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 - AAAI



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Home
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Proceedings
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Proceedings of the AAAI Conference on Artificial Intelligence, 35
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No. 1: AAAI-21 Technical Tracks 1
DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding

February 1, 2023

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
Proceedings:
No. 1: AAAI-21 Technical Tracks 1
Volume
Issue:
Proceedings of the AAAI Conference on Artificial Intelligence, 35
Track:
AAAI Technical Track on Application Domains
Downloads:
Download PDF
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.
DOI:
10.1609/aaai.v35i1.16144
AAAI
Proceedings of the AAAI Conference on Artificial Intelligence, 35

Topics:
AAAI
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</reference>

<statements>
1. DeepTrader (Wang et al., AAAI 2021): macro market-condition embedding dynamically adjusts long/short balance with negative max-drawdown reward, using a causal-graph GCN for cross-asset dependencies.
</statements>

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