolio OptimizationFinancially Guided Deep Portfolio Optimization
Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction costs. Standard predict-then-optimize methods first forecast returns and then solv…
Portfolio OptimizationBeyond ESG Scores: Learning Dynamic Constraints for Sequential Portfolio Optimization
ESG-aware portfolio optimization is increasingly important for sustainable capital allocation, yet most learning-based methods still operationalize ESG by appending static scores to the policy observation or reward. This…
Portfolio OptimizationRobust Out-of-Distribution Stochastic Optimization
Data-driven decision-making under uncertainty typically presumes the collection of historical data from an unknown target probability distribution. However, one may have no access to any data from the target distribution…
Stochastic OptimizationPortfolio OptimizationSTRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction
Credit risk default prediction remains a cornerstone of risk management in the financial industry. The task involves estimating the likelihood that a borrower will fail to meet debt obligations, an objective critical for…
Portfolio OptimizationAn Actor-Critic Framework for Continuous-Time Jump-Diffusion Controls with Normalizing Flows
Continuous-time stochastic control with time-inhomogeneous jump-diffusion dynamics is central in finance and economics, but computing optimal policies is difficult under explicit time dependence, discontinuous shocks, an…
Portfolio OptimizationPortfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training
This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct a teacher student learning pipeline in which a Conditional Value at…
Portfolio OptimizationScalable Mean-Variance Portfolio Optimization via Subspace Embeddings and GPU-Friendly Nesterov-Accelerated Projected Gradient
We develop a sketch-based factor reduction and a Nesterov-accelerated projected gradient algorithm (NPGA) with GPU acceleration, yielding a doubly accelerated solver for large-scale constrained mean-variance portfolio op…
Portfolio Optimization