HADAPS: Hierarchical Adaptive Multi-Asset Portfolio Selection

Jinkyu Kim, Donghee Choi, Mogan Gim, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review


Multi-asset portfolio selection is an asset allocation strategy involving a variety of assets. Adaptive investment strategies which consider the dynamic market characteristics of individual assets and asset classes are vital for maximizing returns and minimizing risks. We introduce HADAPS, a novel computational method for multi-asset portfolio selection which utilizes the Soft-Actor-Critic (SAC) framework enhanced with Hierarchical Policy Network. Contrary to previous approaches that have relied on heuristics for constructing asset allocations, HADAPSdirectly outputs a continuous vector of action values depending on current market conditions. In addition, HADAPS performs multi-asset portfolio selection involving multiple asset classes. Experimental results show that HADAPS outperforms baseline approaches in not only cumulative returns but also risk-adjusted metrics. These results are based on market price data from sectors with various behavioral characteristics. Furthermore, qualitative analysis shows HADAPS ' ability to adaptively shift portfolio selection strategies in dynamic market conditions where asset classes and different assets are uncorrelated to each other.

Original languageEnglish
Pages (from-to)73394-73402
Number of pages9
JournalIEEE Access
Publication statusPublished - 2023

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea under Grant NRF-2023R1A2C3004176; and in part by the Ministry of Science and ICT (MSIT), South Korea, under the ICT Creative Consilience Program, supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP), under Grant IITP-2023-2020-0-01819.

Publisher Copyright:
© 2013 IEEE.


  • Portfolios
  • cryptocurrency
  • investment
  • reinforcement learning
  • stock markets

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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