You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding
							| Proceedings of the AAAI Conference on Artificial Intelligence



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Vol. 35 No. 1: AAAI-21 Technical Tracks 1

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AAAI Technical Track on Application Domains

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

Authors

Zhicheng Wang

Shanghai JiaoTong University

Biwei Huang

Carnegie Mellon University

Shikui Tu

Shanghai Jiao Tong University

Kun Zhang

Carnegie Mellon University

Lei Xu

Shanghai Jiao Tong University

DOI:

https://doi.org/10.1609/aaai.v35i1.16144

Keywords:

Economic/Financial

Abstract

Most existing reinforcement learning (RL)-based portfolio management models do not take into account the market conditions, which limits their performance in risk-return balancing. In this paper, we propose DeepTrader, a deep RL method to optimize the investment policy. In particular, to tackle the risk-return balancing problem, our model embeds macro market conditions as an indicator to dynamically adjust the proportion between long and short funds, to lower the risk of market fluctuations, with the negative maximum drawdown as the reward function. Additionally, the model involves a unit to evaluate individual assets, which learns dynamic patterns from historical data with the price rising rate as the reward function. Both temporal and spatial dependencies between assets are captured hierarchically by a specific type of graph structure. Particularly, we find that the estimated causal structure best captures the interrelationships between assets, compared to industry classification and correlation. The two units are complementary and integrated to generate a suitable portfolio which fits the market trend well and strikes a balance between return and risk effectively. Experiments on three well-known stock indexes demonstrate the superiority of DeepTrader in terms of risk-gain criteria.

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Published

2021-05-18

How to Cite

Wang, Z., Huang, B., Tu, S., Zhang, K., & Xu, L. (2021). DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding.
Proceedings of the AAAI Conference on Artificial Intelligence
,
35
(1), 643–650. https://doi.org/10.1609/aaai.v35i1.16144

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Vol. 35 No. 1: AAAI-21 Technical Tracks 1

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</reference>

<statements>
1. reward = log portfolio return, differential Sharpe, or return net of transaction costs and a risk penalty (e.g., extended Markowitz mean-variance reward as in the RTC-CNN-TD3 model, negative max-drawdown as in DeepTrader or CVaR-aware rewards).
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.