TY - JOUR
T1 - A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning
AU - Almahdi, Saud
AU - Yang, Steve Y.
N1 - Publisher Copyright:
© 2019
PY - 2019/9/15
Y1 - 2019/9/15
N2 - This study extends a recurrent reinforcement portfolio allocation and rebalancing management system with complex portfolio constraints using particle swarm algorithms. In particular, we propose to use a combination of recurrent reinforcement learning (RRL) and particle swarm algorithm (PSO) with Calmar ratio for both asset allocation and constraint optimization. Using S&P100 index stocks, we show such a system with a Calmar ratio based objective function yields a better efficient frontier than the Sharpe ratio and mean-variance based portfolios. By comparing with multiple PSO based long only constrained portfolios, we propose an optimal portfolio trading system that is capable of generating both long and short signals and handling the common portfolio constraints. We further develop an adaptive RRL-PSO portfolio rebalancing decision system with a market condition stop-loss retraining mechanism, and we show that the proposed portfolio trading system outperforms the benchmarks consistently especially under high transaction cost conditions.
AB - This study extends a recurrent reinforcement portfolio allocation and rebalancing management system with complex portfolio constraints using particle swarm algorithms. In particular, we propose to use a combination of recurrent reinforcement learning (RRL) and particle swarm algorithm (PSO) with Calmar ratio for both asset allocation and constraint optimization. Using S&P100 index stocks, we show such a system with a Calmar ratio based objective function yields a better efficient frontier than the Sharpe ratio and mean-variance based portfolios. By comparing with multiple PSO based long only constrained portfolios, we propose an optimal portfolio trading system that is capable of generating both long and short signals and handling the common portfolio constraints. We further develop an adaptive RRL-PSO portfolio rebalancing decision system with a market condition stop-loss retraining mechanism, and we show that the proposed portfolio trading system outperforms the benchmarks consistently especially under high transaction cost conditions.
KW - Optimal portfolio rebalancing
KW - Particle swarm optimization
KW - Portfolio constraint
KW - Recurrent reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85064433484&partnerID=8YFLogxK
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U2 - 10.1016/j.eswa.2019.04.013
DO - 10.1016/j.eswa.2019.04.013
M3 - Article
AN - SCOPUS:85064433484
SN - 0957-4174
VL - 130
SP - 145
EP - 156
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -