A Modularized and Scalable Multi-Agent Reinforcement Learning-based
System for Financial Portfolio Management
- URL: http://arxiv.org/abs/2102.03502v2
- Date: Tue, 9 Feb 2021 16:19:01 GMT
- Title: A Modularized and Scalable Multi-Agent Reinforcement Learning-based
System for Financial Portfolio Management
- Authors: Zhenhan Huang, Fumihide Tanaka
- Abstract summary: Financial Portfolio Management is one of the most applicable problems in Reinforcement Learning (RL)
MSPM is a novel Multi-agent Reinforcement learning-based system with a modularized and scalable architecture for portfolio management.
Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
- Score: 7.6146285961466
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Financial Portfolio Management is one of the most applicable problems in
Reinforcement Learning (RL) by its sequential decision-making nature. Existing
RL-based approaches, while inspiring, often lack scalability, reusability, or
profundity of intake information to accommodate the ever-changing capital
markets. In this paper, we design and develop MSPM, a novel Multi-agent
Reinforcement learning-based system with a modularized and scalable
architecture for portfolio management. MSPM involves two asynchronously updated
units: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A
self-sustained EAM produces signal-comprised information for a specific asset
using heterogeneous data inputs, and each EAM possesses its reusability to have
connections to multiple SAMs. A SAM is responsible for the assets reallocation
of a portfolio using profound information from the EAMs connected. With the
elaborate architecture and the multi-step condensation of the volatile market
information, MSPM aims to provide a customizable, stable, and dedicated
solution to portfolio management that existing approaches do not. We also
tackle data-shortage issue of newly-listed stocks by transfer learning, and
validate the necessity of EAM. Experiments on 8-year U.S. stock markets data
prove the effectiveness of MSPM in profits accumulation by its outperformance
over existing benchmarks.
Related papers
- InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains [0.0]
This study introduces a novel approach using large language models (LLMs) to manage multi-agent inventory systems.
Our model, InvAgent, enhances resilience and improves efficiency across the supply chain network.
arXiv Detail & Related papers (2024-07-16T04:55:17Z) - The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective [53.48484062444108]
We find that the development of models and data is not two separate paths but rather interconnected.
On the one hand, vaster and higher-quality data contribute to better performance of MLLMs, on the other hand, MLLMs can facilitate the development of data.
To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective.
arXiv Detail & Related papers (2024-07-11T15:08:11Z) - MMRel: A Relation Understanding Dataset and Benchmark in the MLLM Era [72.95901753186227]
Multi-Modal Relation Understanding (MMRel) is a comprehensive dataset for studying inter-object relations with Multi-modal Large Language Models (MLLMs)
MMRel features three distinctive attributes: (i) It includes over 15K question-answer pairs, which are sourced from three distinct domains, ensuring large scale and high diversity; (ii) It contains a subset featuring highly unusual relations, on which MLLMs often fail due to hallucinations, thus are very challenging; (iii) It provides manually verified high-quality labels for inter-object relations.
arXiv Detail & Related papers (2024-06-13T13:51:59Z) - Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts [54.529880848937104]
We develop a unified MLLM with the MoE architecture, named Uni-MoE, that can handle a wide array of modalities.
Specifically, it features modality-specific encoders with connectors for a unified multimodal representation.
We evaluate the instruction-tuned Uni-MoE on a comprehensive set of multimodal datasets.
arXiv Detail & Related papers (2024-05-18T12:16:01Z) - Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation [0.0]
We propose a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series, namely the MASAAT.
By reconstructing the tokens of financial data in a sequence, the attention-based cross-sectional analysis module and temporal analysis module of each agent can effectively capture the correlations between assets and the dependencies between time points.
The experimental results clearly demonstrate that the MASAAT framework achieves impressive enhancement when compared with many well-known portfolio optimsation approaches.
arXiv Detail & Related papers (2024-04-13T09:10:05Z) - Model Composition for Multimodal Large Language Models [73.70317850267149]
We propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model.
Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters.
arXiv Detail & Related papers (2024-02-20T06:38:10Z) - Developing A Multi-Agent and Self-Adaptive Framework with Deep
Reinforcement Learning for Dynamic Portfolio Risk Management [1.3505077405741583]
A multi-agent reinforcement learning (RL) approach is proposed to balance the trade-off between the overall portfolio returns and their potential risks.
The obtained empirical results clearly reveal the potential strengths of our proposed MASA framework.
arXiv Detail & Related papers (2024-02-01T11:31:26Z) - FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and
Character Design [11.913409501633616]
textscFinMem is a novel LLM-based agent framework devised for financial decision-making.
textscFinMem's memory module aligns closely with the cognitive structure of human traders, offering robust interpretability.
This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions.
arXiv Detail & Related papers (2023-11-23T00:24:40Z) - AgentBench: Evaluating LLMs as Agents [88.45506148281379]
Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks.
We present AgentBench, a benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
arXiv Detail & Related papers (2023-08-07T16:08:11Z) - MAPS: Multi-agent Reinforcement Learning-based Portfolio Management
System [23.657021288146158]
We propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS)
MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio.
Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio.
arXiv Detail & Related papers (2020-07-10T14:08:12Z) - Reinforcement-Learning based Portfolio Management with Augmented Asset
Movement Prediction States [71.54651874063865]
Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks.
In this paper, we propose SARL, a novel State-Augmented RL framework for PM.
Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
arXiv Detail & Related papers (2020-02-09T08:10:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.