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DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding

AAAI 2021 · Reinforcement learning › Policy gradient and actor-critic · approach: Regularized scalable deep value learning · 139 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the market scoring unit to dynamically adjust long/short allocation (ablation)DeepTrader · DJIA and HSIASR: 0.638 -> 0.718 (DJIA); 0.930 -> 1.044 (HSI)Helped
Add the market scoring unit to dynamically adjust long/short allocation (ablation)DeepTrader · DJIA and HSIMDD (%): 31.24 -> 22.61 (DJIA); 20.17 -> 17.11 (HSI)Helped
Add spatial attention and graph convolution for cross-stock relations (ablation)DeepTrader · DJIA and HSIASR: 0.628 -> 0.718 (DJIA); 0.902 -> 1.044 (HSI)Helped
Add spatial attention and graph convolution for cross-stock relations (ablation)DeepTrader · DJIA and HSIMDD (%): 26.33 -> 22.61 (DJIA); 19.93 -> 17.11 (HSI)Helped
Use negative maximum drawdown instead of rate of return as the market-unit reward (ablation)DeepTrader · DJIAMDD (%): 45.52 -> 22.61Helped
Use negative maximum drawdown instead of Sharpe ratio as the market-unit reward (ablation)DeepTrader · DJIAMDD (%): 46.06 -> 22.61Helped
Use negative maximum drawdown instead of Calmar ratio as the market-unit reward (ablation)DeepTrader · DJIAMDD (%): 31.10 -> 22.61Helped
Use rate of return instead of maximum drawdown as the market-unit reward (ablation)DeepTrader · DJIAARR (%): 12.35 -> 15.60Helped
Use Sharpe ratio instead of maximum drawdown as the market-unit reward (ablation)DeepTrader · DJIAARR (%): 12.35 -> 14.36Helped
Use Sharpe ratio as the market-unit reward (ablation)DeepTrader · DJIAASR: 0.718 -> 0.700Hurt
Use a learned causal graph rather than industry classification (ablation)DeepTrader graph convolution · DJIAASR: 0.718 -> 0.750Helped
Use a learned causal graph rather than industry classification (ablation)DeepTrader graph convolution · HSIMDD (%): 17.11 -> 15.90Helped

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