Portfolio Optimization
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A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
Deep Learning for Portfolio Optimization
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
Qlib: An AI-oriented Quantitative Investment Platform
Bayesian Optimization of Risk Measures
Papers
Learning 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 Optimization