Uncertainty-Aware Portfolio Management With Risk-Sensitive Multiagent Network

Kidon Park, Hong Gyu Jung, Tae San Eom, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


As deep neural networks (DNNs) have gained considerable attention in recent years, there have been several cases applying DNNs to portfolio management (PM). Although some researchers have experimentally demonstrated its ability to make a profit, it is still insufficient to use in real situations because existing studies have failed to answer how risky investment decisions are. Furthermore, even though the objective of PM is to maximize returns within a risk tolerance, they overlook the predictive uncertainty of DNNs in the process of risk management. To overcome these limitations, we propose a novel framework called risk-sensitive multiagent network (RSMAN), which includes risk-sensitive agents (RSAs) and a risk adaptive portfolio generator (RAPG). Standard DNNs do not understand the risks of their decision, whereas RSA can take risk-sensitive decisions by estimating market uncertainty and parameter uncertainty. Acting as a trader, this agent is trained via reinforcement learning from dynamic trading simulations to estimate the distribution of reward and via unsupervised learning to assess parameter uncertainty without labeled data. We also present an RAPG that can generate a portfolio fitting the user's risk appetite without retraining by exploiting the estimated information from the RSAs. We tested our framework on the U.S. and Korean real financial markets to demonstrate the practicality of the RSMAN.

Original languageEnglish
Article number3174642
Pages (from-to)362-375
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number1
Publication statusPublished - 2024 Jan 1

Bibliographical note

Publisher Copyright:
© 2012 IEEE.


  • Deep learning
  • financial trading
  • portfolio management (PM)
  • reinforcement learning (RL)
  • uncertainty quantification

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


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