Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years. The sensitive nature of training Reinforcement Learning agents implies a need for extensive efforts in market representation, behavior objectives, and training processes, which have often been lacking in previous works. We propose a training and evaluation process to assess the performance of classical DRL algorithms for portfolio management. We found that most Deep Reinforcement Learning algorithms were not robust, with strategies generalizing poorly and degrading quickly during backtesting.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingDeep Reinforcement LearningManagementreinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
Cryptocurrency Portfolio Management with Reinforcement Learning: Soft Actor--Critic and Deep Deterministic Policy Gradient Algorithms
This paper proposes a reinforcement learning--based framework for cryptocurrency portfolio management using the Soft Actor--Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms. Traditional portfolio opt…
Reinforcement LearningPortfolio OptimizationReinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information
We develop a portfolio allocation framework that leverages deep learning techniques to address challenges arising from high-dimensional, non-stationary, and low-signal-to-noise market information. Our approach includes a…
Meta-Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)LLM-based Personalized Portfolio Recommender: Integrating Large Language Models and Reinforcement Learning for Intelligent Investment Strategy Optimization
In modern financial markets, investors increasingly seek personalized and adaptive portfolio strategies that reflect their individual risk preferences and respond to dynamic market conditions. Traditional rule-based or s…
Reinforcement LearningMultimodal Deep Reinforcement Learning for Portfolio Optimization
We propose a reinforcement learning (RL) framework that leverages multimodal data including historical stock prices, sentiment analysis, and topic embeddings from news articles, to optimize trading strategies for SP100 s…
ArticlesBenchmarkingDeep Reinforcement LearningPortfolio Optimization+4A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market
This paper presents a comparative analysis of the performances of three portfolio optimization approaches. Three approaches of portfolio optimization that are considered in this work are the mean-variance portfolio (MVP)…
Portfolio OptimizationQ-Learningreinforcement-learningReinforcement Learning