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 approachesdo 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.
Financial portfolio management is one of the most applicable problems in reinforcement learning (RL) owing to 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 propose MSPM, a modularized and scalable , multi-agent RL-based system for financial portfolio management. MSPM involves two asynchronously updated units: an 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 employs its reusability to have connections to multiple SAMs. An SAM is responsible for asset reallocation in a portfolio using profound information from the connected EAMs . With the elaborate architecture and the multi-step condensation of volatile market information, MSPM aims to provide a customizable, stable, and dedicated solution to portfolio management , unlike existing approaches . We also tackle the data-shortage issue of newly-listed stocks by transfer learning, and validate the indispensability of EAM with four different portfolios . Experiments on 8-year U.S. stock market data prove the effectiveness of MSPM in profit accumulation, by its outperformance over existing benchmarks.
R
Portfolio Management
portfolio management
R
Reinforcement Learning
reinforcement learning
R
by
owing to
R
design and develop
propose
D
novel Multi-agent Reinforcement learning-based system with a
R
architecture for
, multi-agent RL-based system for financial
A
an
R
possesses
employs
R
A
An
R
the assets reallocation of
asset reallocation in
R
EAMs connected
connected EAMs
D
the
R
that existing approachesdo not
, unlike existing approaches
A
the
R
necessity of EAM
indispensability of EAM with four different portfolios
R
markets
market
R
profits accumulation
profit accumulation,