Deep Reinforcement Learning for Long-Short Portfolio Optimization
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2020
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| _version_ | 1866912275151978496 |
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| author | Huang, Gang Zhou, Xiaohua Song, Qingyang |
| author_facet | Huang, Gang Zhou, Xiaohua Song, Qingyang |
| contents | With the rapid development of artificial intelligence, data-driven methods effectively overcome limitations in traditional portfolio optimization. Conventional models primarily employ long-only mechanisms, excluding highly correlated assets to diversify risk. However, incorporating short-selling enables low-risk arbitrage through hedging correlated assets. This paper constructs a Deep Reinforcement Learning (DRL) portfolio management framework with short-selling mechanisms conforming to actual trading rules, exploring strategies for excess returns in China's A-share market. Key innovations include: (1) Development of a comprehensive short-selling mechanism in continuous trading that accounts for dynamic evolution of transactions across time periods; (2) Design of a long-short optimization framework integrating deep neural networks for processing multi-dimensional financial time series with mean Sharpe ratio reward functions. Empirical results show the DRL model with short-selling demonstrates significant optimization capabilities, achieving consistent positive returns during backtesting periods. Compared to traditional approaches, this model delivers superior risk-adjusted returns while reducing maximum drawdown. From an allocation perspective, the DRL model establishes a robust investment style, enhancing defensive capabilities through strategic avoidance of underperforming assets and balanced capital allocation. This research contributes to portfolio theory while providing novel methodologies for quantitative investment practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_13773 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Long-Short Portfolio Optimization Huang, Gang Zhou, Xiaohua Song, Qingyang Computational Finance Machine Learning Portfolio Management With the rapid development of artificial intelligence, data-driven methods effectively overcome limitations in traditional portfolio optimization. Conventional models primarily employ long-only mechanisms, excluding highly correlated assets to diversify risk. However, incorporating short-selling enables low-risk arbitrage through hedging correlated assets. This paper constructs a Deep Reinforcement Learning (DRL) portfolio management framework with short-selling mechanisms conforming to actual trading rules, exploring strategies for excess returns in China's A-share market. Key innovations include: (1) Development of a comprehensive short-selling mechanism in continuous trading that accounts for dynamic evolution of transactions across time periods; (2) Design of a long-short optimization framework integrating deep neural networks for processing multi-dimensional financial time series with mean Sharpe ratio reward functions. Empirical results show the DRL model with short-selling demonstrates significant optimization capabilities, achieving consistent positive returns during backtesting periods. Compared to traditional approaches, this model delivers superior risk-adjusted returns while reducing maximum drawdown. From an allocation perspective, the DRL model establishes a robust investment style, enhancing defensive capabilities through strategic avoidance of underperforming assets and balanced capital allocation. This research contributes to portfolio theory while providing novel methodologies for quantitative investment practice. |
| title | Deep Reinforcement Learning for Long-Short Portfolio Optimization |
| topic | Computational Finance Machine Learning Portfolio Management |
| url | https://arxiv.org/abs/2012.13773 |