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Found 7 papers, 0 papers with code
Regime-Aware Asset Allocation: a Statistical Jump Model Approach
This article investigates the impact of regime switching on asset allocation decisions, with a primary focus on comparing different regime identification models.
Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network
Here, we propose a four-step numerical framework for the optimal portfolio execution problem where multiple market regimes exist, with the underlying regime switching based on a Markov process.
Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks
This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network.
End-to-End Risk Budgeting Portfolio Optimization with Neural Networks
Portfolio optimization has been a central problem in finance, often approached with two steps: calibrating the parameters and then solving an optimization problem.
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies
We propose a novel reinforcement learning based framework PoBRL for solving multi-document summarization.
Multi-Period Portfolio Optimization using Model Predictive Control with Mean-Variance and Risk Parity Frameworks
We employ model predictive control for a multi-period portfolio optimization problem.
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning
We consider a setting that lies between pure offline reinforcement learning (RL) and pure online RL called deployment constrained RL in which the number of policy deployments for data sampling is limited.
