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Lower Risks, Better Choices: Stock Correlation Based Portfolio Selection in Stock Markets

Published: 30 April 2023 Publication History

Abstract

Over the past few years, we’ve seen a huge interest in applying AI techniques to develop investment strategies both in academia and the finance industry. However, we note that generating returns is not always the sole investment objective. Take large pension funds for example, they are considerably more risk-averse as opposed to profit-seeking. With this observation, we propose a Risk-balanced Deep Portfolio Constructor (RDPC) that takes risk into explicit consideration. RDPC is an end-to-end reinforcement learning-based transformer trained to optimize both returns and risk, with a hard attention mechanism that learns the relationship between asset pairs, imitating the powerful pairs trading strategy widely adopted by many investors. Experiments on real-world data show that RDPC achieves state-of-the-art performance not just on risk metrics such as maximum drawdown, but also on risk-adjusted returns metrics including Sharpe ratio and Calmar ratio.

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      cover image ACM Conferences
      WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
      April 2023
      1567 pages
      ISBN:9781450394192
      DOI:10.1145/3543873
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 30 April 2023

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      Author Tags

      1. Deep Learning
      2. Portfolio Selection
      3. Reinforcement Learning

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      WWW '23: The ACM Web Conference 2023
      April 30 - May 4, 2023
      TX, Austin, USA

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