Intelligent Portfolio Optimisation Based on Dynamic Trading and Risk Constraints

WANG Wu-yu, ZHANG Ning, FAN Dan, WANG Xi

Journal of Central University of Finance & Economics ›› 2021, Vol. 0 ›› Issue (9) : 32-47.

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Journal of Central University of Finance & Economics ›› 2021, Vol. 0 ›› Issue (9) : 32-47.

Intelligent Portfolio Optimisation Based on Dynamic Trading and Risk Constraints

  • WANG Wu-yu, ZHANG Ning, FAN Dan, WANG Xi
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Abstract

With the continuous growth of consumption and investment demand,much attention is paid to portfolio management.The development of financial big data puts forward higher requirements for the research in this field.This paper proposes a portfolio optimisation method based on dynamic trading using reinforcement learning.This method considers the impact of risk on the process of portfolio management,and is able to transform the portfolio optimisation model automatically according to the market state and asset information,so as to cope with the change of different market.Moreover,through the dynamic trading between the portfolio assets and external market assets,these models can adjust the composition of portfolio assets and asset allocation in real time.Through the empirical analysis of Chinese stock market data,the feasibility and effectiveness of the intelligent portfolio optimisation method are testified.It is found that it is necessary to change the asset composition of the portfolio and consider the risk constraints according to the market changes and dynamic trading patterns.And introducing more information has a positive effect on the portfolio optimisation.Additionally,for portfolio optimisation,considering downside risk constraints is more beneficial to maximize returns under given investment risks than considering the overall risks.

Key words

Portfolio optimisation / Dynamic trading / Risk constraints / Reinforcement learning

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WANG Wu-yu, ZHANG Ning, FAN Dan, WANG Xi. Intelligent Portfolio Optimisation Based on Dynamic Trading and Risk Constraints[J]. Journal of Central University of Finance & Economics, 2021, 0(9): 32-47

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