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>
[2402.16609] Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization

Abstract page for arXiv paper 2402.16609: Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization

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Quantitative Finance > Portfolio Management

arXiv:2402.16609
(q-fin)

[Submitted on 23 Feb 2024]

Title:
Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization

Authors:
Ruoyu Sun
(1),
Angelos Stefanidis
(2),
Zhengyong Jiang
(2),
Jionglong Su
(2) ((1) Xi'an Jiaotong-Liverpool University, School of Mathematics and Physics, Department of Financial and Actuarial Mathematics (2) Xi'an Jiaotong-Liverpool University Entrepreneur College (Taicang), School of AI and Advanced Computing (1))

View a PDF of the paper titled Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization, by Ruoyu Sun (1) and 6 other authors

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Abstract:
As a model-free algorithm, deep reinforcement learning (DRL) agent learns and makes decisions by interacting with the environment in an unsupervised way. In recent years, DRL algorithms have been widely applied by scholars for portfolio optimization in consecutive trading periods, since the DRL agent can dynamically adapt to market changes and does not rely on the specification of the joint dynamics across the assets. However, typical DRL agents for portfolio optimization cannot learn a policy that is aware of the dynamic correlation between portfolio asset returns. Since the dynamic correlations among portfolio assets are crucial in optimizing the portfolio, the lack of such knowledge makes it difficult for the DRL agent to maximize the return per unit of risk, especially when the target market permits short selling (i.e., the US stock market). In this research, we propose a hybrid portfolio optimization model combining the DRL agent and the Black-Litterman (BL) model to enable the DRL agent to learn the dynamic correlation between the portfolio asset returns and implement an efficacious long/short strategy based on the correlation. Essentially, the DRL agent is trained to learn the policy to apply the BL model to determine the target portfolio weights. To test our DRL agent, we construct the portfolio based on all the Dow Jones Industrial Average constitute stocks. Empirical results of the experiments conducted on real-world United States stock market data demonstrate that our DRL agent significantly outperforms various comparison portfolio choice strategies and alternative DRL frameworks by at least 42% in terms of accumulated return. In terms of the return per unit of risk, our DRL agent significantly outperforms various comparative portfolio choice strategies and alternative strategies based on other machine learning frameworks.

Comments:

46 pages, 15 figures

Subjects:

Portfolio Management (q-fin.PM)
; Machine Learning (cs.LG)

Cite as:

arXiv:2402.16609
[q-fin.PM]

(or

arXiv:2402.16609v1
[q-fin.PM]
for this version)

https://doi.org/10.48550/arXiv.2402.16609

Focus to learn more

arXiv-issued DOI via DataCite

Submission history
From: Ruoyu Sun [
view email
]

[v1]

Fri, 23 Feb 2024 16:01:37 UTC (2,749 KB)

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View a PDF of the paper titled Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization, by Ruoyu Sun (1) and 6 other authors
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</reference>

<statements>
1. Sun, Stefanidis, Jiang & Su (2024) Combining Transformer-based DRL with BL: their BDA agent learns to apply BL, using the transformer to capture dynamic cross-asset correlation and drive a long/short strategy on the 29 Dow Jones constituents. It reports the highest accumulated return in all four rolling back-test windows, outperforming comparison strategies by at least 42% in terms of accumulated return.
</statements>

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