Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156941
Title: Learning optimal portfolios with intrinsic rewards
Authors: Guan, Zihang
Keywords: Science::Mathematics::Statistics
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Guan, Z. (2022). Learning optimal portfolios with intrinsic rewards. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156941
Project: MATH/21/040
Abstract: A profitable stock trading strategy is crucial for financial institutions. However, it is difficult to find a successful trading strategy in the complex and dynamic financial market. A wise choice of an appropriate risk measure in trading problems is crucial to evaluate the investment performance as well as to guide the RL trading agent to profit. In this dissertation, we are motivated to study the efficacy of learning optimal portfolios with intrinsic rewards. The main contributions of this dissertation include formally deriving the algorithm to incorporate the optimal intrinsic reward on Advantage Actor-Critic (A2C) RL algorithm and first applying the A2C algorithm with optimal intrinsic reward in finance environment.
URI: https://hdl.handle.net/10356/156941
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

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