arXiv Open Access 2023

Deep Reinforcement Learning for Asset Allocation: Reward Clipping

Jiwon Kim Moon-Ju Kang KangHun Lee HyungJun Moon Bo-Kwan Jeon

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Abstrak

Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits. In this paper, we compare performance between several reinforcement learning algorithms - actor-only, actor-critic and PPO models. Furthermore, we analyze each models' character and then introduce the advanced algorithm, so called Reward clipping model. It seems that the Reward Clipping model is better than other existing models in finance domain, especially portfolio optimization - it has strength both in bull and bear markets. Finally, we compare the performance for these models with traditional investment strategies during decreasing and increasing markets.

Topik & Kata Kunci

q-fin.CP cs.AI cs.LG q-fin.PM

Penulis (5)

Jiwon Kim

Moon-Ju Kang

KangHun Lee

HyungJun Moon

Bo-Kwan Jeon

Format Sitasi

APA MLA BibTeX

Kim, J., Kang, M., Lee, K., Moon, H., Jeon, B. (2023). Deep Reinforcement Learning for Asset Allocation: Reward Clipping. https://arxiv.org/abs/2301.05300

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓