According to documents, there has not been a completely artificial intelligence framework with shorting mechanism in continuous action space for portfolio optimization. The objective of this paper is to verify that current cutting-edge technology, deep reinforcement learning, can be applied to portfolio management , and help us get artificial intelligence. We improve on the existing Deep Reinforcement Learning Portfolio model and make many innovations . Unlike many previous studies on discrete trading signals, we make the agent to short in a continuous action space for portfolio optimization. In addition, we design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors. Furthermore, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data. In experiments, we use our model in several randomly selected portfolios which include CSI300 that represents the market's rate of return and the randomly selected constituents of CSI500. The experimental results show that no matter what stocks we select in our portfolios, we can always get a higher return than the market itself, namely we can get artificial intelligence through deep reinforcement learning to defeat market .
According to documents, there has not been a completely artificial intelligence framework with shorting mechanism in continuous action space for portfolio optimization. The objective of this paper is to verify that current cutting-edge technology, deep reinforcement learning, can be applied in portfolio management and help us get artificial intelligence. We improve on the existing Deep Reinforcement Learning Portfolio model and make many innovations , such as adding short mechanism, designing an arbitrage mechanism . In experiments, we use our model in several randomly selected portfolios , the results show we can get artificial intelligence and defeat market through deep reinforcement learning .
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to portfolio management ,
in portfolio management
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. Unlike many previous studies on discrete trading signals, we make the agent to short in a continuous action space for portfolio optimization. In addition, we design
, such as adding short mechanism, designing
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based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors. Furthermore, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data.
.
R
which include CSI300 that represents the market's rate of return and the randomly selected constituents of CSI500. The experimental results show that no matter what stocks we select in our portfolios, we can always get a higher return than the market itself, namely we can get artificial intelligence
, the results show we can get artificial intelligence and defeat market
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to defeat market